research in teacher professional development

• Search Menu
• Sign in through your institution
• Browse content in A - General Economics and Teaching
• Browse content in A1 - General Economics
• A12 - Relation of Economics to Other Disciplines
• A14 - Sociology of Economics
• Browse content in B - History of Economic Thought, Methodology, and Heterodox Approaches
• Browse content in B4 - Economic Methodology
• B41 - Economic Methodology
• Browse content in C - Mathematical and Quantitative Methods
• Browse content in C1 - Econometric and Statistical Methods and Methodology: General
• C18 - Methodological Issues: General
• Browse content in C2 - Single Equation Models; Single Variables
• C21 - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
• Browse content in C3 - Multiple or Simultaneous Equation Models; Multiple Variables
• C38 - Classification Methods; Cluster Analysis; Principal Components; Factor Models
• Browse content in C5 - Econometric Modeling
• C59 - Other
• Browse content in C8 - Data Collection and Data Estimation Methodology; Computer Programs
• C80 - General
• C81 - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
• C83 - Survey Methods; Sampling Methods
• Browse content in C9 - Design of Experiments
• C93 - Field Experiments
• Browse content in D - Microeconomics
• Browse content in D0 - General
• D02 - Institutions: Design, Formation, Operations, and Impact
• D03 - Behavioral Microeconomics: Underlying Principles
• D04 - Microeconomic Policy: Formulation; Implementation, and Evaluation
• Browse content in D1 - Household Behavior and Family Economics
• D10 - General
• D12 - Consumer Economics: Empirical Analysis
• D14 - Household Saving; Personal Finance
• Browse content in D2 - Production and Organizations
• D22 - Firm Behavior: Empirical Analysis
• D24 - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
• Browse content in D3 - Distribution
• D31 - Personal Income, Wealth, and Their Distributions
• Browse content in D6 - Welfare Economics
• D61 - Allocative Efficiency; Cost-Benefit Analysis
• D62 - Externalities
• D63 - Equity, Justice, Inequality, and Other Normative Criteria and Measurement
• Browse content in D7 - Analysis of Collective Decision-Making
• D72 - Political Processes: Rent-seeking, Lobbying, Elections, Legislatures, and Voting Behavior
• D73 - Bureaucracy; Administrative Processes in Public Organizations; Corruption
• D74 - Conflict; Conflict Resolution; Alliances; Revolutions
• Browse content in D8 - Information, Knowledge, and Uncertainty
• D83 - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
• D85 - Network Formation and Analysis: Theory
• D86 - Economics of Contract: Theory
• Browse content in D9 - Micro-Based Behavioral Economics
• D91 - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
• D92 - Intertemporal Firm Choice, Investment, Capacity, and Financing
• Browse content in E - Macroeconomics and Monetary Economics
• Browse content in E2 - Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy
• E23 - Production
• E24 - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
• Browse content in E4 - Money and Interest Rates
• E42 - Monetary Systems; Standards; Regimes; Government and the Monetary System; Payment Systems
• Browse content in E5 - Monetary Policy, Central Banking, and the Supply of Money and Credit
• E52 - Monetary Policy
• E58 - Central Banks and Their Policies
• Browse content in E6 - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook
• E60 - General
• E61 - Policy Objectives; Policy Designs and Consistency; Policy Coordination
• E62 - Fiscal Policy
• E65 - Studies of Particular Policy Episodes
• Browse content in F - International Economics
• Browse content in F0 - General
• F01 - Global Outlook
• Browse content in F1 - Trade
• F10 - General
• F11 - Neoclassical Models of Trade
• F13 - Trade Policy; International Trade Organizations
• F14 - Empirical Studies of Trade
• F15 - Economic Integration
• F19 - Other
• Browse content in F2 - International Factor Movements and International Business
• F21 - International Investment; Long-Term Capital Movements
• F22 - International Migration
• F23 - Multinational Firms; International Business
• Browse content in F3 - International Finance
• F32 - Current Account Adjustment; Short-Term Capital Movements
• F34 - International Lending and Debt Problems
• F35 - Foreign Aid
• F36 - Financial Aspects of Economic Integration
• Browse content in F4 - Macroeconomic Aspects of International Trade and Finance
• F41 - Open Economy Macroeconomics
• F42 - International Policy Coordination and Transmission
• F43 - Economic Growth of Open Economies
• Browse content in F5 - International Relations, National Security, and International Political Economy
• F50 - General
• F52 - National Security; Economic Nationalism
• F53 - International Agreements and Observance; International Organizations
• F55 - International Institutional Arrangements
• Browse content in F6 - Economic Impacts of Globalization
• F61 - Microeconomic Impacts
• F63 - Economic Development
• F66 - Labor
• Browse content in G - Financial Economics
• Browse content in G0 - General
• G01 - Financial Crises
• Browse content in G1 - General Financial Markets
• G10 - General
• G15 - International Financial Markets
• G18 - Government Policy and Regulation
• Browse content in G2 - Financial Institutions and Services
• G20 - General
• G21 - Banks; Depository Institutions; Micro Finance Institutions; Mortgages
• G22 - Insurance; Insurance Companies; Actuarial Studies
• G23 - Non-bank Financial Institutions; Financial Instruments; Institutional Investors
• G28 - Government Policy and Regulation
• Browse content in G3 - Corporate Finance and Governance
• G32 - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
• G33 - Bankruptcy; Liquidation
• G38 - Government Policy and Regulation
• Browse content in H - Public Economics
• Browse content in H1 - Structure and Scope of Government
• H11 - Structure, Scope, and Performance of Government
• Browse content in H2 - Taxation, Subsidies, and Revenue
• H20 - General
• H23 - Externalities; Redistributive Effects; Environmental Taxes and Subsidies
• H25 - Business Taxes and Subsidies
• H26 - Tax Evasion and Avoidance
• H27 - Other Sources of Revenue
• Browse content in H3 - Fiscal Policies and Behavior of Economic Agents
• H31 - Household
• Browse content in H4 - Publicly Provided Goods
• H41 - Public Goods
• H43 - Project Evaluation; Social Discount Rate
• Browse content in H5 - National Government Expenditures and Related Policies
• H52 - Government Expenditures and Education
• H53 - Government Expenditures and Welfare Programs
• H54 - Infrastructures; Other Public Investment and Capital Stock
• H55 - Social Security and Public Pensions
• H56 - National Security and War
• H57 - Procurement
• Browse content in H6 - National Budget, Deficit, and Debt
• H60 - General
• H61 - Budget; Budget Systems
• Browse content in H7 - State and Local Government; Intergovernmental Relations
• H71 - State and Local Taxation, Subsidies, and Revenue
• H75 - State and Local Government: Health; Education; Welfare; Public Pensions
• H77 - Intergovernmental Relations; Federalism; Secession
• Browse content in H8 - Miscellaneous Issues
• H83 - Public Administration; Public Sector Accounting and Audits
• H84 - Disaster Aid
• Browse content in I - Health, Education, and Welfare
• Browse content in I0 - General
• I00 - General
• Browse content in I1 - Health
• I10 - General
• I12 - Health Behavior
• I15 - Health and Economic Development
• I18 - Government Policy; Regulation; Public Health
• Browse content in I2 - Education and Research Institutions
• I20 - General
• I21 - Analysis of Education
• I22 - Educational Finance; Financial Aid
• I24 - Education and Inequality
• I25 - Education and Economic Development
• I28 - Government Policy
• Browse content in I3 - Welfare, Well-Being, and Poverty
• I30 - General
• I31 - General Welfare
• I32 - Measurement and Analysis of Poverty
• I38 - Government Policy; Provision and Effects of Welfare Programs
• Browse content in J - Labor and Demographic Economics
• Browse content in J0 - General
• J01 - Labor Economics: General
• J08 - Labor Economics Policies
• Browse content in J1 - Demographic Economics
• J10 - General
• J11 - Demographic Trends, Macroeconomic Effects, and Forecasts
• J12 - Marriage; Marital Dissolution; Family Structure; Domestic Abuse
• J13 - Fertility; Family Planning; Child Care; Children; Youth
• J15 - Economics of Minorities, Races, Indigenous Peoples, and Immigrants; Non-labor Discrimination
• J16 - Economics of Gender; Non-labor Discrimination
• J17 - Value of Life; Forgone Income
• J18 - Public Policy
• Browse content in J2 - Demand and Supply of Labor
• J21 - Labor Force and Employment, Size, and Structure
• J22 - Time Allocation and Labor Supply
• J23 - Labor Demand
• J24 - Human Capital; Skills; Occupational Choice; Labor Productivity
• J26 - Retirement; Retirement Policies
• J28 - Safety; Job Satisfaction; Related Public Policy
• Browse content in J3 - Wages, Compensation, and Labor Costs
• J38 - Public Policy
• Browse content in J4 - Particular Labor Markets
• J48 - Public Policy
• Browse content in J5 - Labor-Management Relations, Trade Unions, and Collective Bargaining
• J58 - Public Policy
• Browse content in J6 - Mobility, Unemployment, Vacancies, and Immigrant Workers
• J61 - Geographic Labor Mobility; Immigrant Workers
• J62 - Job, Occupational, and Intergenerational Mobility
• J63 - Turnover; Vacancies; Layoffs
• J68 - Public Policy
• Browse content in J8 - Labor Standards: National and International
• J88 - Public Policy
• Browse content in K - Law and Economics
• Browse content in K2 - Regulation and Business Law
• K23 - Regulated Industries and Administrative Law
• Browse content in K3 - Other Substantive Areas of Law
• K34 - Tax Law
• Browse content in K4 - Legal Procedure, the Legal System, and Illegal Behavior
• K40 - General
• K41 - Litigation Process
• K42 - Illegal Behavior and the Enforcement of Law
• Browse content in L - Industrial Organization
• Browse content in L1 - Market Structure, Firm Strategy, and Market Performance
• L11 - Production, Pricing, and Market Structure; Size Distribution of Firms
• L14 - Transactional Relationships; Contracts and Reputation; Networks
• L16 - Industrial Organization and Macroeconomics: Industrial Structure and Structural Change; Industrial Price Indices
• Browse content in L2 - Firm Objectives, Organization, and Behavior
• L20 - General
• L23 - Organization of Production
• L25 - Firm Performance: Size, Diversification, and Scope
• L26 - Entrepreneurship
• Browse content in L3 - Nonprofit Organizations and Public Enterprise
• L33 - Comparison of Public and Private Enterprises and Nonprofit Institutions; Privatization; Contracting Out
• Browse content in L5 - Regulation and Industrial Policy
• L51 - Economics of Regulation
• L52 - Industrial Policy; Sectoral Planning Methods
• Browse content in L9 - Industry Studies: Transportation and Utilities
• L94 - Electric Utilities
• L97 - Utilities: General
• L98 - Government Policy
• Browse content in M - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics
• Browse content in M5 - Personnel Economics
• M53 - Training
• Browse content in N - Economic History
• Browse content in N1 - Macroeconomics and Monetary Economics; Industrial Structure; Growth; Fluctuations
• N10 - General, International, or Comparative
• Browse content in N3 - Labor and Consumers, Demography, Education, Health, Welfare, Income, Wealth, Religion, and Philanthropy
• N35 - Asia including Middle East
• Browse content in N5 - Agriculture, Natural Resources, Environment, and Extractive Industries
• N55 - Asia including Middle East
• N57 - Africa; Oceania
• Browse content in N7 - Transport, Trade, Energy, Technology, and Other Services
• N77 - Africa; Oceania
• Browse content in O - Economic Development, Innovation, Technological Change, and Growth
• Browse content in O1 - Economic Development
• O10 - General
• O11 - Macroeconomic Analyses of Economic Development
• O12 - Microeconomic Analyses of Economic Development
• O13 - Agriculture; Natural Resources; Energy; Environment; Other Primary Products
• O14 - Industrialization; Manufacturing and Service Industries; Choice of Technology
• O15 - Human Resources; Human Development; Income Distribution; Migration
• O16 - Financial Markets; Saving and Capital Investment; Corporate Finance and Governance
• O17 - Formal and Informal Sectors; Shadow Economy; Institutional Arrangements
• O18 - Urban, Rural, Regional, and Transportation Analysis; Housing; Infrastructure
• O19 - International Linkages to Development; Role of International Organizations
• Browse content in O2 - Development Planning and Policy
• O20 - General
• O22 - Project Analysis
• O23 - Fiscal and Monetary Policy in Development
• O24 - Trade Policy; Factor Movement Policy; Foreign Exchange Policy
• O25 - Industrial Policy
• Browse content in O3 - Innovation; Research and Development; Technological Change; Intellectual Property Rights
• O31 - Innovation and Invention: Processes and Incentives
• O32 - Management of Technological Innovation and R&D
• O33 - Technological Change: Choices and Consequences; Diffusion Processes
• O34 - Intellectual Property and Intellectual Capital
• O38 - Government Policy
• Browse content in O4 - Economic Growth and Aggregate Productivity
• O40 - General
• O41 - One, Two, and Multisector Growth Models
• O43 - Institutions and Growth
• O44 - Environment and Growth
• O47 - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence
• Browse content in O5 - Economywide Country Studies
• O55 - Africa
• O57 - Comparative Studies of Countries
• Browse content in P - Economic Systems
• Browse content in P1 - Capitalist Systems
• P14 - Property Rights
• Browse content in P2 - Socialist Systems and Transitional Economies
• P26 - Political Economy; Property Rights
• Browse content in P3 - Socialist Institutions and Their Transitions
• P30 - General
• Browse content in P4 - Other Economic Systems
• P43 - Public Economics; Financial Economics
• P48 - Political Economy; Legal Institutions; Property Rights; Natural Resources; Energy; Environment; Regional Studies
• Browse content in Q - Agricultural and Natural Resource Economics; Environmental and Ecological Economics
• Browse content in Q0 - General
• Q01 - Sustainable Development
• Browse content in Q1 - Agriculture
• Q10 - General
• Q12 - Micro Analysis of Farm Firms, Farm Households, and Farm Input Markets
• Q13 - Agricultural Markets and Marketing; Cooperatives; Agribusiness
• Q14 - Agricultural Finance
• Q15 - Land Ownership and Tenure; Land Reform; Land Use; Irrigation; Agriculture and Environment
• Q16 - R&D; Agricultural Technology; Biofuels; Agricultural Extension Services
• Q17 - Agriculture in International Trade
• Q18 - Agricultural Policy; Food Policy
• Browse content in Q2 - Renewable Resources and Conservation
• Q25 - Water
• Browse content in Q3 - Nonrenewable Resources and Conservation
• Q30 - General
• Q33 - Resource Booms
• Browse content in Q4 - Energy
• Q41 - Demand and Supply; Prices
• Q43 - Energy and the Macroeconomy
• Browse content in Q5 - Environmental Economics
• Q51 - Valuation of Environmental Effects
• Q52 - Pollution Control Adoption Costs; Distributional Effects; Employment Effects
• Q54 - Climate; Natural Disasters; Global Warming
• Q56 - Environment and Development; Environment and Trade; Sustainability; Environmental Accounts and Accounting; Environmental Equity; Population Growth
• Q57 - Ecological Economics: Ecosystem Services; Biodiversity Conservation; Bioeconomics; Industrial Ecology
• Q58 - Government Policy
• Browse content in R - Urban, Rural, Regional, Real Estate, and Transportation Economics
• Browse content in R1 - General Regional Economics
• R11 - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes
• R12 - Size and Spatial Distributions of Regional Economic Activity
• R13 - General Equilibrium and Welfare Economic Analysis of Regional Economies
• R14 - Land Use Patterns
• Browse content in R2 - Household Analysis
• R20 - General
• R23 - Regional Migration; Regional Labor Markets; Population; Neighborhood Characteristics
• R28 - Government Policy
• Browse content in R3 - Real Estate Markets, Spatial Production Analysis, and Firm Location
• R38 - Government Policy
• Browse content in R4 - Transportation Economics
• R40 - General
• R41 - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise
• R48 - Government Pricing and Policy
• Browse content in R5 - Regional Government Analysis
• R52 - Land Use and Other Regulations
• Browse content in Y - Miscellaneous Categories
• Y8 - Related Disciplines
• Browse content in Z - Other Special Topics
• Browse content in Z1 - Cultural Economics; Economic Sociology; Economic Anthropology
• Z13 - Economic Sociology; Economic Anthropology; Social and Economic Stratification
• Advance articles
• Author Guidelines
• Open Access
• About The World Bank Research Observer
• About the World Bank
• Editorial Board
• Advertising and Corporate Services
• Self-Archiving Policy
• Dispatch Dates
• Journals on Oxford Academic
• Books on Oxford Academic

Article Contents

• < Previous

Teacher Professional Development around the World: The Gap between Evidence and Practice

• Article contents
• Figures & tables
• Supplementary Data

Anna Popova, David K Evans, Mary E Breeding, Violeta Arancibia, Teacher Professional Development around the World: The Gap between Evidence and Practice, The World Bank Research Observer , Volume 37, Issue 1, February 2022, Pages 107–136, https://doi.org/10.1093/wbro/lkab006

• Permissions Icon Permissions

Many teachers in low- and middle-income countries lack the skills to teach effectively, and professional development (PD) programs are the principal tool that governments use to upgrade those skills. At the same time, few PD programs are evaluated, and those that are evaluated show highly varying results. This paper proposes a set of indicators—the In-Service Teacher Training Survey Instrument—to standardize reporting on teacher PD programs. An application of the instrument to 33 rigorously evaluated PD programs shows that programs that link participation to career incentives, have a specific subject focus, incorporate lesson enactment in the training, and include initial face-to-face training tend to show higher student learning gains. In qualitative interviews, program implementers also report follow-up visits as among the most effective characteristics of their professional development programs. This paper then uses the instrument to present novel data on a sample of 139 government-funded, at-scale professional development programs across 14 countries. The attributes of most at-scale teacher professional development programs differ sharply from those of programs that evidence suggests are effective, with fewer incentives to participate in PD, fewer opportunities to practice new skills, and less follow-up once teachers return to their classrooms.

Good teachers have a major impact on student performance, both over the course of the school year ( Araujo et al. 2016 ) and into adulthood ( Chetty, Friedman, and Rockoff 2014 ). However, teachers in low- and middle-income countries often lack the skills they need to teach students effectively. Across seven African countries, only seven percent of fourth-grade teachers had the minimum knowledge necessary to teach language; in four countries, the statistic was zero percent. For math teaching, 68 percent had the minimum knowledge needed to teach math—higher than the seven percent for language, but still leaving one in three teachers with insufficient knowledge. Teachers also scored woefully low in terms of pedagogical knowledge—their ability to prepare a lesson, formulate questions that would elicit student knowledge effectively, and their performance in the classroom ( Bold et al. 2017 ).

The principal tool that countries across the income spectrum use to improve the knowledge and skills of their practicing teachers is professional development (PD), which refers to on-the-job training activities ranging from formal, lecture-style training to mentoring and coaching. However, few PD programs are rigorously evaluated, and among those that are, the evidence of their effectiveness is wildly mixed. Some programs are effective: training teachers to provide literacy instruction using students’ mother tongue in Uganda and training teachers to evaluate student performance more regularly and adjust teaching based on those evaluations in Liberia both had sizeable impacts on student reading ability ( Piper and Korda 2011 ; Kerwin and Thornton 2021 ). Others demonstrate opposite results: a large-scale, government-implemented PD program in China had zero impact on teacher knowledge, teaching practices, or student learning outcomes ( Loyalka et al. 2019 ), and a program that trained teachers to engage their middle school math students more actively in learning in Costa Rica resulted in worse learning outcomes for students ( Berlinski and Busso 2017 ). Indeed, there is much more variation in effectiveness across teacher training programs than across education programs more broadly ( McEwan 2015 ; Evans and Popova 2016a ). With this limited and highly variable evidence, policymakers and practitioners may be left puzzled as to how to structure teacher PD programs effectively.

In this paper, we propose a set of indicators—the In-service Teacher Training Survey Instrument, or ITTSI—to allow comparisons across teacher PD programs with varying impacts. On average, existing studies of PD programs only report on about half of these indicators. We supplement that information through interviews with implementors of evaluated PD programs. We compare the characteristics of 33 rigorously evaluated PD programs to identify which characteristics are associated with larger student learning gains. We then gather data from 139 government-funded, at-scale PD programs across 14 countries. Like most at-scale government programs, none of these programs have been evaluated rigorously. We compare the two samples to examine whether the PD programs that most teachers actually experience exhibit similar characteristics to those of PD programs that have been evaluated and shown to produce sizeable student learning gains.

When we apply our instrument to evaluated PD programs, results suggest that programs deliver high student learning gains when they link participation in PD to incentives such as promotion or salary implications, when they have a specific subject focus, when teachers practice enacting lessons during the training, and when training has at least an initial face-to-face aspect. Meanwhile, program implementers highlight two characteristics of effective training in interviews: mentoring follow-up visits after the PD training, and complementary materials such as structured lesson plans to help teachers apply what they have learned during PD.

When we subsequently use the ITTSI to characterize a sample of at-scale, government-funded PD programs around the world, we find a divergence in the characteristics common to these programs and those that typify evaluated programs that were found to be effective. Relative to top-performing PD programs—defined as those found to be the most effective at increasing student learning—very few at-scale PD programs are linked to any sort of career opportunities, such as promotion or salary implications. Similarly, in-school follow-up support and including time to practice with other teachers is less common among at-scale PD programs. This highlights a substantial gap between the kind of teacher PD supported by research and that currently being provided by many government-funded, at-scale programs.

These results have implications for both researchers and policymakers. For researchers, future evaluations will contribute much more to an understanding of how to improve teachers’ skills if they report more details of the characteristics of the PD programs. Our proposed set of indicators can serve as a guide. For policymakers, at-scale PD programs should incorporate more aspects of successful, evaluated PD programs, such as incentives, practice, and follow-up in-school support. For both, more programs can be evaluated at scale, using government delivery systems, in order to improve the skills of teachers in the future.

Conceptual Framework

The defining attributes of teacher professional development programs fall principally into three categories. The first is the content of the PD program: What is taught? The second is the delivery of the PD program: Who is teaching, when, and for how long? The third is the organization of the program beyond content and delivery: What are the scale and resources of the program? Are there incentives for participation? Was it designed based on a diagnostic of teachers? In this section, we discuss the theory behind each of these three categories.

On the content, PD programs focused on subject-specific pedagogy are likely to be most effective. General pedagogical knowledge—i.e., broad strategies of classroom management and organization—may contribute to student learning, driving the recent development of a range of classroom observation instruments ( La Paro and Pianta 2003 ; Molina et al. 2018 ). However, different subjects require radically different pedagogies ( Shulman 1986 ; Villegas-Reimers 2003 ). A highly scripted approach may work to teach early grade reading, whereas teaching science or civics in later grades—for example—may require more flexible approaches. PD programs that focus on arming teachers with subject-specific pedagogy are thus likely to make the largest contribution to student learning.

With respect to the delivery, the method, trainers, duration, and location of instruction all play a role. First, because working, professional teachers are the students in PD, principles of adult education are relevant to the method of instruction. Adult education tends to work best with clear applications rather than a theoretical focus ( Cardemil 2001 ; Knowles, Holton, and Swanson 2005 ). The method of instruction should include concrete, realistic goals ( Baker and Smith 1999 ) and the teaching of formative evaluation so that teachers can effectively evaluate their own progress towards their teaching goals ( Bourgeois and Nizet 1997 ). Second, the quality of trainers—i.e., those providing the PD—is crucial to learning ( Knowles, Holton, and Swanson 2005 ). In terms of the delivery of PD, this calls into question the common cascade model of PD in low-income environments, in which both information and pedagogical ability may be diluted as a master trainer trains another individual as a trainer, who may go on to train another trainer below her, and so forth.

Third, on the duration of instruction, there is no theoretical consensus on exactly how long training should last, although there is suggestive empirical evidence in the literature in favor of sustained contact over a significant period of time and against brief, one-time workshops ( Desimone 2009 ). Fourth, on the location of instruction, teacher PD in the school (“embedded”) is likely to be most effective so that participating teachers can raise concrete problems that they face in the local environment, and they can also receive feedback on actual teaching ( Wood and McQuarrie 1999 ). However, this will depend on the environment. In very difficult teaching environments, some degree of training outside the school may facilitate focus on the part of the trainees ( Kraft and Papay 2014 ).

Finally, the organization of the PD—which includes overarching aspects such as who is organizing it, for whom, and how—provides an important backdrop when we consider any PD program. This includes aspects such as the scale, cost, and targeting of the program. In general, it is predictably easier to provide high-quality PD through smaller scale, higher cost programs that provide more tailored attention to a given teacher. In terms of targeting, teacher PD will work best if it adjusts at different points in the teachers’ careers: One would not effectively teach a brand-new teacher in the same way as one would train a teacher with 20 years of experience ( Huberman 1989 ). Teachers see their greatest natural improvements in the first five years of teaching, which may be an indicator of greater skill plasticity, so there may be benefits to leveraging that time ( TNTP 2015 ).

What Works in High-Income Countries?

A full review of the literature in high-income countries is beyond the scope of this study. However, it may be useful to highlight recent work on in-service teacher PD from the United States—which spends almost $18,000 per teacher and 19 days of teacher time on training each year ( TNTP 2015 )—and other high-income countries, in order to ensure that low- and middle-income countries are not ignoring well-established evidence. Several promising themes that emerge from this work are the importance of making PD specific and practical, providing sustained follow-up support for teachers, and embedding it in the curriculum.

Specific and practical teacher PD finds support from multiple reviews of teacher PD studies in high-income countries, which conclude that concrete, classroom-based programs make the most difference to teachers ( Darling-Hammond et al. 2009 ; Walter and Briggs 2012 ). More recently, a meta-analysis of 196 randomized evaluations of education interventions—not just PD—in the United States that measure student test scores as an outcome examined the impact of both “general” and “managed” professional development, relative to other interventions ( Fryer 2017 ). General PD may focus on classroom management or increasing the rigor of teachers’ knowledge, whereas managed professional development prescribes a specific method, with detailed instructions on implementation and follow-up support. On average, managed PD increased student test scores by 2.5 times (0.052 standard deviations) as much as general PD and was at least as effective as the combined average of all school-based interventions. A recent review of nearly 2,000 impact estimates from 747 randomized controlled trials of education interventions in the United States proposes that an effect size of 0.05 be considered a “medium” effect size, higher than the average effect size, weighted by study sample size ( Kraft 2020 ), which suggests that these are not trivial impacts.

The importance of sustained follow-up support is echoed by another U.S.-focused review, which found that PD programs with significant contact hours (between 30 and 100 in total) over the course of six to twelve months were more effective at raising student test scores ( Yoon et al. 2007 ). Likewise, a narrative review of U.S. studies concluded that the most effective programs are not “one-shot workshops”: they are sustained, intense, and embedded in the curriculum ( Darling-Hammond et al. 2009 ).

Despite these conclusions, the experimental or quasi-experimental evidence is thin, even in high-income countries. The meta-analysis of 196 evaluations of education interventions included just nine PD studies ( Fryer 2017 ), and another review of 1,300 PD studies identified just nine that had pre- and post-test data and some sort of control group ( Yoon et al. 2007 ). Similarly, a review of PD in mathematics found more than 600 studies of math PD interventions, but only 32 used any research design to measure effectiveness, and only five of those were high-quality randomized trials ( Gersten et al. 2014 ). The question of what drives effective teacher PD remains understudied, even in high-income environments.

We expect teachers in lower and middle-income countries to learn in fundamentally similar ways to their high-income counterparts. However, lower resource contexts are typically characterized by more binding cost constraints and lower teacher and coach pedagogical capacity. These challenges may make certain elements of PD programs more and less relevant in lower-income contexts. Teachers and coaches in low- and middle-income countries may benefit from more prescriptive instructions on implementation and, while they too require ongoing follow-up as part of PD, this may need to be provided in lower-cost forms, whether in group sessions, using technology for remote coaching, or training school principals and experienced peer teachers as coaches.

To understand which characteristics of PD programs are associated with student test score gains, and to analyze the degree to which these effective characteristics are incorporated into at-scale PD programs in practice, we first developed a standardized instrument to characterize in-service teacher training. Second, we applied this instrument to already evaluated PD programs to understand which PD characteristics are associated with student learning gains. Third, we applied the survey instrument to a sample of at-scale PD programs to see how these programs line up with what the evidence suggests works in teacher training. The information we present thus comes from two different samples of PD programs: One sample of evaluated PD programs, those with impact evaluations that include student assessment results; and one sample of at-scale , government-funded PD programs. 1 The remainder of this section introduces the instrument briefly before describing its application to each of the two samples.

The In-Service Teacher Training Survey Instrument (ITTSI)

The ITTSI was designed based on the conceptual framework and empirical literature characterized in the previous sections, as well as on the authors’ prior experience studying in-service teacher PD. We drafted an initial list of 51 key indicators to capture details about a range of program characteristics falling into three main categories: Organization, Content, and Delivery, paralleling the three elements of our conceptual framework ( fig. 1 ). We supplement those categories with a fourth category, Perceptions, which we added to collect qualitative data from program implementors.

Summary of the In-Service Teacher Training Survey Instrument (ITTSI)

Source : Authors’ summary of the elements of the In-Service Teacher Training Survey Instrument, as detailed in supplementary online appendices A1 and A2 .

Taking each of these in turn, the Organization section includes items such as the type of organization responsible for the design and implementation of a given teacher training program, to whom the program is targeted, what (if any) complementary materials it provides, the scale of the program, and its cost. The Content section includes indicators capturing the type of knowledge or skills that a given program aims to build among beneficiary teachers, such as whether the program focuses on subject content (and if so, which subject), pedagogy, new technology, classroom management, counseling, assessment, or some combination.

Delivery focuses on indicators capturing program implementation details, such as whether it is delivered through a cascade model, the profile of the trainers who directly train the teachers, the location of the training, the size of the sessions, and the time division between lectures, practice, and other activities. Finally, the Perceptions section includes indicators capturing program implementers’ own perceptions of which elements were responsible for any positive impacts and which were popular or unpopular among teachers. We piloted the draft instrument by using it to collect data on a sample of evaluated programs, and validated its ability to accurately characterize the details of PD programs by sharing our results with a series of expert researchers and practitioners in teacher PD. We updated the indicators in light of this feedback, resulting in a final version of the instrument, which includes 70 indicators plus three pieces of metadata. Further information on the instrument can be found in the supplementary online appendices: Appendix A1 provides a more detailed description of instrument development; appendix A2 presents the final instrument (ITTSI); and appendix A3 presents the Brief In-Service Teacher Training Instrument (BITTSI), a supplementary instrument we developed containing a subset of the 13 most critical questions from the ITTSI based on our reading of the literature.

The ITTSI does not collect extensive data about the broader educational context. Context includes teacher policies (e.g., pre-service training and the structure of the teacher career), other education policies, and the current state of education (e.g., learning and absenteeism rates). Context matters for the impact of teacher PD programs. As a simple example, in a setting where student absenteeism is extremely high, teacher PD programs may have a limited impact on student learning due to few hours of contact between teachers and students. That said, certain principles of teacher PD may translate across cultures, even if the applications vary. Professionals need practice to master skills across contexts, so giving teachers the opportunity to practice lessons during training may be valuable across contexts, even if how they do that may vary. Other survey instruments have been developed and tested broadly to gather a wide range of data on the education system, notably the World Bank's Systems Approach for Better Education Results (SABER) ( Rogers and Demas 2013 ). For a rich view of teacher PD in context, the ITTSI could be complemented with the SABER instrument or other data about the education system.

Applying the ITTSI to Evaluated PD Programs

We searched the existing literature on in-service teacher PD in low- and middle-income countries to identify a sample of PD programs that had been evaluated for their impact on student learning. Our inclusion criteria for the search were impact evaluations of primary and secondary education interventions in low- and middle-income countries that (a) focused primarily on in-service teacher PD or included this as a major component of a broader program, and (b) reported impacts of the program on student test scores in math, language, or science. We included both published and unpublished papers and did not restrict by year of authorship.

In order to identify papers fulfilling the above criteria, we searched a range of databases in 2016 . 2 The search yielded 6,049 results and automatically refined the results by removing exact duplicates from the original results, which reduced the number of results to 4,294. To this we added 20 impact evaluations which mention teacher PD from a recent review ( Evans and Popova 2016a ). We examined the 4,314 results from both sources to exclude articles that—from their title and abstract—were clearly not impact evaluations of teacher training programs. This review process excluded 4,272 results and left 42 full articles to be assessed for eligibility. After going through the full texts, another 18 papers were excluded as the full text revealed that they did not meet the inclusion criteria. This yielded 23 papers, which evaluated 26 different PD programs. In February 2018, we updated this original sample with full articles published between 2016 and 2018 which fit the inclusion criteria. This resulted in seven new papers and teacher PD programs for a total of 30 papers evaluating 33 programs. The search process is detailed in  fig. 2 . The 30 papers are listed in supplementary online appendix A4 .

Search Process and Results for Evaluated Professional Development Programs

Source : Constructed by the authors based on the search described in the text.

Note : The 30 papers documenting the evaluation of the final 33 programs are listed in supplementary online appendix A4 .

Data collection and coding for the sample of 33 evaluated programs comprised two phases. The first of these phases consisted of carefully reviewing the impact evaluation studies and coding the information they provided. The draft version of the instrument for which we collected data included 51 indicators in total, and on average, information on 26 (51 percent) of these indicators was reported in the impact evaluations. Crucially, the amount of program information reported across the impact evaluations varies noticeably by topic ( table 1 ). Sixty-four percent of details concerning the organization of teacher training programs—such as whether the program was designed by a government or by a non-governmental organization (NGO)—can be extracted from the evaluations. In contrast, on average, only 47 percent of information concerning program content and 42 percent of information concerning program delivery is reported.

Data Available on Evaluated Programs from Studies vs. Interviews

Source : Constructed by the authors based on the application of the In-Service Teacher Training Survey Instrument items ( supplementary online appendix A2 ) to the 33 professional development programs identified ( supplementary online appendix A4 ).

Note : Percentage data collected refers to the percentage of indicators for which data were collected across the 33 programs in our evaluated sample. This is calculated by the number of programs for which each indicator has data, summed for every indicator in a given section (or total) and divided by the number of indicators in that section (or total), and finally divided by the 33 programs.

The second phase of data collection sought to fill this gap in reported data by interviewing individuals involved in the actual implementation of each program. To do this, we emailed the authors of each of the impact evaluations in our sample, asking them to connect us with the program implementers. After three attempts to contact the implementers, we received responses from authors for 25 of the 33 programs. We contacted all of the individuals to whom the authors referred us—who in many cases directed us to more relevant counterparts—and were eventually able to hold interviews with program implementers for 18 of the 33 programs. 3 The interviews loosely followed the survey instrument, but included open-ended questions and space for program implementers to provide any additional program information that they perceived as important.

The ITTSI data were gathered retrospectively for this study, which means that in most cases, the evaluation results (and so whether or not the program was effective) were likely to have been known to the interviewee. We propose three reasons that this should not pose a substantive problem for the quality of the data. First, most of the indicators have no normative response. Whether a program is government- or researcher-designed or implemented, whether it has a subject focus or a general pedagogy focus, or whether or not it has a distance learning element have no obvious “right” answers. Second, the survey was administered to program implementers, who usually were not part of the team of researchers who evaluated the program, so they had little stake in confirming research results. Third, the survey had low stakes: interviewees knew that we were independent researchers doing a synthesis review. In some cases, the PD program being discussed no longer existed in the same form. For future PD studies, these data could be collected at the design stage of programs.

For the 18 programs for which we conducted interviews, we were able to collect information for an average of 50 out of the 51 (98 percent) indicators of interest. Consequently, conducting interviews decreased the differences in data availability across categories. The pooled average of indicators for which we had information after conducting interviews (for interviewed and not interviewed programs combined) increased to 79 percent for Organization indicators, 68 percent of Content indicators, and 72 percent of Delivery indicators ( table 1 ).

For our sample of evaluated in-service teacher PD programs, we analyze which characteristics of teacher training programs are associated with the largest improvements in student learning, as measured by test score gains. We conduct both quantitative and qualitative analyses. The analytical strategy for the quantitative analysis essentially consists of comparing means of student learning gains for programs with and without key characteristics, using a bivariate linear regression to derive the magnitude and statistical significance of differences in means. We do not carry out multivariate regression analysis because of the small sample; thus, these results are only suggestive, as multiple characteristics of programs may be correlated. Because we are testing each coefficient separately, we are not able to test the relative value of coefficients, so differences in point estimates are only suggestive.

In preparation for this analysis, we standardize the impact estimates for each of the programs. We convert the program characteristic variables to indicator variables wherever possible to facilitate comparability of coefficients. Although our sample of impact evaluations has a common outcome—impact on student test scores—these are reported on different scales across studies, based on different sample sizes. 4 We standardize these effects and the associated standard errors in order to be able to compare them directly. Supplementary online appendix A5 provides mathematical details of the standardization.

Turning to the independent variables, as originally coded, the 51 indicators for which we collected information capturing various design and implementation characteristics of the PD programs took a number of forms. These consisted of indicator variables (e.g., the intervention provides textbooks alongside training = 0 or 1), categorical variables (e.g., the primary focus of the training was subject content [= 1], pedagogy [= 2], new technology [= 3]), continuous variables (e.g., the proportion of training hours spent practicing with students), and string variables capturing open-ended perceptions (e.g., which program elements do you think were most effective?). To maximize the comparability of output from our regression analysis we convert all categorical and continuous variables into indicator variables. 5

We then conduct our bivariate regressions on this set of complete indicator variables with continuous impact estimates on test scores as the outcome variable for each regression. Because of the limitations associated with running a series of bivariate regressions on a relatively small sample of evaluations, we propose the following robustness check. First, we estimate robust Eicker-Huber-White (EHW) standard errors as our default standard errors (reported in  tables 2 – 4 ) and assess significance according to p -values associated with these. Second, we estimate bootstrapped standard errors and the associated p -values. Third, we run Fisher randomization tests to calculate exact p -values, a common approach in the context of small samples. 6 We report significance under each of these methods separately and report results as robust if they are significant under at least two of the three methods, and if the significant effect is driven by at least two observations—i.e., the results are not explained by a single PD program.

Organization – Bivariate Regressions with Robustness Checks

Source : Constructed by the authors based on data extracted from 33 professional development programs ( supplementary online appendix A4 ) using the In-Service Teacher Training Survey Instrument, and analyzed by regression, as described in the text.

Note : ∗ p  < 0.10, ∗∗ p  < 0.05, ∗∗∗ p  < 0.01 correspond to the significance of p- val ues of robust standard Noteerrors. § corresponds to significance at the 10 percent level or higher for bootstrapped standard errors. † corresponds to significance at the 10 percent level or higher for the Fisher Randomization tests. Numbers specified in parentheses in variable labels are the reported medians for dummy variables in which the variable equals 1 if greater than the median. Total programs refers to the number of programs that report whether or not they have the characteristic. The robust column includes an X if the finding is statistically significant across at least two methods and if the finding is driven by two or more evaluations (i.e., not a single evaluation).

Content – Bivariate Regressions with Robustness Checks

Note : ∗ p  < 0.10, ∗∗ p  < 0.05, ∗∗∗ p  < 0.01 correspond to the significance of p -values of robust standard errors. § corresponds to significance at the 10 percent level or higher for bootstrapped standard errors. † corresponds to significance at the 10 percent level or higher for the Fisher Randomization tests. Total programs refers to the number of programs that report whether or not they have the characteristic. The robust column includes an X if the finding is statistically significant across at least two methods and if the finding is driven by two or more evaluations (i.e., not a single evaluation).

Delivery – Bivariate Regressions with Robustness Checks

Note : ∗ p  < 0.10, ∗∗ p  < 0.05, ∗∗∗ p  < 0.01 correspond to the significance of p -values of robust standard errors. § corresponds to significance at the 10 percent level or higher for bootstrapped standard errors. † corresponds to significance at the 10 percent level or higher for the Fisher Randomization tests. Numbers specified in parentheses in variable labels are the reported medians for dummy variables in which the variable equals 1 if greater than the median. Total programs refers to the number of programs that report whether or not they have the characteristic. The robust column includes an X if the finding is statistically significant across at least two methods and if the finding is driven by two or more evaluations (i.e., not a single evaluation).

We supplement this regression analysis with a qualitative analysis of what works, relying on the self-reported perceptions of program implementers along three dimensions: (a) Which program elements they identified as most responsible for any positive impacts on student learning; (b) which elements, if any, teachers particularly liked; and (c) which elements, if any, teachers particularly disliked.

Applying the ITTSI to At-Scale PD Programs

The sampling process for at-scale programs is detailed in  fig. 3 . To obtain a sample of at-scale, government-funded PD programs across the world, we first identified four to five countries in each region where the World Bank has operations. 7 We worked with regional education managers at the World Bank in each region to select countries in which government counterparts and World Bank country teams had an interest in learning more about in-service teacher PD programs. We made clear that the exercise was appropriate for countries with any level of teacher PD, not specific to countries with recent reforms or innovations. The final set of countries sampled included Burkina Faso, Cambodia, El Salvador, The Gambia, Guinea, India (Bihar state), Jordan, Kazakhstan, the Kyrgyz Republic, Mauritania, Mexico (Guanajato, Oaxaca, and Puebla, and a national PD program for middle school teachers), Moldova, Niger, and the Russian Federation.

Sampling Process for At-Scale Professional Development Programs

Source : Constructed by the authors to reflect the process to identify at-scale professional development programs, as described in the text.

We then obtained permission from the Ministry of Education (MoE) or other relevant government counterparts in each country and worked with them to complete a roster, or listing, of all teacher PD programs conducted between 2012 and 2016. 8 The roster, available in supplementary online appendix A6 , was created along with the ITTSI instrument and collects the following information about each of the teacher PD programs that received government funding: program name; program coordinator's name and contact information; the number of teachers trained; and the types of teachers targeted (e.g., pre-primary, primary, or secondary school teachers). In some countries, such as Mexico and India, where policymaking about teacher PD happens at the state level, we worked with individual states.

After receiving completed roster information about teacher PD programs in a country/state, we used the roster to select a sample of teacher PD programs to interview. In each country/state, we chose the sample by selecting the 10 largest teacher PD programs in terms of teacher coverage, defined as the number of teachers reached by the program during its most recent year of implementation. Of the 10 sampled programs for each country/state, the full ITTSI was administered to the two largest programs targeting primary school teachers and the largest program that targeted secondary school teachers. The brief version of the instrument, the BITTSI, was administered in the remaining seven programs in the country/state. In total, 48 at-scale programs completed the ITTSI and 91 at-scale programs completed the BITTSI across 14 countries.

We applied the ITTSI survey through a combination of phone interviews with and online surveys of PD program coordinators. In a few instances (in The Gambia, El Salvador, and Mexico), depending on the preferences of the program coordinator and their primary language, program coordinators were given the option of completing the ITTSI questionnaire online. For the majority of programs, however, we held phone interviews with program coordinators, in which we asked them the questions included in the ITTSI survey items directly and filled out the instrument ourselves with their responses.

The ITTSI survey applied to the sample of at-scale programs consists of 70 indicators. We were able to collect information for an average of 66 of the 70 (94 percent) indicators of interest for the 48 at-scale teacher PD programs to which the full ITTSI survey was applied, and for 26.5 of the 27 (97 percent) indicators—derived from 13 questions—for the 91 programs to which the BITTSI was applied.

For the sample of at-scale PD programs, we compare the average of observed characteristics of at-scale teacher PD programs with the average for evaluated PD programs that resulted in the largest improvements in student learning (“top performers”), as measured by student test score gains. To determine the characteristics of “top performers,” we ranked all evaluated programs, using their standardized impact on student test scores. We then selected the top half of programs (16 programs, all of which displayed positive impacts), and calculated the average value of program indicators for those “top performers.” We compare them to the means of at-scale PD programs in order to better understand the gap between at-scale PD practices and the best practices of top-performing PD programs.

This section characterizes the specific characteristics of teacher PD programs that successfully improve student learning in low- and middle-income countries and how common these characteristics are across at-scale, government-funded programs. First, we present the results of our quantitative and qualitative analyses examining which PD characteristics are associated with large gains in student learning for the sample of evaluated programs. Second, we present descriptive statistics from the sample of at-scale PD programs and from the top-performing PD programs in the evaluated sample to shed light on how they differ in terms of those PD characteristics found to be associated with positive impacts on student learning.

Which PD Characteristics are Most Associated with Student Learning Among Evaluated Programs?

We discuss, for each of our categories—Organization, Content, and Delivery—those characteristics we observe to be most associated with student learning gains.  Tables 2 – 4 present the results of our bivariate regressions for each of these categories in turn. In each case, we report the results with the three different methods of calculating significance as well as an indicator of robustness.

Among Organization ( table 2 ), two characteristics are robustly associated with significant gains in student learning. These include linking career opportunities (improved status, promotion, or salary) to PD programs and targeting training programs based on teachers’ years of experience. First, in teacher PD programs where participation has no implications for promotion, salary, or status increases, student learning is 0.12 standard deviations lower (significant at 95 percent). In other words, programs that do link participation to career incentives have higher effectiveness. 9 Second, targeting participant teachers by their years of experience is associated with 0.10 standard deviations higher student learning (significant at 90 percent). This is driven by two programs: the Balsakhi program in rural India, which trains women from the local community who have completed secondary school to provide remedial education to students falling behind ( Banerjee et al. 2007 ); and the Science teacher training program in Argentina, which trains teachers in different structured curricula and coaching techniques and finds that coaching is only effective for less experienced teachers ( Albornoz et al. 2018 ). Indeed, these are the only two programs out of the 33 that explicitly targeted teachers based on their experience, both of which resulted in student learning gains. In addition, the provision of complementary materials such as storybooks and other reading materials (e.g., flashcards or word banks) have large coefficients associated with improving student learning (0.11 and 0.13 standard deviations), although these are not statistically significant.

Among the Content variables ( table 3 ), programs with a specific subject focus result in higher learning gains than more general programs. Specifically, programs with no subject focus show 0.24 standard deviations lower impact on student learning (significant at 99 percent). A deeper look reveals that within focus areas, programs that are not focused on a given academic subject—such as those focused on counseling—are associated with 0.2 lower standard deviations in student learning (significant at 99 percent). Lastly, when a teacher PD program involves teaching practice through lesson enactment, it is associated with a 0.10 standard deviation increase in student learning (significant at 90 percent).

Turning to Delivery characteristics ( table 4 ), three characteristics of teacher PD programs are robust. First, teacher PD programs that provide consecutive days of face-to-face teacher training are associated with a 0.14 standard deviation increase in student learning (significant at 99 percent). Second, holding face-to-face training at a central location—such as a hotel or government administrative building (as opposed to a university or training center, which was the omitted category)—is associated with a 0.13 lower standard deviation in student learning (significant at 90 percent). Third, teacher PD training programs that are conducted remotely using distance learning are associated with a 0.10 standard deviation decrease in student learning (significant at 90 percent). In alignment with recent literature highlighting the overly theoretical nature of many training programs as an explanation for their limited effects on student learning—as well as the above finding that training programs that involve teaching practice are associated with 0.16 larger gains in student learning—the proportion of training time spent practicing with other teachers is highly correlated with learning impacts (although not consistently statistically significant). Also, the inclusion of follow-up visits to review material taught in the initial training—as opposed to visits for monitoring purposes alone or no follow-up visits—is associated with a 0.14 standard deviation higher program impact on student learning (not significant, but one of the largest coefficients). These findings support the literature that subject-focused teacher PD programs with consecutive days of face-to-face training that include time for teachers to practice with one another, are associated with improved student learning outcomes.

We supplement the quantitative results with an analysis of self-reported perceptions by the implementers of the evaluated programs. These concern the characteristics of their programs which they believe are most responsible for any positive effects on student learning, as well as those elements which were popular and unpopular among the beneficiary teachers. We elicited these perceptions using open-ended questions and then tallied the number of program implementers that mentioned a given program element in their response, albeit not necessarily using the exact same language as other respondents. These responses come from 18 interviewees, so they should be taken as suggestive. That said, the results broadly align with the quantitative results: Five of 18 interviewees—tied for the most common response—mentioned that mentoring follow-up visits were a crucial component in making their training work. Similarly, five of the 18 interviewees discuss the importance of having complementary materials, such as structured lessons or scripted materials that provide useful references in the classroom and help to guide teachers during the training sessions. The next most commonly reported elements were engaging teachers for their opinions and ideas—either through discussion or text messages—and designing the program in response to local context, building on what teachers already do and linking to everyday experiences: both were mentioned by four of 18 interviewees.

We also asked the program implementers about the program characteristics that they believed teachers liked and disliked the most about their training programs and, interestingly, we only found two common responses for what teachers particularly liked and one common response for what they disliked. 10 Seven of the 18 interviewees reported that the part of their program that teachers most enjoyed was that it was fun and engaging (or some variation of that). In other words, teachers appreciated that certain programs were interactive and involved participation and discussion rather than passive learning. In addition to having “fun” teacher PD programs, five of the 18 interviewees suggested that teachers especially liked the program materials provided to them. Similarly, in terms of unpopular program elements, four of the 18 program implementers we interviewed reported that teachers disliked the amount of time taken by participating in the training programs, which they perceived as excessive.

What Do We Learn from At-Scale PD Programs?

Government-funded, at-scale teacher PD programs have a number of characteristics in common ( supplementary online appendix tables A7.1–A7.3 ). The vast majority are designed by government (80 percent) and implemented by government (90 percent). Almost all provide materials to accompany the PD (96 percent), and most include at least some lesson enactment (73 percent) and development of materials (73 percent). Most have a subject focus (92 percent) and include an initial period of face-to-face training for several days (85 percent). Most do not formally target teachers by subject (only 19 percent do), grade (31 percent), or years of experience (13 percent), and few have negative consequences if teachers are poorly evaluated (17 percent). These at-scale programs differ sharply from programs that are evaluated in general, as well as from top-performing evaluated programs specifically. We provide a full list of average characteristics of at-scale programs and all evaluated programs (not just top-performers) in supplementary online appendix tables A7.1–A7.3 .

Our principal focus in this section is how at-scale programs compare to evaluated programs that deliver relatively high gains in student learning. We assess the top half of programs (N = 16) from the sample of evaluated programs by selecting those characteristics that produced the largest standard deviation increases in student assessment scores. In  table 5 , we compare the means of at-scale programs and top-performing, evaluated programs. We focus specifically on the characteristics shown to have a statistically significant relationship with student learning outcomes and those with large coefficients, identified for interest (as identified in  tables 2 – 4 ).

Comparison of Means of At-Scale Programs and Top-Performing, Evaluated Programs

Source : Constructed by authors, comparing summary statistics for the top performing professional development (PD) programs among rigorously evaluated PD programs to at-scale PD programs.

Note : For the full list of statistics, see supplementary online appendix Tables A7.1–A7.3 .

Regarding Organization ( table 5 ), two key characteristics—whether or not the training is linked to career opportunities and whether or not the program targets teachers based on their years of experience—are robustly associated with improved student learning gains. There are notable and substantive differences between top-performing PD programs and the sample of at-scale PD programs when it comes to providing incentives; 88 percent of top-performing PD programs link training to status or to new career opportunities such as promotion or salary, as compared to only 55 percent of at-scale programs. Our results suggest that without incentives, training may not have a meaningful impact. Furthermore, top-performing programs and at-scale PD programs are similar in the degree to which they target teachers based on their years of experience. For instance, 13.3 percent of top-performers and 12.5 percent of at-scale programs target teachers based on their experience. Other notable organizational characteristics include the provision of complementary materials such as storybooks and reading materials. Top-performing PD programs and at-scale PD programs are similar in the amount of materials they provide, but our results suggest that the kinds of complementary materials may differ somewhat. For instance, only 12.5 percent and 21 percent of at-scale programs provide storybooks and reading materials, respectively—materials correlated with student learning gains—as compared to 36 percent and 43 percent of evaluated programs.

Turning next to Content ( table 5 ), top-performing PD programs and at-scale PD programs perform similarly. In both instances, the majority of programs include subject content and subject-specific pedagogy as either a primary or secondary focus. Few programs—none of the top performers—and only eight percent of at-scale programs lack a subject focus. Moreover, no top-performing programs and few at-scale programs (fewer than six percent) focus on general training in areas such as counseling or providing training on how to use a specific tool—types of training that are statistically linked to lower gains in student learning.

Finally, Delivery characteristics ( table 5 ) include whether or not there are consecutive days of face-to-face training, training location, the amount of time teachers spend practicing with one another, and follow-up visits. Specifically, 100 percent of top-performing programs include consecutive days of face-to-face training as compared to 85 percent of evaluated programs. Our research further suggests that the location of PD training programs may influence program effectiveness, and training held at central locations such as hotels or conference rooms (as opposed to universities or training centers) may be less effective. Currently 73 percent of at-scale, government-funded programs are held at central locations as compared to only 38 percent of evaluated programs.

Follow-up visits with teachers and the amount of time teachers spend practicing with other teachers during the training program are shown to be positively correlated with large coefficients (albeit not statistically significant) on student learning. In both instances, top-performing PD programs include more follow-up visits (five versus two median visits among programs with visits) and spend more time allowing teachers to practice with other teachers (40 percent versus 16 percent of training time) than do at-scale programs. 11 Results of our analysis suggest that training may be more effective if there are follow-up visits. This is an imperative finding when comparing top-performing PD programs, in which 85 percent include follow-up visits, with government-funded, at-scale PD programs, in which only half of programs include follow-up visits. Also, in top-performing PD programs, teachers spend more time practicing what they have learned with other teachers (40 percent of overall training time) relative to at-scale programs (only 16 percent). An existing body of research suggests that when teachers have opportunities to practice the new skills they acquire in PD programs, they are more likely to adopt these new skills in their classrooms ( Wiley and Yoon 1995 ; Wenglinsky 2000 ; Angrist and Lavy 2001 ; Borko 2004 ).

Governments spend enormous amounts of time and money on in-service professional development. Many countries have multiple in-service PD programs running simultaneously, as evidenced by our sample of at-scale PD programs. Many go unevaluated and may be ineffective. This paper makes three major contributions: first, it reveals broad weaknesses in reporting on teacher PD interventions. There are almost as many program types as there are programs, with variations in subject and pedagogical focus, hours spent, capacity of the trainers, and a host of other variables. Yet reporting on these often seeks to reduce them to a small handful of variables, and each scholar decides independently which variables are most relevant to report. We propose a standard set of indicators—the ITTSI—that would encourage consistency and thoroughness in reporting. Academic journals may continue to pressure authors to report limited information about the interventions, wishing instead to reserve space for statistical analysis. However, authors could easily include the full set of indicators in an appendix attached to the paper or online.

Second, this paper demonstrates that some characteristics of teacher PD programs—notably, linking participation to incentives such as promotion or salary implications, having a specific subject focus, incorporating lesson enactment in the training, and including initial face-to-face training—are positively associated with student test score gains. Furthermore, qualitative evidence suggests that follow-up visits to reinforce skills learned in training are important to effective training. Further documentation of detailed program characteristics, coupled with rigorous evaluation, will continue to inform effective evaluations.

The impacts of these characteristics are not small: having a specific subject focus and incorporating lesson enactment are associated with 0.24 and 0.10 more standard deviations in learning, respectively, for example. Comparing these effect sizes to those from a sample of 747 education-related randomized controlled trials in the United States puts them both above the 50th percentile in terms of effectiveness ( Kraft 2020 ). Comparing to a set of 130 randomized controlled trials in low- and middle-income countries likewise put them at or above the 50 th percentile of 0.10 standard deviations ( Evans and Yuan 2020 ). In high-income countries, Kennedy (2019) proposes that the impact of teacher PD programs be benchmarked against a much less costly “community of practice” model in which teachers help each other, like Papay et al. (2020) . While we are not aware of a rigorously evaluated, costed model of that class of program in a low- or middle-income country, an alternative would be to compare teacher PD results to a pure monitoring model, such as an increase in inspections. Along these lines, Muralidharan et al. (2017) show—using data from India—that increased frequency of monitoring would be a much more cost-effective way to reduce effective class sizes (through reduced teacher absenteeism) than hiring more teachers. These are useful avenues to pursue for future research as countries consider the cost-effectiveness of alternative investments in teachers.

Third, by comparing the means of at-scale PD programs with top-performing evaluated programs, our findings highlight gaps between what evidence suggests are effective characteristics of teacher PD programs and the contextual realities of most teacher PD programs in their design, content, and delivery. In particular, our findings taken together suggest that at-scale programs often lack key characteristics of top-performing training programs. At-scale programs are much less likely to be linked to career incentives, to provide storybooks or other reading materials, to have a subject content focus, to include time for practicing with other teachers, or to include follow-up visits.

The approach taken by this paper centers on using the ITTSI to collect and compare data on rigorously evaluated and at-scale, government-funded teacher PD programs. This approach has limitations. First, the evidence of what works within rigorously evaluated programs is limited by those programs that have been evaluated. There may be innovative PD programs that are not among the “top performers” simply because they have yet to be evaluated. While this evidence base can push policymakers away from approaches that do not work, it should not deter policymakers from innovating and evaluating those innovations.

A second, related limitation concerns the relatively small sample of evaluated teacher PD programs in low- and middle-income countries, on which our findings about effective PD characteristics are based. Some of the larger coefficients in the regressions are driven by a small number of teacher training programs. These instances have been noted in the text. As more evaluations of PD programs are conducted, the ITTSI can be applied to these and our analyses re-run to shed further light on the specific characteristics associated with PD programs that improve student learning. The ITTSI data were already updated once in this way in 2018, increasing the number of evaluated programs in our sample from 26 to 33.

Third, a conceptual concern with evaluating teacher professional programs is the risk that impacts may be explained by observer effects (also referred to as Hawthorne effects). These effects have been documented in education ( Muralidharan et al. 2017 ) and health in low- and middle-income countries ( Leonard 2008 ; Leonard and Masatu 2010 ). The impact of any education intervention may partly be due to observer effects, since the introduction of an intervention suggests that someone is paying attention to the teacher's efforts. Both randomized controlled trials and more traditional monitoring and evaluation may enhance these effects, as teachers may further respond favorably to the observation associated with measurement. Randomized controlled trials and quasi-experimental studies with a credible comparison group overcome part of this concern, as the observer effect associated with measurement will exist in both the treatment and comparison groups, and measured program impacts should be net of those effects.

That leaves the impact of the intervention itself. In this review, all of the studies we include evaluate interventions and, as such, all may be subject to an observer effect. Our analysis implicitly assumes the magnitude of this observer effect to be constant across different types of PD. By comparing PD characteristics across programs, we observe whether those characteristics are associated with a larger total effect on learning. Part of that total effect may stem from increased teacher skills, and part may be explained by certain PD characteristics inducing greater observer effects (since any observer effects that are uncorrelated with PD characteristics would be absorbed in our regression constant terms). In the short run, the impact for students is observationally equivalent. Even with longer run studies (of which there are very few in education and development), observer effects may fade, but teacher skills may also depreciate ( Cilliers et al. 2020 ). As a result, we consider the total association of PD characteristics with student learning, including through increased teacher human capital and observer effects.

Fourth, there are challenges in comparing evaluated PD programs with at-scale PD programs. As the data demonstrate, at-scale PD programs tend to be larger programs designed by governments, often at the national level, and aimed at providing broad training to teachers. In light of these differences, we highlight the fact that top-performing programs—regardless of their core objectives—share certain common sets of characteristics that most at-scale programs do not share. Awareness of these characteristics may be useful in the conceptualization and implementation of future teacher PD programs in low- and middle-income countries, including large-scale programs funded by governments.

One key reason that at-scale programs may differ from successful, evaluated programs is that the latter group of evaluations may not be designed in a way that is conducive to scaling. Evaluated programs tend to be much smaller than at-scale programs: in our data, evaluated programs reached an average of 96 schools versus at-scale programs that reached more than 6,000 schools on average ( supplementary online appendix table A7.1 ). These smaller programs often have higher per-pupil costs ( Evans and Popova 2016b ), so scaling them nationwide requires cutting elements. Smaller programs are easier to staff and easier to monitor. Evaluated programs were three times as likely to be designed by researchers and less than one-third as likely to be implemented by government ( supplementary online appendix table A7.1 ). One solution, obviously, is more large-scale evaluations, like Loyalka et al. (2019) . However, even smaller evaluations can do more to mimic scalable policies. Gove et al. (2017) , reflecting on programs evaluated both at pilot and at scale in Kenya and Liberia, suggest the value of testing as many elements as possible in the pilot, using government systems in the pilot as much as possible, and to make sure that pilot costs are within what a government budget can handle. Duflo et al. (2020) combine these two approaches in a recent nationwide, five-arm randomized controlled trial in Ghana, to test the scalability of four different models to reach remedial learners, which had previously been tested in small pilot randomized controlled trials elsewhere. When implemented within existing government systems, they find all four interventions to be effective, pointing to the program's inception within the government as key, as opposed to an initial non-government organization initiative subsequently and imperfectly implemented by the government.

Improving in-service teacher professional development may be a clear win for governments. They are already spending resources on these programs, and there is broad support for these programs among teachers and teachers’ unions. Interventions such as the above provide learning opportunities for country governments and stakeholders seeking to design effective teacher PD programs. While no single characteristic of top-performing PD programs may transform an ineffective PD program into an effective one, this paper highlights trends in top-performing programs, such as including incentives, a specific subject focus, and lesson enactment. These are characteristics that, if included and implemented successfully, have the potential to improve the quality of teacher PD programs, and ultimately, the quality of instruction and student learning.

The authors are grateful for comments from Denise Bello, Luis Benveniste, Barbara Bruns, Martin Carnoy, Joost de Laat, Margaret Dubeck, Deon Filmer, Susanna Loeb, Prashant Loyalka, Ezequiel Molina, Andrew Ragatz, and Halsey Rogers. They are also grateful to Fei Yuan for excellent research assistance, to Veronica Michel Gutierrez, Olga A. Rines, Lea Jeanne Marie Lungmann, Fata No, and Elissar Tatum Harati for their support with data collection, and to numerous teacher training implementers for providing information on programs. This paper subsumes an earlier paper, “Training Teachers on the Job: What Works and How to Measure It” (World Bank Policy Research Working Paper Number 7834).

This work was supported by the Bill & Melinda Gates Foundation, the World Bank's Systems Approach for Better Education (SABER) Trust Fund, which was supported by the United Kingdom's Department for International Development (DFID) and Australia's Department of Foreign Affairs and Trade (DFAT), and the Strategic Impact Evaluation Fund at the World Bank.

Both samples focus on teacher training programs at the primary and secondary school level. Pre-primary schools are excluded.

The databases we searched were the Education Resources Information Center (ERIC); Academic Search Complete; Business Source Complete; Econlit with Full Text; Education Full Text (H. W. Wilson); Education Index Retrospective: 1929–1983; Education Source; Educational Administration Abstracts; Social Science Full Text (H. W. Wilson); Teacher Reference Center; and EconLit. We looked for articles containing the terms (“teacher training” OR “teacher education” OR “professional development”) AND (``learning'' OR ``scores'' OR ``attainment'') AND (“impact evaluation” OR ``effects'') AND (“developing country 1” OR “developing country 2” OR “developing country N”), where “developing country” was replaced by country names.

In six cases, program implementers failed to schedule an interview after three attempts at contact, and in the case of one older program, the implementer had passed away. Interviews were held over the phone or in-person, and lasted between 45 and 90 minutes for each program.

A limitation is that some of the impact estimates from school-randomized control trials in our evaluated sample are over-estimates because the authors fail to account for the clustering of children within teachers or schools ( Hedges 2009 ).

For categorical variables, this is straightforward. For example, we convert the original categorical variable for the location of the initial teacher PD—which includes response options of schools, a central location, a training center, or online—into four dummy variables. In order to convert the continuous variables to a comparable scale, we create a dummy for each continuous variable which, for a given program, takes a value of 1 if the continuous variable is greater than the median value of this variable across all programs, and a value of 0 if it is less than or equal to the value of this variable across all programs. We apply this method to the conversion of all continuous variables except three—proportion of teachers that dropped out of the program, number of follow-up visits, and weeks of distance learning—which we convert directly to dummy variables that take a value of 1 if the original variable was greater than 0, and a value of 0 otherwise.

We estimate bootstrapped standard errors by resampling our data with replacement 1,000 times. We run Fisher randomization tests by treating each indicator PD characteristic as a treatment and calculating a randomization distribution of mean differences (the test statistic) across treatment assignments. Specifically, for 1,000 permutations, we randomly reassign values of 0 or 1 to the independent variables in our regressions, while maintaining the overall proportion of 0s and 1s observed in the empirical sample for a given variable. We then calculate Fisher exact p -values by finding the proportion of the randomization distribution that is larger than our observed test statistic ( Fisher 1925 , 1935 ; Imbens and Rubin 2015 ).

These regions include: Africa, Eastern and Central Europe, Latin American and the Caribbean, the Middle East and North Africa, and East and South Asia.

This includes programs ongoing in 2016 and programs that were implemented anytime in the range of 2012 to 2016. Hence, the programs could have been designed prior to 2012. We still include them if they were implemented any time between 2012 and 2016. We were not successful in obtaining roster information in all countries. For instance, in Morocco and the Arab Republic of Egypt, the Ministries of Education were in the process of making changes to the structure and delivery of teacher training programs and indicated that it was not a good time for data collection. In Tanzania there was a change in leadership among government counterparts during efforts to complete the roster and data collection process, and we were not able to properly sample and apply the ITTSI in all teacher-training programs in the country. In India, we had initially identified two states, Bihar and Karnataka, to work with at the subnational level, but ultimately only collected data in one state, Bihar, since the principal government counterpart in Karnataka was not available to complete the roster.

In some cases, we test a negative (e.g., no implications for status in table 2 or no subject focus in table 3 ) because we are testing an exhaustive series of indicators derived from the same question (e.g., subject focus is math, subject focus is literacy, or no subject focus).

Because it is difficult to imagine an effective teacher professional development program that teachers actively dislike (they have to learn for it to work, after all), their preferences are relevant.

When we include programs with no follow-up visits, the median number of follow-up visits to teachers in top programs becomes 3.5 as compared to 0 for at-scale programs.

Albornoz   F. , Anauati   M. V. , Furman   M. , Luzuriaga   M. , Podestá   M. E. , Taylor   I. . 2018. “ Training to Teach Science: Experimental Evidence from Argentina .” Policy Research Working Paper 8594 , World Bank, Washington, DC .

Angrist   J. D. , Lavy   V. . 2001 . “ Does Teacher Training Affect Pupil Learning? Evidence from Matched Comparisons in Jerusalem Public Schools .” Journal of Labor Economics   19 ( 2 ): 343 – 69 .

Google Scholar

Araujo   M. C. , Carneiro   P. , Cruz-Aguayo   Y. , Schady   N. . 2016 . “ Teacher Quality and Learning Outcomes in Kindergarten .” Quarterly Journal of Economics   131 ( 3 ): 1415 – 53 .

Baker   S. , Smith   S. . 1999 . “ Starting off on the Right Foot: The Influence of Four Principles of Professional Development in Improving Literacy Instruction in Two Kindergarten Programs .” Learning Disabilities Research & Practice   14 ( 4 ): 239 – 53 .

Banerjee   A. , Cole   S. , Duflo   E. , Linden   L. L. . 2007 . “ Remedying Education: Evidence from Two Randomized Experiments in India .” Quarterly Journal of Economics   122 ( 3 ): 1235 – 64 .

Berlinski   S. , Busso   M. . 2017 . “ Challenges in Educational Reform: An Experiment on Active Learning in Mathematics .” Economics Letters   156 : 172 – 5 .

Bold   T. , Filmer   D. , Martin   G. , Molina   E. , Rockmore   C. , Stacy   B. , Svensson   J. , Wane   W. . 2017 . “ What Do Teachers Know and Do? Does It Matter? Evidence from Primary Schools in Africa .” Policy Research Working Paper 7956 , World Bank, Washington, DC .

Borenstein   M. , Hedges   L. V. , Higgins   J. P. T. , Rothstein   H. R. . 2009 . Introduction to Meta-Analysis .   Chichester, United Kingdom : John Wiley & Sons .

Google Preview

Borko   H.   2004 . “ Professional Development and Teacher Learning: Mapping the Terrain .” Educational Researcher   33 ( 8 ): 3 – 15 .

Bourgeois   E. , Nizet   J. . 1997 . Aprendizaje y formación de personas adultas .   Paris, France : Presses Universite de France .

Cardemil   C.   2001 . “ Procesos y condiciones en el aprendizaje de adultos .” Jornada Nacional de Supervisores. Supervisión para aprendizajes de calidad y oportunidades para todos. Educación Rural .   Santiago : Ministerio de Educación . https://repositorio.uahurtado.cl/handle/11242/8517 .

Chetty   R. , Friedman   J. N. , Rockoff   J. E. . 2014 . “ Measuring the Impacts of Teachers II: Teacher Value-Added and Student Outcomes in Adulthood .” American Economic Review   104 ( 9 ): 2633 – 79 .

Cilliers   J. , Fleisch   B. , Kotze   J. , Mohohlwane   M. , Taylor.   S.   2020 . “ The Challenge of Sustaining Effective Teaching: Spillovers, Fade-out, and the Cost-effectiveness of Teacher Development Programs .” Unpublished Working Paper . https://www.dropbox.com/s/6xmv7283oxoysj2/The%20Challenge%20of%20Sustaining%20Effective%20Teaching%20with%20appendix.pdf?dl=0 .

Darling-Hammond   L. , Wei   R. C. , Andree   A. , Richardson   N. , Orphanos   S. . 2009 . Professional Learning in the Learning Profession .   Washington, DC : National Staff Development Council . https://edpolicy.stanford.edu/sites/default/files/publications/professional-learning-learning-profession-status-report-teacher-development-us-and-abroad.pdf .

Desimone   L. M.   2009 . “ Improving Impact Studies of Teachers’ Professional Development: Toward Better Conceptualizations and Measures .” Educational Researcher   38 ( 3 ): 181 – 99 .

Duflo   A. , Kiessel   J. , Lucas   A. . 2020 . “ External Validity: Four Models of Improving Student Achievement .” Working Paper No. w27298 , National Bureau of Economic Research , Cambridge, MA .

Evans   D. K. , Popova   A. . 2016a . “ What Really Works to Improve Learning in Developing Countries? An Analysis of Divergent Findings in Systematic Reviews .” World Bank Research Observer   31 ( 3 ): 242 – 70 .

Evans   D. K. , Popova   A. . 2016b . “ Cost-Effectiveness Analysis in Development: Accounting for Local Costs and Noisy Impacts .” World Development   77 : 262 – 76 .

Evans   D. K. , Yuan   F. . 2020 . “ How Big are Effect Sizes in International Education Studies? ” Working Paper 545 , Center for Global Development , Washington, DC .

Fisher   R. A.   1925 . Statistical Methods for Research Workers , first edition. Edinburgh : Oliver and Boyd Ltd .

Fisher   R. A.   1935 . The Design of Experiments ,  sixth edition. Edinburgh : Oliver and Boyd, Ltd , 1951 .

Fryer, Jr   R. G.   2017 . “ The Production of Human Capital in Developed Countries: Evidence from 196 Randomized Field Experiments .” Handbook of Economic Field Experiments   2 : 95 – 322 .

Gersten   R. , Taylor   M. J. , Keys   T. D. , Rolfhus   E. , Newman-Gonchar   R. . 2014 . Summary of Research on the Effectiveness of Math Professional Development Approaches .   Washington, DC : Institute of Education Sciences, National Center for Education Evaluation and Regional Assistance, U.S. Department of Education; and Regional Educational Laboratory Southeast at Florida State University . http://files.eric.ed.gov/fulltext/ED544681.pdf .

Gove   A. , Poole   M. K. , Piper   B. . 2017 . “ Designing for Scale: Reflections on Rolling Out Reading Improvement in Kenya and Liberia .” New Directions for Child and Adolescent Development   2017 ( 155 ): 77 – 95 .

Hedges   L.V . 2009 . “ Effect Sizes in Nested Designs .” In The Handbook of Research Synthesis and Meta-analysis , edited by Cooper   H. , Hedges   L. V. , Valentine,   J. C.   337 – 56 . New York, NY : Russell Sage Foundation .

Huberman   M.   1989 . “ The Professional Life Cycle of Teachers .” Teachers College Record   91 ( 1 ): 31 – 57 .

Imbens   G. W. , Rubin   D. B. . 2015 . Causal Inference in Statistics, Social, and Biomedical Sciences .   Cambridge, UK: Cambridge University Press .

Kennedy   M. M . 2019 . “ How We Learn About Teacher Learning .” Review of Research in Education   43 ( 1 ): 138 – 62 .

Kerwin   J. T. , Thornton   R. L. . 2021 . “ Making the Grade: The Sensitivity of Education Program Effectiveness to Input Choices and Outcome Measures .” Review of Economics and Statistics   103 ( 2 ): 251 – 64 .

Knowles   M. S. , Holton   E. F. , Swanson   R. A. . 2005 . The Adult Learner , sixth edition.   Burlington, MA : Elsevier .

Kraft   M. A.   2020 . “ Interpreting Effect Sizes of Education Interventions .” Educational Researcher   49 ( 4 ): 241 – 53 .

Kraft   M. A. , Papay   J. P. . 2014 . “ Can Professional Environments in Schools Promote Teacher Development? Explaining Heterogeneity in Returns to Teaching Experience .” Educational Evaluation and Policy Analysis   36 ( 4 ): 476 – 500 .

La Paro   K. M. , Pianta   R. C. . 2003 . CLASS: Classroom Assessment Scoring System . Charlottesville, VA : University of Virginia .

Leonard   K. L.   2008 . “ Is Patient Satisfaction Sensitive to Changes in the Quality of Care? An Exploitation of the Hawthorne Effect .” Journal of Health Economics   27 ( 2 ): 444 – 59 .

Leonard   K. L. , Masatu   M. C. . 2010 . “ Using the Hawthorne Effect to Examine the Gap Between a Doctor's Best Possible Practice and Actual Performance .” Journal of Development Economics   93 ( 2 ): 226 – 34 .

Loyalka   P. , Popova   A. , Li   G. , Shi   Z. . 2019 . “ Does Teacher Training Actually Work? Evidence from a Large-Scale Randomized Evaluation of a National Teacher Training Program .” American Economic Journal: Applied Economics   11 ( 3 ): 128 – 54 .

McEwan   P.   2015 . “ Improving Learning in Primary Schools of Developing Countries: A Meta-analysis of Randomized Experiments .” Review of Educational Research   85 ( 3 ): 353 – 94 .

Molina   E. , Fatima   S. F. , Ho   A. , Hurtado   C. M. , Wilichowski   T. , Pushparatnam   A. . 2018 . “ Measuring Teaching Practices at Scale: Results from the Development and Validation of the Teach Classroom Observation Tool .” Policy Research Working Paper 8653 , World Bank , Washington, DC .

Muralidharan   K. , Das   J. , Holla   A. , Mohpal   A. . 2017 . “ The Fiscal Cost of Weak Governance: Evidence from Teacher Absence in India .” Journal of Public Economics   145 : 116 – 35 .

Papay   J. P. , Taylor   E. S. , Tyler   J. H. , Laski   M. E. . 2020 . “ Learning Job Skills from Colleagues at Work: Evidence from a Field Experiment Using Teacher Performance Data .” American Economic Journal: Economic Policy   12 ( 1 ): 359 – 88 .

Piper   B. , Korda   M. . 2011 . EGRA Plus: Liberia (Program evaluation report) . Durham, NC : RTI International .

Rogers   H. , Demas   A. . 2013 . The What, Why, and How of the Systems Approach for Better Education Results (SABER) . Washington, DC: World Bank . http://wbgfiles.worldbank.org/documents/hdn/ed/saber/supporting_doc/Background/SABER_Overview_Paper.pdf .

Shulman   L. S.   1986 . “ Those Who Understand: Knowledge Growth in Teaching .” Educational Researcher   15 ( 2 ): 4 – 14 .

TNTP . 2015 . The Mirage: Confronting the Hard Truth about Our Quest for Teacher Development . The New Teacher Project. http://files.eric.ed.gov/fulltext/ED558206.pdf .

Villegas-Reimers   E.   2003 . Teacher Professional Development: An International Review of the Literature . Paris : UNESCO International Institute for Educational Planning . http://www.iiep.unesco.org/en/publication/teacher-professional-development-international-review-literature .

Walter   C. , Briggs   J. . 2012 . What Professional Development Makes the Most Difference to Teachers .   Oxford : University of Oxford Department of Education . https://www.oupjapan.co.jp/sites/default/files/contents/events/od2018/media/od18_Walter_reference.pdf .

Wenglinsky   H.   2000 . “ How Teaching Matters: Bringing the Classroom Back into Discussions of Teacher Quality .” Policy Information Center Report , Educational Testing Service (ETS) .

Wiley   D. , Yoon   B. . 1995 . “ Teacher Reports of Opportunity to Learn: Analyses of the 1993 California Learning Assessment System .” Educational Evaluation and Policy Analysis   17 ( 3 ): 355 – 70 .

Wood   F. H. , McQuarrie   F. Jr.   1999 . “On the Job Learning. New Approaches will Shape Professional Learning in the 21st Century .” Journal of Staff Development   20 : 10 – 13 .

Yoon   K. S. , Duncan   T. , Lee   S. W. Y. , Scarloss   B. , Shapley   K. . 2007 . Reviewing the Evidence on how Teacher Professional Development Affects Student Achievement (Issues & Answers Report No. 033) . Washington, DC : Institute of Education Sciences, National Center for Education Evaluation and Regional Assistance, U.S. Department of Education; and Regional Educational Laboratory Southeast at Florida State University . http://files.eric.ed.gov/fulltext/ED498548.pdf .

Supplementary data

Email alerts

Citing articles via.

• Recommend to your Library

Affiliations

• Online ISSN 1564-6971
• Print ISSN 0257-3032
• Copyright © 2024 World Bank
• About Oxford Academic
• Publish journals with us
• University press partners
• What we publish
• New features  
• Open access
• Institutional account management
• Rights and permissions
• Get help with access
• Accessibility
• Advertising
• Media enquiries
• Oxford University Press
• Oxford Languages
• University of Oxford

Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide

• Copyright © 2024 Oxford University Press
• Cookie settings
• Cookie policy
• Privacy policy
• Legal notice

This Feature Is Available To Subscribers Only

Sign In or Create an Account

This PDF is available to Subscribers Only

For full access to this pdf, sign in to an existing account, or purchase an annual subscription.

Shifting the focus of research on effective professional development: Insights from a case study of implementation

• Open access
• Published: 29 October 2021
• Volume 24 , pages 345–363, ( 2023 )

Cite this article

You have full access to this open access article

• Sally Patfield   ORCID: orcid.org/0000-0002-9591-7676 1 ,
• Jennifer Gore   ORCID: orcid.org/0000-0002-7309-5405 1 &
• Jess Harris   ORCID: orcid.org/0000-0003-4584-6993 1  

11k Accesses

11 Citations

8 Altmetric

Explore all metrics

Globally, teacher professional development is heralded as a key mechanism for educational reform. With governments investing heavily in PD programs, the aim of these interventions is not only enhanced teacher knowledge and practice but, ultimately, improved student outcomes. A substantial body of research has attempted to identify characteristics of effective PD, generating a growing list of features that ostensibly ‘work’. As such, program design has become the dominant analytic focus. In this paper, we shift attention squarely to program implementation as necessary in conceptualising and evaluating effective PD. We apply the lens of implementation science to a case study of how one regional secondary school in NSW, Australia, implemented a robust PD program called Quality Teaching Rounds that has strong evidence of effectiveness. Despite the school’s attempts to remain true to the spirit of the PD, a combination of remoteness, lack of casual relief teachers, high teacher turnover, and negative perceptions of peer observation result in a form of QTR that is almost unrecognisable from its intended design. We argue greater attention must be given to understanding and supporting successful implementation within and across diverse school contexts in order to take effective forms of PD to scale.

Similar content being viewed by others

The Role of Professional Learning Communities in Successful Response to Intervention Implementation

Taking Change to Scale: Lessons from the Jika iMfundo Campaign for Overcoming Inequalities in Schools

The school-wide pedagogical framework at an Australian primary school: experiences and perspectives of teachers

Explore related subjects.

• Artificial Intelligence

Avoid common mistakes on your manuscript.

Introduction

Globally, policymakers and governments have set ambitious targets for educational reform. While improvement agendas vary widely across nations and jurisdictions, two key commonalities are embedded within most large-scale reform efforts. First, improving student outcomes is positioned as an important goal, spurred on by both large-scale international assessments that underpin global comparisons of performance and social justice imperatives to alleviate disparities in achievement (Meissel et al., 2016 ). Second, teachers are unequivocally positioned as fundamental to—even inseparable from—reform. Teachers are crucial ‘enactors’ of educational policy (Ball et al., 2012 ) and, ultimately, the facilitators of any changes to classroom practice (Borko, 2004 ; OECD, 2019 ).

Accordingly, we have seen significant investment in teacher professional development (PD), heralded as a key catalyst for improving student outcomes. PD is now ‘big business’ (Hill, 2009 ), with governments and educational jurisdictions investing heavily in a host of initiatives and interventions, varying in scope and content (OECD, 2019 ). While a wide range of learning experiences fall under the umbrella of ‘PD’ (Hill et al., 2013 ; OECD, 2019 ; Wei et al., 2009 ), scholarly attention has increasingly been directed at ‘effective PD’—“structured professional learning that results in changes in teacher practices and improvements in student learning outcomes” (Darling-Hammond et al., 2017 , p. v). As such, it is now commonplace for the ‘final test’ of PD to be whether or not an intervention leads to better academic outcomes for students—not just teachers’ knowledge, skills or pedagogy (Darling-Hammond et al., 2017 ; Desimone, 2011 ).

In this paper, our focus is ‘effective PD’, as distinct from the broader spectrum of activity conceptualised as ‘teacher PD’ or ‘professional learning’ (PL). In light of the current climate of reform and desire for strong return on investment by governments (Gore et al., in press), research on effective PD has flourished over the past decade, triggering a methodological shift from small-scale studies using teacher self-reports of satisfaction and change, to experimental designs measuring student outcomes (Hill et al., 2013 ). Despite this agenda, however, effective PD—as measured by student achievement—remains somewhat elusive, with many studies failing to demonstrate positive gains in academic outcomes and/or criticised for lacking scientific rigour (Borko et al., 2010 ; Yoon et al., 2007 ).

In order to better understand effective PD, the dominant line of enquiry focuses on program design (Darling-Hammond et al., 2017 ; Hill et al., 2013 ). Such studies seek to identify the features of PD initiatives associated with positive gains in teacher knowledge and practice and, most importantly, student outcomes. These features include: a focus on discipline-specific content knowledge and pedagogy; sustained duration; coaching; collaboration; opportunities for feedback and reflection; and active learning (Darling-Hammond et al., 2017 ; Desimone, 2009 , 2011 ; Garet et al., 2001 ). This area of research has risen in prominence to such an extent that some scholars refer to an informal consensus on the core characteristics of PD that ‘work’ (Desimone, 2009 , 2011 ), or what others describe as a ‘new orthodoxy’ grounded in the view that for PD to be effective, these specific features must be included (Gore et al., in press).

We contend, however, that this consensus is problematic for a variety of reasons, not least because of the weak evidence—often based more on conjecture than empirical evidence—underpinning its claims (Gore et al., in press; Sims & Fletcher-Wood, 2021 ). Indeed, even when studies have used rigorous randomised controlled trial (RCT) designs, PD encompassing many of these features has rarely shown success in improving student outcomes (Gore et al., 2021 ; Hill et al., 2013 ; Yoon et al., 2007 ). Effective PD also often works as a ‘package,’ such that it is difficult to isolate which specific design features are important when an intervention is successful, or how particular features work together to engender positive outcomes (Hill et al., 2013 ; Opfer & Pedder, 2011 ).

Furthermore, the so-called consensus has not attended carefully to context, instead bringing together characteristics of interventions that have worked ‘somewhere’ for ‘someone’ (Bryk, 2015 ). This kind of generality does little to illuminate how effective forms of PD will translate into outcomes across diverse school communities and student populations, and contexts with different political, social, cultural and material elements (Ball et al., 2012 ). The importance of context is already well-established in many comparable fields of research, including policy enactment (Ball et al., 2012 ), school reform (Datnow et al., 2002 ) and the use of professional learning communities for improvement (Wenger, 1998 ). It is surprising, then, that research on effective PD has largely ignored the context of implementation, giving much greater credence to program design. As a result, little analytic attention has been paid to examining how effective PD can be implemented across diverse settings (Borko, 2004 ; Borko et al., 2010 ) and how, therefore, implementation might be conceptualised and evaluated across sites.

This paper offers precisely this kind of analysis through a case study of the implementation of one form of rigorously-tested, effective PD called Quality Teaching Rounds (QTR). Under RCT conditions, QTR has already produced significant positive effects for both teachers and students, including notable increases in teacher morale, teaching quality, and student academic achievement (Gore et al., 2017 ; Gore et al., 2021 ). However, less is known about how to support high-quality implementation in diverse contexts outside of research settings or how to support different kinds of school communities to successfully implement QTR.

With a focus on depth and particularity rather than breadth, we adopt a case study approach to examine the implementation of QTR in one school community—Olsen Valley High School (pseudonym)—located in the state of New South Wales, Australia. The analysis is anchored in ‘implementation science’, an approach that is embedded in clinical, health and community-based research (Moir, 2018 ) but a relatively recent phenomenon in education (Centre for Evidence & Implementation, 2017 ). Specifically, we draw on Proctor et al.′s ( 2011 ) heuristic of eight implementation outcomes—acceptability, adoption, appropriateness, feasibility, fidelity, cost, penetration, and sustainability—to conceptualise how QTR was implemented at Olsen Valley High and consider the merits of drawing on implementation science to evaluate the implementation of effective PD more broadly. We begin with a brief discussion of implementation science, its value to the study of effective PD and the specifics of Proctor’s heuristic. Next, we present an overview of QTR, the intervention that forms the basis of the paper.

Implementation science and effective PD

Implementation science has been defined as the “scientific study of methods to promote the systematic uptake of research findings and other evidence-based practices into routine practice” (Eccles & Mittman, 2006 , para 2). It draws attention to the temporal gap between research and practice, with its focus on how evidence-based interventions are adopted and sustained in real-world contexts (Bauer et al., 2015 ; Thomas et al., 2017 ). Applied to the field of effective PD, we can ask how are evidence-based PD interventions implemented within schools and systems and, thus, how can they be implemented to increase their effectiveness and maximise outcomes (Kelly, 2012 )?

In contrast to research that synthesises and generalises core program features of effective PD, implementation science is fundamentally concerned with the specificity of context. That is, the implementation of an intervention is seen to be entwined with the unique set of circumstances associated with where—and even when—it takes place (Damschroder et al., 2009 ; Kelly, 2012 ). In this way, context is positioned as active in shaping both program implementation and program outcomes; it is much more than just a passive backdrop for an intervention (Datnow et al., 2002 ). Although context is defined in varying ways within implementation science theories and frameworks, the ideas of ‘outer’ and ‘inner’ setting (Damschroder et al., 2009 ) are particularly applicable to school contexts. The outer setting encompasses the economic, political, social, and cultural climate in which a school is situated while the inner setting focuses attention on the characteristics of the school itself (Damschroder et al., 2009 ). The distinction between these two layers is somewhat arbitrary, however, as their interaction is often dynamic, permeable, and reciprocal.

In this light, the implementation of any intervention can be thought of as the “product of the context in which it is implemented” (The Design-Based Research Collective, 2003 , p. 5). Consideration of context is not new in education (see, for example, Ball et al., 2012 ; Datnow et al., 2002 ). However, the systematic application of principles of implementation science to the investigation of implementation quality is in its infancy. The potential value of applying such a lens in studies of effective PD lies in the notion that positive gains in student outcomes are related to both the quality of an intervention and the quality of its implementation (Centre for Evidence & Implementation, 2017 ). Increasing pressure on schools to evaluate PD drawing on sophisticated standards of evidence (Desimone, 2011 ) makes such analysis timely.

Proctor et al.′s ( 2011 ) heuristic of eight implementation outcomes, derived from a major synthesis of implementation literature, is useful in conceptualising and evaluating implementation efforts. Table 1 sets out these outcomes as an overarching framework and set of concepts to guide implementation science research. While the heuristic is derived primarily from health and behavioural sciences research, we find it to be a valuable starting point for studying the implementation of effective PD, especially in the absence of a robust field of implementation science research in education more generally (Centre for Evidence & Implementation, 2017 ) and the lack of attention given to implementation within effective PD specifically (Hill et al., 2013 ).

By and large, Proctor et al. ( 2011 ) argue that these implementation outcomes represent the effects of deliberate and purposive actions to implement an intervention. In this way, they can be thought of as ‘preconditions’ for attaining the desired changes brought about by a specific intervention, such as positive gains in student achievement in the case of effective PD. As such, implementation outcomes can be seen as impacting other kinds of outcomes, although they are not interchangeable.

While Proctor et al. ( 2011 ) suggest that the eight implementation outcomes are discrete conceptual categories, we note a degree of overlap and correspondence between many of them. For example, the perceived acceptability of an intervention among stakeholders is closely related to its perceived appropriateness for the setting. Likewise, penetration and sustainability both relate to the integration of an intervention in a particular setting, although sustainability is generally observed further along—or even after—the implementation process.

Implementation must therefore be viewed as a process (Proctor et al., 2011 ), one that involves a sequence of interrelated activities over time. Indeed, in practice, each outcome is interconnected in complex and dynamic ways, such that one aspect can influence most, if not all, of the other outcomes. In our paper, we use Proctor et al.′s ( 2011 ) heuristic to examine the different phases of QTR implementation, from the initial decision to implement QTR, through to ongoing implementation efforts within the school and attempts to incorporate QTR as part of normal routine.

When an intervention is unsuccessful in practice, policy actors often move to the next idea, the next ‘fad’ or the next reform initiative that can be transplanted from ‘somewhere else’ (Bryk, 2015 ; Datnow et al., 2002 ). Instead, we take the view that developing a more robust understanding of effective PD necessitates a dual focus on both program features and program implementation.

The intervention: Quality Teaching Rounds

Quality Teaching Rounds (QTR) is a rigorously researched approach to PD that has been widely used in New South Wales, Australia, and increasingly adopted in other state educational jurisdictions. At its core, QTR is underpinned by four interrelated components. First, it is collaborative, with teachers working in professional learning communities (PLCs) to observe, analyse, and discuss one another’s practice. Teachers in a PLC can come from any Year level, teaching specialisation, or career stage (Gore & Rickards, 2020a ; Gore & Rosser, 2020b ). Second, it is an approach to teaching rounds (City et al., 2009 ; Elmore, 2007 )—similar to the idea of medical rounds—which supports teachers to discuss and develop a shared understanding of ‘good teaching’. The goal is instructional improvement guided by teachers, rather than an external facilitator. Third, it uses a pedagogical framework, the Quality Teaching (QT) Model, which scaffolds the Rounds process. The QT Model provides a comprehensive set of concepts and associated language for deep, professional conversations about teaching practice (Bowe & Gore, 2017 ; Gore et al., 2017 ; Gore et al., 2021 ). And fourth, it is underpinned by a set of protocols that have been designed to address power relations among teachers (Gore et al., 2021 ), encouraging full participation, turn-taking and confidentiality by members of the PLC.

Logistically, QTR usually consists of four ‘Rounds’, with each Round taking place over a single day. Each Round begins with discussion of a chosen professional reading. The aim of this initial session is to support teachers to engage in professional conversation and build a sense of community within their PLC. Next, a full lesson is taught by one member of the PLC and observed by all others. Each member is required to be fully present throughout the Rounds process, and have a lesson observed on a rotational basis over the course of the Rounds. After each observation, lesson coding occurs. Each teacher in the PLC (including the ‘host’ teacher) codes the lesson individually using the QT Model, which consists of three overarching dimensions and 18 elements (see Table 2 ). To conclude the day, the PLC members discuss the observed lesson, and pedagogy more broadly, drawing on the language, concepts, and structure of the QT Model. The purpose of this final session is to support meaningful analysis of practice, which is achieved through a process whereby teachers discuss their codes and associated evidence, and try to come to agreement as a group about the appropriate code for each element of the QT Model. These codes remain confidential within the PLC and are less important than the rich conversation generated about practice.

Research design

Before commencing QTR in their schools, at least two teachers from each school attend a two-day workshop designed to support implementation. In 2019, 687 teachers who participated in a QTR workshop between July 2014 and May 2018 were sent an email by the project team which included a link to a short, online questionnaire, administered via SurveyMonkey. The questionnaire included a series of questions designed to ascertain if, and how, QTR was implemented in their schools after attending the workshop.

Overall, 177 survey responses were received from teachers at 81 schools. From this pool of responses, schools were categorised into three implementation categories: ‘QTR embedded’, where QTR was embedded throughout school processes after attendance at a workshop; ‘QTR introduced’, where QTR was implemented for some or many staff at a school, but was not yet embedded in school planning and processes; and ‘QTR discontinued’, where QTR had been implemented but discontinued at a school. Principals from each school were subsequently sent an email inviting them to participate in a study examining the implementation and sustainability of QTR, with the aim of recruiting schools from each of these three implementation categories. Six schools were recruited (two from each category), however, the two schools where QTR had been discontinued withdrew after providing organisational consent. While there are lessons to be learned from termination or suspension of an intervention and we remain interested in studying such schools as part of our broader research agenda, for this analysis, our primary interest was schools that adopted QTR.

Data collection and analysis were informed by case study methodology, aiming for richness and depth rather than breadth (Yin, 2013 ). Given the nature of the research, the unit of analysis was the school, with data collected from multiple participants at each site for the purposes of triangulation (Yin, 2013 ). All teachers within a school were invited to participate, with written consent provided by participants at the time of interview. Two researchers visited each school in late 2019 and conducted interviews with the principal and a sub-sample of volunteer teachers available on the scheduled interview dates. Interviews were semi-structured and focused on: experiences of QTR; enablers and barriers to implementation; adaptations (if any); overall impressions; and perceived impact. Interviews were audio-recorded and lasted approximately 60 minutes. Schools and participants were allocated pseudonyms to protect anonymity. Transcripts were coded using the NVivo 12 software program, drawing on a two-step case-oriented approach to analysis (Yin, 2013 ): (1) open coding, where a line-by-line reading of each transcript was undertaken to define and develop categories or ‘nodes’; and (2) abstraction and interpretation, where nodes were grouped, and subsequently reduced, at higher levels of meaning.

This paper focuses on one school community only, Olsen Valley High School, where interviews were conducted with the Principal and eight teachers from a wide array of subject specialisations. Olsen Valley High represents an example of ‘QTR embedded’, highlighting a degree of sustainability. We selected it for this analysis, however, because it represents an ‘extreme case’ (Jahnukainen, 2009 ) as a school that has substantially modified aspects of QTR against recommended implementation, providing a powerful opportunity to consider the implementation of effective PD more broadly. Our case study explores potential benefits of using implementation science for evaluating and subsequently enhancing implementation, taking into account the nuances and complexities of context.

Adopting QTR: Perceptions of acceptability and appropriateness

Olsen Valley High School is a comprehensive, co-educational secondary school situated in the inland community of Olsen Valley, a regional township in NSW. Located a vast distance from major metropolitan centres, the community is geographically isolated and predominantly surrounded by desert. According to data from the Australian Bureau of Statistics, the median weekly family income is far less than the state’s average, similarly reflected in the relative socio-educational advantage of the school which is below the national mean. These characteristics are echoed in the responses of the teachers we interviewed, who describe the community as ‘remote’ and depict the student population as primarily from lower socio-economic backgrounds.

Aligning with Departmental priorities, Olsen Valley High School currently has three strategic directions centred around quality teaching, learning, and distributed instructional leadership. These goals are clearly stated in the recent school plan, which explicitly references QTR as a core whole-of-school mechanism to enhance both the quality of teaching and student outcomes. Although QTR is a relatively new practice in the school, peer observation has been a part of the school’s culture for many years, initially driven by an aim to deprivatise classroom practice:

Our current Deputy Principal, he led a team that we called the ‘lesson observation team’ and the aim of that team was to try and open the doors of classrooms because it kind of felt that teachers pretty much kept to themselves in their classrooms. And teaching being such a complex practice that nobody really went and watched anyone else teach, it was just you were teaching or you were madly preparing your stuff—you know—flat out, there was no time to go and watch someone else or take anything else in. (Rick)

Although geographically isolated, Rick emphasises the school’s attempt to overcome the professional isolation that can characterise any teaching context. The deliberate aim has been to interrupt teaching as a ‘private act’ (Cochran-Smith, 2015 )— teachers pretty much kept to themselves in their classrooms— by making teaching more public and open. This constitutes a shift in both culture and practice—the literal opening of classroom doors— by creating time and space for teachers to participate in peer observation, primarily driven at this point in time by the lesson observation team.

This effort began at Olsen Valley High with proformas to guide observation. After attending a QTR workshop, however, the leadership team became convinced that QTR would take them to a new level:

We started the lesson observation team purely with the intention to get teachers comfortable with being observed. So we had a few tools that we used that were more tick-box proformas, that they could say ‘yes that’s happening in the classroom’. It was all around rules and routines, praise and consequence and things like that. Then myself and another staff member went and did the training for QTR and came back to the school and sort of said ‘this is where we need to go. This is such a good model, we can really dive into this.’ (Jerry)

Jerry’s description of the original tick-box and yes/no observation tools used at Olsen Valley suggests a process underpinned by appraisal and judgement. Indeed, the original observations were about rules, routines, praise, and consequence , signalling how easily the deprivatisation of teaching can become a means of surveillance and accountability when executed without a broad understanding of the culture and ecology of a school (Charteris & Smardon, 2018 ; Cochran-Smith, 2015 ). By contrast, Jerry exalts QTR as offering greater depth— we can really dive into this —indicating a level of acceptability and appropriateness needed for this new approach to be adopted.

More specifically, QTR was perceived as offering the school community an explicit focus on teaching and learning. In discussing the impetus for initially adopting QTR at Olsen Valley High, Rick identifies two interrelated characteristics of QTR which he believes make it a powerful form of professional learning:

Well, it focused on teaching [and] it was a model that everybody could use that focused on improving teaching. So regardless of what level of experience… like I see the value in it and I’ve been teaching for 30 years. (Rick)

What stands out in Rick’s account is a strong belief that QTR focuses on the core business of schools and, therefore, is for everybody , thus adding to its acceptability and appropriateness. Observational frameworks are often subject-specific, as in mathematics, English/language arts, or science (Gore & Rosser, 2020b ; Kane et al., 2013 ), thus narrowing the pool of teachers able to work with, and learn from, their colleagues. However, QTR’s focus on pedagogy makes it appropriate for whole-of-school implementation. Furthermore, as Rick notes, it is relevant for both beginning and experienced teachers (Gore & Bowe, 2015 ; Gore & Rickards, 2020a ), providing an important foundation for garnering teacher buy-in to this form of PD.

Struggles with feasibility and fidelity

While QTR was perceived to be the right fit for Olsen Valley High, the school community immediately faced structural constraints that affected its feasibility and the degree to which it could be implemented with fidelity. Interestingly, several teachers used the phrase Rolls Royce to signal the logistical impossibilities of implementation created by the school’s context, particularly in terms of geographic isolation and the subsequent lack of casual relief teachers (CRTs) in the area:

The nature of being out here, with casual cover being non-existent, is that it’s very difficult to get the scale of what we wanted with that sort of ‘Rolls Royce’ model. So we were lucky enough that we already had scheduled, within our teaching load, one ‘professional learning’ period a cycle. So we were able to use that as a sort of trade-off with QTR, in that one of those periods was designated for you to go and observe a teacher and another one of those periods was designated for you to code that lesson and then on a Tuesday afternoon staff meeting was when we would come together to do that group coding. (Jerry)

The use of the term Rolls Royce positions QTR as a luxury; one that is elusive, unattainable, even an impossibility. By contrast, Jerry’s description of the Olsen Valley community emphasises a poverty of resources due to the tyranny of distance— being out here— especially the lack of CRTs ( non-existent ) which profoundly impacts implementation. Thus a compromise— trade-off —is made to balance the requirements of the PD with contextual limitations. Each Round is now conducted over a number of days (instead of during a single day), separating out the observation, individual coding, and group coding/discussion components of QTR, and removing the initial reading discussion altogether.

This substantial modification to QTR generated further implications for fidelity. To keep costs down and manage logistics, PLCs are formed based on practicality and convenience, with one teacher in each PLC being the ‘host’ (the observed teacher) of a given Round and the other teachers designated observers due to their professional development period (or ‘free period’) being timetabled at the same time. Unfortunately, this means that only one Round occurs per term and that a PLC only functions for this single Round:

[After completing one Round] our groups changed. And I didn’t realise the groups would change. Thinking the Executive went first—“Oh, that’s really good”. And then [thinking that] one of us will be next… But after [the Round] we were talking and they’re like, “Oh, you won’t get to see us, because we’ll be in a different group”… Personally, I like staying [in the PLC]. I just think it would be nice to see that Head Teacher that we watched. The purpose [is meant to be that] they went first. But then they didn’t see us in return….like you watch a lesson, and you come in, and you do your coding, and you do [the] group code, but then that’s it. (Holly)

Having participated in QTR at her previous school, Holly was very surprised to find PLCs at Olsen Valley High would not be sustained or reciprocal ( they didn’t see us in return ). Thus, the very basis of QTR—teaching rounds within PLCs—is interrupted (Gore et al., 2017 ); observation is not mutual, there is no time to develop a group identity ( …then that’s it ) and commitment to the PLC is limited ( because we’re in a different group ). These core elements of QTR, typically involving PLC members engaging in mutual observation and ongoing collaboration, were designed to flatten power hierarchies in observation and build a sense of community (Bowe & Gore, 2017 ). However, at Olsen Valley, Holly describes a QTR experience that is reduced to a simple, single lesson observation—just with a different conceptual lens from the proformas used previously at the school.

Another major adaptation has been the separation of the lesson observation from the individual coding; two components of QTR that are usually undertaken on the same day. Most teachers at Olsen Valley High have to return to their own classrooms straight after conducting an observation, creating a substantial time lag in the process:

It's recommended obviously to do it [the coding] straight away so it's fresh in the mind. At the same time we can't control everyone's free periods and give them two periods off or something to do it… [But] you don't want to leave it too long. And I've found that personally I have done that before and either forgotten about doing it or had a lesson straight after and then didn't have a free period until the next day or something like that. Then I found that quite difficult trying to remember what the lesson was about and code it properly. (Arnold)

Unlike more superficial forms of observation, QTR requires teachers to assign codes to elements of an observed lesson and note associated evidence as a means to collaboratively analyse and discuss practice. As such, being given the time to individually code a lesson is particularly important to facilitating QTR discussions. Importantly, the coding process is conceptualised as a means to an end; a scaffold to generate analytical dialogue (Bowe & Gore, 2017 ; Bowe, 2016 ) rather than a quantitative measure of teaching performance (Kane et al., 2013 ). The time gap between the observation and coding at Olsen Valley, however, is an imposed structural constraint ( we can’t control… ) which leads to teachers like Arnold forgetting to do the coding or finding it difficult to remember what the lesson was about , despite taking notes during the observation.

Similarly, the value of the group coding and discussion—the final component of QTR—also appears to be diminished. This process has been adapted and compressed into a regularly scheduled staff meeting at the end of each term:

They want it to be the hour length, but they were finding that you could never do all dimensions in that amount of time, or the 50-minutes length sorry, because that's the period, for 52 minutes. So yeah, they reduce the amount [of elements] that you do. They went through the school—“What's the most important ones? Well, the top row [see Table 1 ] is the most important for the school.” Then it's, "What do you want to benefit the most from?" And you're meant to pick the ones that you go, "Okay, this one I want to up the most." So, you'd never pick the one that you're going to score a ‘one’ that wasn't a part of the lesson. So, you're meant to pick the ones that you want to improve the most and the observers are then picking the ones that they think are interesting. (Pat)

The Quality Teaching Model that underpins QTR represents a holistic and comprehensive framework that is designed to honour the complexity of teaching (Gore, 2021 ; Gore et al., 2017 ). Yet Pat’s description of the modifications adopted at Olsen Valley High turns the elements comprising the Model into a set of choices, as emphasised by Pat’s repeated reference to picking the ones that will be discussed. Here the complex practice of teaching is reduced to a number of elements, rather than the sum of its parts. Although selection is based on teachers’ perceptions of importance , benefit, and needed improvement , they are unlikely to explore the multi-dimensional nature of teaching to the extent they do when all 18 elements of the Quality Teaching Model are addressed, again raising issues of implementation fidelity.

Moving forward: Penetration and sustainability

Unsurprisingly, it has been difficult for Olsen Valley High to build momentum integrating QTR into the school’s PD program. The school experiences very high levels of teacher turnover, and is seen by some long-term staff as a momentary stopover in a career:

The majority of our teachers are early career teachers in the first or second year of teaching… Because of the nature of the incentive transfer system people will come to Olsen Valley to get a permanent job—they will do their three years, and then they’ll transfer back to family on the coast… Unfortunately we kind of see ourselves as a bit of a factory for teachers in that we put lots into them, we produce teachers that go out to other areas of the State and they’re just really hitting their straps by the time they leave us. So yeah, but if that’s what we’re doing, that’s what we’re doing I suppose and then the next batch comes in. (Rick)

Rick’s factory metaphor highlights the extent of teacher attrition at Olsen Valley High: teachers usually arrive early in their careers, are shaped and fashioned through PD and other opportunities at the school, and then leave. The cycle continues as the next batch comes in . In NSW, graduates often wait years for a permanent teaching appointment, especially in coastal regions. Because of the government’s incentive transfer system that allots higher ‘transfer point ratings’ to harder-to-staff schools—usually in rural and remote areas, like Olsen Valley—these schools can represent an attractive stepping stone in securing a permanent job.

In some years, almost half of the school’s teaching staff have left at the end of the year to work elsewhere. This has meant an unrelenting process of inducting new staff, including almost having to start from scratch every year with QTR:

You can't create a small community because a small community is constantly changing. It might last for a year but every year there might be 17 teachers leaving. So, you can't get the strong group to stick together… Then it's just the whole re-educating people all the time on what you need to do because you've constantly got large numbers of new teachers all the time. So, every single year, it's like, "Okay, we're going to have to do training to refresh, but also we've got all these new teachers we've got to induct." (Pat)

In speaking about the staff culture at Olsen Valley High, most teachers raised the challenges posed by high attrition. Pat’s comments illustrate just how taxing it can be for a relatively small teaching community to lose staff—it is not only constantly changing but large numbers of teachers are leaving all the time— thus inhibiting the development of an ongoing community of practice around the experience of QTR. These circumstances signal an underlying difficulty in sustaining QTR in a school where it is hard to build a community of teachers who are invested, support each other and stick together .

While the school has mechanisms in place to induct new staff into QTR, the high turnover also brings the related challenge of overcoming pre-existing negative attitudes about lesson observation:

Mainly with new staff there is still that stigma about ‘is this performance-based?’ And that’s probably the first thing that we do every year is to try and break down those stigmas that “hey, this is not a performance-based thing. There’s no one judging you as a teacher” because everyone has their own opinions about what a quality teacher is. But this gives us a really good framework to look at what quality teaching is. So there’s so much more to being a teacher than just being in front of a class and what’s going on in the classroom. So by no means are we saying “this is you as a teacher”. It’s all about the teach ing . (Jerry)

Lesson observation has increasingly been embraced in Australian classrooms as a means to improve practice. Globally, however, observation is used for both ‘low-stakes’ purposes—such as self-reflection and formative feedback— and high-stakes purposes—namely, decisions about remuneration, tenure, and dismissal (Cohen & Goldhaber, 2016 ). In an era of accountability, where teachers are often positioned as ‘performance workers’ (Ball, 2003 ), it is not surprising that some are wary of observation. However, as Jerry eloquently explains, QTR is the antithesis of this view—it focuses on teaching , not teachers —which is why it was initially viewed as having a high level of appropriateness for the school. Indeed, in this light, Jerry makes an important distinction between teaching as a practice ( it’s all about the teaching ) and the traits of individual teachers ( by no means we are saying… this is you as a teacher ). But with such high turnover rates, there is an added layer of effort to constantly have to remove the stigma attached to lesson observation more broadly.

More recently, the sustainability of QTR has also been hindered by the current political environment in the local community, when the union intervened at another local school where teachers made complaints about observation more generally. Ultimately, this set of circumstances has triggered further adaptations to how QTR is implemented at Olsen Valley:

This year it’s been really different because we’ve had the same teachers observed twice. Because there hasn’t been as many people put their hand up. I’m not sure if that’s representative of—we’ve got a lot of new teachers who are sort of like, “we don’t want to be observed yet, we don’t really understand the process”. Particularly that’s what happened in our KLA [Key Learning Area]. I sort of said to our new staff member, “do you want to get observed?” and he’s like “not yet because I don’t understand what this is”. (Racquel)

Unfortunately, that’s where I’m not happy with it because, at the moment, we have the same sort of ten to fifteen staff members volunteering each term. And in my point of view, that’s not how it should be run. Everyone should be having a turn at hosting… To me that’s not the ideology of the process. You should be hosting a Round if you’re going and watching other people. You should be comfortable enough to have them come and watch you as well. (Jerry)

With the same group of teachers now repeatedly observed by their colleagues, there is an imbalance in the way QTR operates at Olsen Valley, affecting both feasibility and fidelity. This disparity revolves around a new opt-in process, where teachers must now come forward and put their hand up to be observed, rather than everyone taking a turn within a four-person PLC. Jerry understands that this approach conflicts with a key premise of QTR, which is the need to build reciprocity and trust (Bowe & Gore, 2017 ; Gore et al., 2017 ); you should be hosting a Round if you’re going and watching other people . However, when coupled with high teacher turnover, and hence fewer teachers to pass on their positive experience, new teachers are understandably hesitant to volunteer to host a Round. They are unfamiliar with QTR ( we don’t really understand the process ) and cautious about being observed by strangers soon after starting at the school. Although the school leadership team is unhappy with this imposed adaptation, they are doing what they can to keep QTR running, given their perception of its value as a form of professional development.

This paper sought to go beyond broad generalisations about effective PD by shifting the focus to program implementation. In the current climate of educational reform, the key measure of an intervention’s success is increasingly the oft-elusive goal of academic achievement; that is, positively influencing student outcomes (Darling-Hammond et al., 2017 ; Gore et al., in press; Hill et al., 2013 ). To date, however, the long list of program features advocated as central planks to ‘best practice’ (Darling-Hammond et al., 2017 ; Desimone, 2011 ) has largely overshadowed an analytic focus on what happens at the point of PD implementation. As the case study presented in this paper illustrates, even effective and robust forms of PD will not necessarily translate into effective implementation.

Appying the lens of implementation science—specifically Proctor et al.'s ( 2011 ) heuristic of implementation outcomes—highlights both the possibilites and constraints of translating effective PD like QTR across diverse contexts. The initial adoption of QTR at Olsen Valley High was underpinned by the best of intentions—to deprivatise classroom practice using a rigorous observational framework. Indeed, QTR was perceived to be both acceptable and appropriate among staff and leaders at the school, particularly when compared to the observational tools previously used. However, the combination of remoteness, lack of CRTs in the area, high teacher turnover, and negative perceptions of lesson observation (both locally and more broadly within the teaching profession) had major repercussions for all other implementation outcomes (Proctor et al., 2011 )—feasibility, fidelity, cost, penetration, and sustainability—ultimately resulting in a form of QTR that is almost unrecognisable from its intended design.

Adaptation to create a better fit between an intervention and local conditions is a widely acknowledged need of educational reform at scale (Borko, 2004 ; Datnow et al., 2002 ; Quinn & Kim, 2017 ). However, extreme variation in implementation, such as occurred at Olsen Valley, highlights how program integrity can be lost through modifications to, and removal of, core components. First, the reading discussion was removed, thus limiting the building of community through shared engagement in professional discussion of ideas. Second, each Round took place over a number of days—often weeks—rather than a single day, losing coherence in the process of observing, coding, and discussing a lesson. Third, PLCs were not sustained for a set of Rounds (typically four days spread over a period of weeks), instead operating fleetingly based on teaching schedules and availability, thereby limiting the building of trust among participants and reciprocity that comes from observing and analysing each other’s teaching. Finally, the coding discussion was both compressed in terms of time allocated and reductionist in the selection of elements, losing adherence to the protocol of addressing all dimensions and elements in the Quality Teaching Model. This adaptation limits the deep professional learning that comes from comprehensive analysis and discussion of teaching practice.

While it might therefore be easy to characterise this case study as an ‘implementation failure’ (Thomson, 2014 ), we argue that the lens of implementation science helps to understand the situation differently. Thomson ( 2014 ) argues that ‘failure’ in educational reform is too often attributed to those involved in the implementation—teachers, leaders, schools—or to the context itself, which is blamed for posing too many difficulties. It is certainly true that the structural limitations faced by Olsen Valley have had serious consequences for the uptake and sustainability of QTR. However, we neither see the environment as ‘too difficult’ for implementation nor do we criticise the people involved. Instead, implementation science offers a framework to systematically assess the outcomes of implementation and identify what is needed to enhance the effectiveness of PD.

When implemented with fidelity, QTR has wide-ranging benefits for both teachers and students (Bowe & Gore, 2017 ; Gore et al., 2017 ; Gore et al., 2021 ; Gore & Rickards., 2020a ; Gore & Rosser., 2020b ). We ask, therefore, how can these benefits be realised in school communities like Olsen Valley? One possibility is ‘QTR Digital’, a modified version of QTR recently trialled specifically for regional and remote contexts, utilising digital technologies to support implementation. This form of QTR is comprised of the same core features, but with a few critical changes to support uptake and sustainability. In particular, teachers digitally video record a lesson to be observed by the other members of their PLC—rather than the observation occurring face-to-face—supporting implementation in schools that face issues with securing CRTs and/or facilitating teacher release. Teachers can also form PLCs either within or across schools, which further supports small schools and schools situated in remote areas that struggle to release four teachers for a full set of Rounds.

At a policy level, the recent announcement by the NSW Department of Education of a trial to permanently employ CRTs to ‘cover’ classes in regional and remote areas of the state (NSW Department of Education, 2020 ) is a critical step to support the implementation of effective PD. Such a strategy provides not only a means for teachers to engage in effective PD, like QTR, but creates an incentive for casual teachers to teach in hard-to-staff areas by appointing them to a permanent position. Given that this trial just commenced at the time of writing, future research into how it might impact the implementation of effective PD in regional and remote areas of Australia is an important area for further investigation.

In sum, many of these implementation challenges are not new in the field of PD or in educational reform more broadly (Ball et al., 2012 ; Datnow et al., 2002 ; Hill et al., 2013 ). However, implementation science offers a new mechanism for conceptualising, evaluating and enhancing implementation through a systematic focus on context. It is insufficient to examine changes in teachers’ knowledge and practice or even student achievement associated with different program features. Instead, for PD to be effective, both the design of a program and the quality of its implementation are critical (Centre for Evidence & Implementation, 2017 ). When ‘effective PD’ fails to improve student outcomes, it may well be because implementation has deviated from best practice (Hill et al., 2013 ). Heuristics such as Proctor et al.'s ( 2011 ) can be highly beneficial by providing evidence with which to support implementation at scale. We argue that a dual focus on program features and program implementation is critical in the ongoing quest for effective PD.

Empirically and conceptually, this case study has significant implications for the study of effective PD. Rather than continuing to showcase interventions that ‘work’ and pinpoint their key design features, there is clearly an unmet and critical need to understand how programs are—and can be—implemented across diverse school contexts and how, in turn, implementation can be evaluated and enhanced. As Bryk ( 2015 ) argues, “the latter is what practitioners typically want to know—what will it take to make it work for me , for my students, and in my circumstances?” (p. 469; emphasis added). The money invested in PD—estimated to be billions of dollars annually (Kraft et al., 2018 )—demands that, in going forward, understandings of effective PD must be accompanied by knowledge of effective implementation.

Ball, S. J. (2003). The teacher’s soul and the terrors of performativity. Journal of Education Policy, 18 (2), 215–228. https://doi.org/10.1080/0268093022000043065

Article   Google Scholar  

Ball, S. J., Maguire, M., & Braun, A. (2012). How schools do policy: Policy enactments in secondary schools . Taylor & Francis.

Google Scholar  

Bauer, M. S., Damschroder, L., Hagedorn, H., Smith, J., & Kilbourne, A. M. (2015). An introduction to implementation science for the non-specialist. BMC Psychology . https://doi.org/10.1186/s40359-015-0089-9

Borko, H. (2004). Professional development and teacher learning: Mapping the terrain. Educational Researcher, 33 (8), 3–15. https://doi.org/10.3102/0013189x033008003

Borko, H., Jacobs, J., & Koellner, K. (2010). Contemporary approaches to teacher professional development. In P. Peterson, E. Baker, & B. McGaw (Eds.), International encyclopedia of education (Vol. 7, pp. 548–556). Elsevier.

Chapter   Google Scholar  

Bowe, J. (2016). Quality Teaching Rounds: Strengthening the knowledge base and collaborative processes for teacher professional development [dissertation]. The University of Newcastle, Newcastle, Australia. Retrieved from https://nova.newcastle.edu.au/

Bowe, J., & Gore, J. (2017). Reassembling teacher professional development: The case for Quality Teaching Rounds. Teachers and Teaching, 23 (3), 352–366. https://doi.org/10.1080/13540602.2016.1206522

Bryk, A. S. (2015). 2014 AERA distinguished lecture: Accelerating how we learn to improve. Educational Researcher, 44 , 467–477. https://doi.org/10.3102/0013189x15621543

Centre for Evidence and Implementation. (2017). Implementation in education: Findings from a scoping review . Centre for Evidence and Implementation. Retrieved from https://evidenceforlearning.org.au/

Charteris, J., & Smardon, D. (2018). "Professional learning on steroids”: Implications for teacher learning through spatialised practice in new generation learning environments. Australian Journal of Teacher Education, 43 (12), 12–29. https://doi.org/10.14221/ajte.2018v43n12.2

City, E., Elmore, R., Fiarman, S., & Teitel, L. (2009). Instructional rounds in education . Harvard Education Press.

Cochran-Smith, M. (2015). A tale of two teachers: Learning to teach over time. Learning Landscapes, 8 (2), 111–134. https://doi.org/10.36510/learnland.v8i2.699

Cohen, J., & Goldhaber, D. (2016). Building a more complete understanding of teacher evaluation using classroom observations. Educational Researcher, 45 (6), 378–387. https://doi.org/10.3102/0013189x16659442

Damschroder, L. J., Aron, D. C., Keith, R. E., Kirsh, S. R., Alexander, J. A., & Lowery, J. C. (2009). Fostering implementation of health services research findings into practice: A consolidated framework for advancing implementation science. Implementation Science . https://doi.org/10.1186/1748-5908-4-50

Darling-Hammond, L., Hyler, M. E., & Gardner, M. (2017). Effective teacher professional development . Learning Policy Institute. Retrieved from https://learningpolicyinstitute.org/

Datnow, A., Hubbard, L., & Mehan, H. (2002). Extending educational reform: From one school to many . RoutledgeFalmer.

Desimone, L. M. (2009). Improving impact studies of teachers’ professional development: Toward better conceptualizations and measures. Educational Researcher, 38 (3), 181–199. https://doi.org/10.3102/0013189x08331140

Desimone, L. M. (2011). A primer on effective professional development. The Phi Delta Kappan, 92 (6), 68–71. https://doi.org/10.1177/003172171109200616

Eccles, M. P., & Mittman, B. S. (2006). Welcome to implementation science. Implementation Science . https://doi.org/10.1186/1748-5908-1-1

Elmore, R. (2007). Professional networks and school improvement. The School Administrator, 64 (4), 20–24. Retrieved from https://www.aasa.org/

Garet, M. S., Porter, A. C., Desimone, L., Birman, B. F., & Yoon, K. S. (2001). What makes professional development effective? Results from a national sample of teachers. American Educational Research Journal, 38 (4), 915–945. https://doi.org/10.3102/00028312038004915

Gore, J. (2021). The quest for better teaching. Oxford Review of Education, 47 (1), 45–60. https://doi.org/10.1080/03054985.2020.1842182

Gore, J., & Bowe, J. (2015). Interrupting attrition? Re-shaping the transition from preservice to inservice teaching through Quality Teaching Rounds. International Journal of Educational Research, 73 , 77–88. https://doi.org/10.1016/j.ijer.2015.05.006

Gore, J., & Rickards, B. (2020a). Rejuvenating experienced teachers through Quality Teaching Rounds professional development. Journal of Educational Change , Advance online publication. https://doi.org/10.1007/s10833-020-09386-z

Gore, J., & Rosser, B. (2020b). Beyond content-focused professional development: Powerful professional learning through genuine learning communities across grades and subjects. Professional Development in Education , Advance online publication. https://doi.org/10.1080/19415257.2020.1725904

Gore, J., Lloyd, A., Smith, M., Bowe, J., Ellis, H., & Lubans, D. (2017). Effects of professional development on the quality of teaching: Results from a randomised controlled trial of Quality Teaching Rounds. Teaching and Teacher Education, 68 , 99–113. https://doi.org/10.1016/j.tate.2017.08.007

Gore, J., Miller, A., Fray, L., Harris, J., & Prieto, E. (2021). Improving student achievement through professional development: Results from a randomised controlled trial of Quality Teaching Rounds. Teaching and Teacher Education, 101 . https://doi.org/10.1016/j.tate.2021.103297

Gore, J., Patfield, S., & Fray, L. (in press). Questioning the consensus on effective professional development. In R. Tierney, F. Rizvi, G. Smith, & K. Gutierrez (Eds.), International Encyclopedia of Education (4th ed.). Oxford, UK: Elsevier.

Hill, H. C. (2009). Fixing teacher professional development. Phi Delta Kappan, 90 (7), 470–476. https://doi.org/10.1177/003172170909000705

Hill, H. C., Beisiegel, M., & Jacob, R. (2013). Professional development research: Consensus, crossroads, and challenges. Educational Researcher, 42 (9), 476–487. https://doi.org/10.3102/0013189x13512674

Jahnukainen, M. (2009). Extreme cases. In A. J. Mills, G. Durepos, & E. Wiebe (Eds.), Encyclopedia of case study research (online). SAGE Publications.

Kane, T. J., McCaffrey, D. F., Miller, T., & Staiger, D. O. (2013). Have we identified effective teachers? Validating measures of effective teaching using random assignment . Bill & Melinda Gates Foundation. Retrieved from https://usprogram.gatesfoundation.org/

Kelly, B. (2012). Implementation science for psychology in education. In B. Kelly & D. Perkins (Eds.), Handbook of implementation science for psychology in education (pp. 3–12). Cambridge University Press.

Kraft, M. A., Blazar, D., & Hogan, D. (2018). The effect of teacher coaching on instruction and achievement: A meta-analysis of the causal evidence. Review of Educational Research, 88 (4), 547–588. https://doi.org/10.3102/0034654318759268

Meissel, K., Parr, J. M., & Timperley, H. S. (2016). Can professional development of teachers reduce disparity in student achievement? Teaching and Teacher Education, 58 , 163–173. https://doi.org/10.1016/j.tate.2016.05.013

Moir, T. (2018). Why Is implementation science important for intervention design and evaluation within educational settings? Frontiers in Education, 3 , 1–9. https://doi.org/10.3389/feduc.2018.00061

NSW Department of Education. (2020). More casual teachers for regional schools . NSW Department of Education. Retrieved from https://www.nsw.gov.au/news/more-casual-teachers-for-regional-schools

OECD. (2019). TALIS 2018 results (volume 1): Teachers and school leaders as lifelong learners. OECD Publishing. Retrieved from http://www.oecd.org/education/

Opfer, D., & Pedder, D. (2011). Conceptualizing teacher professional learning. Review of Educational Research, 81 (3), 376–407. https://doi.org/10.3102/0034654311413609

Proctor, E., Silmere, H., Raghavan, R., Hovmand, P., Aarons, G., Bunger, A., Griffey, R., & Hensley, M. (2011). Outcomes for implementation research: Conceptual distinctions, measurement challenges, and research agenda. Administration and Policy in Mental Health, 38 (2), 65–76. https://doi.org/10.1007/s10488-010-0319-7

Quinn, D. M., & Kim, J. S. (2017). Scaffolding fidelity and adaptation in educational program implementation: Experimental evidence from a literacy intervention. American Educational Research Journal, 54 (6), 1187–1220. https://doi.org/10.3102/0002831217717692

Sims, S., & Fletcher-Wood, H. (2021). Identifying the characteristics of effective teacher professional development: A critical review. School Effectiveness and School Improvement, 32 (1), 47–63. https://doi.org/10.1080/09243453.2020.1772841

The Design-Based Research Collective. (2003). Design-based research: An emerging paradigm for educational inquiry. Educational Researcher, 32 (1), 5–8. https://doi.org/10.3102/0013189x032001005

Thomas, D. C., Berry, A., Djuricich, A. M., Kitto, S., Kreutzer, K. O. K., Van Hoof, T. J., Carney, P. A., Kalishman, S., & Davis, D. (2017). What is implementation science and what forces are driving a change in medical education? American Journal of Medical Quality, 32 (4), 438–444. https://doi.org/10.1177/1062860616662523

Thomson, P. (2014). ‘Scaling up’ educational change: Some musings on misrecognition and doxic challenges. Critical Studies in Education, 55 (2), 87–103. https://doi.org/10.1080/17508487.2014.863221

Wei, R. C., Andree, A., & Darling-Hammond, L. (2009). How nations invest in teachers: High-achieving nations treat their teachers as professionals. Educational Leadership , 66(5), 28–33. Retrieved from http://www.ascd.org/

Wenger, E. (1998). Communities of practice: Learning, meaning, and identity . Cambridge University Press.

Book   Google Scholar  

Yin, R. (2013). Case study research: Design and methods (5th ed.). SAGE Publications.

Yoon, K. S., Duncan, T., Lee, S. W. Y., Scarloss, B., & Shapley, K. (2007). Reviewing the evidence on how teacher professional development affects student achievement . U.S. Department of Education, Institute of Education Sciences, National Center for Education Evaluation and Regional Assistance. Retrieved from https://ies.ed.gov/ncee/edlabs/

Download references

This research was funded by the Paul Ramsay Foundation and the NSW Department of Education. We wish to thank the schools and teachers involved in the research, as well as the project staff involved in data collection. We particularly wish to thank Claire Wallington for her oversight in project management.

Author information

Authors and affiliations.

Teachers and Teaching Research Centre, School of Education, University of Newcastle, University Drive, Callaghan, NSW, 2308, Australia

Sally Patfield, Jennifer Gore & Jess Harris

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Sally Patfield .

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Patfield, S., Gore, J. & Harris, J. Shifting the focus of research on effective professional development: Insights from a case study of implementation. J Educ Change 24 , 345–363 (2023). https://doi.org/10.1007/s10833-021-09446-y

Download citation

Accepted : 13 October 2021

Published : 29 October 2021

Issue Date : June 2023

DOI : https://doi.org/10.1007/s10833-021-09446-y

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Teacher professional development
• Implementation
• Effective professional development
• Implementation science
• Quality Teaching Rounds
• Educational reform
• Find a journal
• Publish with us
• Track your research

• Architecture and Design
• Asian and Pacific Studies
• Business and Economics
• Classical and Ancient Near Eastern Studies
• Computer Sciences
• Cultural Studies
• Engineering
• General Interest
• Geosciences
• Industrial Chemistry
• Islamic and Middle Eastern Studies
• Jewish Studies
• Library and Information Science, Book Studies
• Life Sciences
• Linguistics and Semiotics
• Literary Studies
• Materials Sciences
• Mathematics
• Social Sciences
• Sports and Recreation
• Theology and Religion
• Publish your article
• The role of authors
• Promoting your article
• Abstracting & indexing
• Publishing Ethics
• Why publish with De Gruyter
• How to publish with De Gruyter
• Our book series
• Our subject areas
• Your digital product at De Gruyter
• Contribute to our reference works
• Product information
• Tools & resources
• Product Information
• Promotional Materials
• Orders and Inquiries
• FAQ for Library Suppliers and Book Sellers
• Repository Policy
• Free access policy
• Open Access agreements
• Database portals
• For Authors
• Customer service
• People + Culture
• Journal Management
• How to join us
• Working at De Gruyter
• Mission & Vision
• De Gruyter Foundation
• De Gruyter Ebound
• Our Responsibility
• Partner publishers

Your purchase has been completed. Your documents are now available to view.

Professional Development through Teacher Research

Stories from language teacher educators.

• Edited by: Darío Luis Banegas , Emily Edwards and Luis S. Villacañas de Castro
• X / Twitter

Please login or register with De Gruyter to order this product.

• Language: English
• Publisher: Multilingual Matters
• Copyright year: 2022
• Audience: College/higher education;
• Main content: 240
• Keywords: Teacher training ; Language teaching theory & methods
• Published: March 21, 2022
• ISBN: 9781788927727

10 Strategies for Effective Teacher Professional Development (with Examples)

Most teachers are also passionate lifelong learners. In fact, courses and programs that provide opportunities for professional development for teachers are extremely important in the education field.

Motivated by intellectual curiosity and a desire to be the best educator they can be, many teachers also take professional development courses to help position themselves for salary advancement or to further their career goals.

So, what is teacher professional development and why is it so important?

Professional development for educators usually takes the form of either in-house, school district-sponsored training sessions or continuing education courses and programs, delivered on campus or online, under the auspices of a college or university.

Courses range from general topics that help educators stay on top of the newest standards and strategies, to specific subjects designed to help them enrich their teaching practice and improve classroom learning.

Unfortunately, enough educators have had an underwhelming experience with professional development that many do not hold the concept in high regard. According to a study cited in the Center for Public Education report “Teaching the Teachers: Effective Professional Development in an Era of High Stakes Accountability,” there is no shortage of opportunity. Researchers found that 90 percent of teachers reported participating in professional development, but “most of those teachers also reported that it was totally useless.” That is why the emphasis must be on effective professional development.

Why Teacher Professional Development is Important

The field of education is constantly changing. So it makes sense that teachers must embrace the idea of being lifelong learners as well.

Professional development describes programs that enable educators to improve their own teaching — both by learning new teaching styles, tips and techniques, as well as subject areas; and by interacting with expert instructors and experienced educators in the program of their choice.

Federal, state and local educational organizations all place a high value on ongoing professional development for teachers. And it’s easy to understand why. For one, there is research to indicate that teacher professional development can enhance student comprehension and achievement, according to a tolerance.org report titled “Teaching Teachers: PD To Improve Student Achievement.”

Here are several of the perceived and proven benefits of quality professional development programs for teachers:

• Enables teachers to hone specific skills and learn new ones.
• Provides new strategies for educators.
• Can enhance student learning.
• Encourages the success of new teachers.
• Is an opportunity for teachers to go in-depth on specific topics.
• Promotes a professional growth mindset.
• Can be used to meet salary advancement requirements.
• Creates better planning and organizational skills.
• Keeps educators engaged, motivated and positive.

The Learning Policy Institute authored a report in which it defines effective teacher professional development as “structured professional learning that results in changes in teacher practices and improvements in student learning outcomes.”

10 Strategies for Effective, Engaging Teacher Professional Development

Ranging from in-school workshops during so-called professional development days to in-depth courses and programs taught by college-level instructors in a university classroom or online setting, professional development programs for teachers can range from obligatory to inspiring.

So what are some ways to ensure that such programs deliver the most bang for the buck — for teachers, for their school districts and, most important, for students? Here’s a closer look at several strategies aimed at ensuring that teacher professional development efforts are as effective as possible.

• Focus on honing classroom teaching skills: This goes to the heart of the idea that one of the most important purposes of teacher professional development is to enhance student learning. There are many teacher professional development courses created specifically to help working teachers improve their practice, ranging from programs for Beginning Teachers (example: Introduction to Instructional Design for Educators ) to courses on Classroom Teaching Techniques (example: Using Inquiry, Discussion and Experience to Develop Critical Thinkers and Inspire Lifelong Learning )
• Use it to develop subject matter expertise: Helping teachers gain advanced expertise in key academic areas, especially those that track with their personal and professional interests, can pay dividends in student achievement as well as teacher engagement and satisfaction. For example: A teacher who takes an in-depth course on the interconnectedness of Science, Technology, Engineering, the Arts and Mathematics is likely to return to their classroom with a fresh head of STEAM .
• Provide strategies for overcoming specific challenges in the classroom: Teacher development programs on such important topics as Bullying Prevention and Classroom Management provide valuable insight and perspective into aspects of the educational experience that can help set the stage for optimal learning.
• Encourage added value through networking and collaboration: Meaningful interactions with expert instructors and experienced fellow educators are another valuable aspect of the professional development experience. In online teacher professional development courses, for example, peers often come from other parts of the country and can bring new and unique perspectives to familiar topics.  
• Consider different formats: While in-depth professional development courses and one-off workshops are two of the most common formats for teacher professional development, there is a range of other models as well. From “unconferences” and lab classrooms to creating professional learning communities (PLCs), education blogger Jennifer Gonzalez examines nine different models in a post titled “OMG Becky. Professional Development is Getting So Much Better!”
• Don’t forget technology: The transformative impact of technology in education is vitally important, but occasionally overlooked. Though some teachers are resistant to technology, others may be surprised to discover that it can enhance their ability to help students thrive in the digital age. Examples of technology-themed professional development courses for educators include: The Tech Savvy Teacher, STEAM: Supporting Creativity for Innovation and Digital Literacies for the 21st Century Classroom.
• Keep it simple and specific: Picking one or two things to focus on, rather than seven or eight, is an example of addition by subtraction. Whether you’re a teacher in search of the ideal professional development courses or representing a school or district that provides formal training for educators, specific in-depth training is more likely to yield actionable classroom “takeaways” than programming that is too broad in scope. 
• Make it ongoing: For school districts, professional development training is most effective when paired with ongoing support and evaluation from administrators, including opportunities to review and learn from what worked and what did not. For teachers seeking ongoing professional development opportunities outside their district — the world is your oyster in terms of choosing topics that track with your interests. Regarding opportunities for ongoing, in-depth study, the professional development departments at some universities offer educators the option of completing certificate programs, multi-course programs of study that demonstrate the mastery of a specific subject area or body of knowledge.
• Create opportunities for feedback and discussion: Many school districts do a solid job at developing systems for providing teachers with helpful feedback and for determining whether professional development initiatives are having an effect on student achievement. Teachers can also get feedback independently by cultivating connections with fellow teachers in their district and by using online professional development courses to develop new connections with educators from other locales.
• Actually put new training to work in the classroom: Much like a guidebook that gets written and then put on the shelf, teacher professional development is only effective when educators put what they’ve learned to use in their teaching. Of course, this means it is essential that PD training be interesting and relevant but, just as important, that teachers commit to continuing the work in the classroom.

5 REASONS WHY CONTINUING EDUCATION MATTERS FOR EDUCATORS

The education industry is always changing and evolving, perhaps now more than ever. Learn how you can be prepared by downloading our eBook.

Popular Topics for Teacher Professional Development Courses

Topics for professional development courses run the gamut from Common Core Standards to Introduction to Coding for Educators. Differentiated Instruction, Adolescent Literacy and Closing the Achievement Gap, are among those cited by Teacher.org .

The University of San Diego, which offers hundreds of high-quality professional development courses for educators through its Division of Professional and Continuing Education, lists the following courses as among its most popular:

• Dynamic Vocabulary Instruction
• Teaching Methods for Diverse Learners
• Rethinking Your Homework and Grading Paradigm
• Infusing Art in the 21st Century Classroom
• Google Tools for Collaborative Teaching
• Chromebooks in the Classroom
• Teaching Positive Social Skills to Students
• Introduction to the Mindful Classroom: The Art & Science of Well-being for Staff and Students
• Math is Not Only Numbers: Infusing Literacy and Brain Research in Teaching Math Concepts

Teacher Professional Development FAQs

What is teacher professional development.

Teacher professional development typically refers to programs focused on continuing education effort for educators. The goal is to provide teachers with opportunities to continue to improve their skills and learn new strategies and techniques, thereby leading to better student outcomes in the classroom.

Can teacher professional development courses be used for salary advancement?

Yes. Many school districts provide financial incentives for teachers who continue their professional growth by offering salary step increases for completing professional development programs. Note: The requirements for obtaining such salary increases vary widely; therefore, it is important to seek pre-approval from your school district to ensure that courses taken for salary advancement will fulfill district requirements.

Do some states require teachers to complete professional development training to maintain their licenses?

Specific requirements for license renewal vary greatly from state to state, with most states requiring some amount of continuing professional development training be completed by teachers to maintain their licenses, according to teachtomorrow.org . (California, New Jersey and Rhode Island are among the exceptions.)

Are there different formats for teacher professional development?

Yes. Teacher professional development is often thought of as in-school training programs administered by the individual school or district, either on designated teacher development days during the school year or in the summer. Educational conferences and workshops can sometimes count as professional development. Additionally, educators can find a world of professional development opportunities in online and on-campus courses offered through an accredited college or university. 

Curriculum covered in this article

Be sure to share this article.

• Share on Twitter
• Share on Facebook
• Share on LinkedIn

Your Salary

Browse over 500+ educator courses and numerous certificates to enhance your curriculum and earn credit toward salary advancement.

Iowa Reading Research Center

Research Article of the Month: August 2024

This blog post is part of our  Research Article of the Month series. For this month, we highlight “ Teacher Professional Development and Student Reading Achievement: A Meta-Analytic Review of the Effects ,” an article published in Journal of Research on Educational Effectiveness in 2019. Important words related to research are bolded, and definitions of these terms are included at the end of the article in the “Terms to Know” section.

Why Did We Pick This Paper?

Research suggests that high-quality teachers play a significant role in student achievement—higher than any other school factor (Hattie, 2009). One way to build teacher capacity is through professional development (PD)—training on current, effective, evidence-based instructional methods. PD can take different forms, including workshops, professional learning communities, coaching, online training, or conference attendance. 

High-quality PD can improve teachers’ knowledge and skills and change their attitudes and beliefs. This, in turn, can affect their instruction and practices, which is likely to positively impact student learning (Desimone, 2009).

This study measures the effects of teacher PD on student reading outcomes. Researchers also examine moderators  that may influence these outcomes, including characteristics of the study design (e.g., experimental design, student outcomes measured), PD opportunities (e.g., intensity, delivery method, level of collaboration, format), teachers (e.g., years of experience, certifications and degrees), and students (e.g., disability status, grade level). 

Findings from this study may help districts identify and provide high-quality PD that builds teacher knowledge and supports student reading achievement.

What Are the Research Questions or Purpose?

The researchers examined the impact of teacher PD on student reading outcomes by addressing the following research questions:

• What are the effects of PD on reading achievement for students in Grades K–8?

What elements of study design are potential moderators of effects?

What characteristics of professional development are potential moderators of effects, what characteristics of participants, both teacher and student, are potential moderators of effects, what methodology do the authors employ.

The authors conducted a meta-analysis of 28 quantitative research studies that examined the impacts of teacher PD on student reading outcomes. To be included in the analysis, the studies needed to:

• Be conducted in a K–8 setting
• Examine teacher PD as the independent variable
• Examine student reading achievement (e.g., phonological awareness, decoding, word identification, fluency, vocabulary, or comprehension) as the dependent variable  
• Utilize an experimental or quasi-experimental design
• Include effect sizes or the ability to calculate them
• Be published in a peer-reviewed journal in English between 1975 and 2017

For each of the included studies, researchers examined the students’ performance on a reading outcome. These outcomes were classified as either code-focused (e.g., phonological awareness, decoding, word identification, fluency), meaning-focused (e.g., comprehension, vocabulary), or general reading ability. 

Researchers also took into account other variables in the studies that could affect the outcomes of PD. These variables included:

• Experimental design ( randomized control trials , quasi-experimental design)
• Student outcomes measured (code-focused or meaning-focused)
• Intensity (number of hours of PD)
• Delivery method (district staff, researchers, online)
• Level of collaboration and active participation
• Format (whole group, summer workshop, professional learning community, coaching)
• Average years of teaching experience
• Percentage of teachers with advanced degrees
• Disability status
• Grade level

The researchers estimated effect sizes of teacher PD on student reading outcomes using a random effects model . They also examined the relationships between potential moderators (i.e., study, PD, teacher, and student characteristics) and student outcomes. 

What Are the Key Findings?

What are the effects of pd on reading achievement for students in grades k – 8.

Overall, the analysis results of the study indicate that teacher PD positively impacted student reading outcomes in reading (g = 0.18). However, this is the average effect size, and there is notable variation in effect sizes reported from the primary studies included in this meta-analysis. This indicates that some kinds of PD were more effective than others. For example, Teacher Study Group (TSG), a PD model, had a medium to large positive effect on student reading outcomes (Gersten et al., 2010) whereas other PD models had small or no effect. 

• For randomized controlled trials (g = 0.18) and quasi-experimental design studies (g = 0.19), teacher PD significantly improved student outcomes in reading.
• Teacher PD significantly improved both code-focused student outcomes (g = 0.22) and meaning-focused student outcomes (g = 0.17).

The PD characteristics examined did not significantly moderate the effect between PD and reading outcomes.

The teacher and student characteristics examined did not significantly moderate the effect between teacher PD and reading outcomes.

What Are the Practical Applications of Key Findings?

Findings suggest that teacher PD generally has a positive effect on student reading achievement in Grades K–8. It is worth noting that the average total length of teacher PD was around 52 hours, with a range of 4 to 295 hours across studies, and these PD opportunities were associated with varying levels of impact on student outcomes. This wide range indicates that a certain level of intensity and duration may be necessary for teacher PD to have a significant effect on student outcomes, although there is no agreement among researchers on the level of intensity required to effectively enhance teacher knowledge and practices and produce improved student outcomes. When providing reading and literacy PD for teachers, it is important to ensure that the content is both meaningful and relevant to the teacher’s instructional needs. Additionally, allocating sufficient time to PD can help maximize its benefits for teachers and students. 

What Are the Limitations of This Paper?

When examining the relationships between the moderators and the student reading outcomes, teacher characteristics such as teaching experience and advanced degrees were included in the moderator analysis. However, teacher knowledge and skills, another important part of teacher characteristics, was not considered or included in the analysis. Teacher knowledge and skills has been shown to potentially influence student learning and, ultimately, their reading outcomes (Soodla, Jogi & Kikas, 2017;  Porter et al., 2023). Future research should incorporate this characteristic to provide a more comprehensive understanding of the factors that contribute to student outcomes, as the primary goal of teacher PD programs is to enhance both teacher knowledge and skills. 

In addition, there is a lack of data on students with disabilities and secondary students across studies. Only one study included in the meta-analysis examined reading outcomes for students with reading difficulties, and there were few studies at the secondary level. It remains unclear whether teacher PD could effectively address the needs of students with disabilities, who require high-quality, specialized instructional strategies to support their reading. Future studies should explore the impact of PD on teacher’s ability to support these students. 

Terms to Know

• Moderator: Moderators are variables that affect the relationship between two other variables. For example, the relationship between the length of a reading intervention and reading comprehension may be stronger for students who are at risk for reading disabilities versus students who are not at risk. In this case, at-risk status would be a moderator.
• Independent variable: An independent variable is a factor that influences dependent variables in experimental studies. For example, the length of a reading intervention in total minutes (independent variable) may affect a student’s composite reading score (dependent variable). They are called “independent” because they are manipulated by the experimenter and therefore independent of other influences.
• Dependent variable: Dependent variables are factors that may change in response to an independent variable. For example, a student’s composite reading score (dependent variable) may change in response to the length of reading intervention they receive in total minutes (independent variable).
• Experimental: Experimental research aims to determine whether a certain treatment influences a measurable outcome—for example, whether a certain instructional method influences students’ reading comprehension scores. To do this, participants are divided into two groups: an experimental group, which receives the treatment, and a control group, which does not receive the treatment. In an experimental study, these groups are randomly assigned, meaning each participant has equal probability of being in either the treatment or the control group. Both groups are tested before and after the treatment, and their results are compared. Because participants are randomly assigned to a control group, this kind of study is also known as a randomized control trial .
• Quasi-experimental: A quasi-experimental study is similar to an experimental study except that participants are not randomly assigned to groups. In educational research, groups often are assigned by classroom rather than through random assignment, making this kind of research quasi-experimental.
• Effect size: In statistics, effect size is a measure of the strength of the relationship between two variables in statistical analyses. A commonly used interpretation is to refer to effect size as small (g = 0.2), medium (g = 0.5), and large (g = 0.8) based on the benchmarks suggested by Cohen (1988), where “g” refers to Hedge’s g, a statistical measure of effect size.
• Peer-reviewed journal: When an author submits an article to a peer-reviewed journal , the article is reviewed by scholars in the field. They make sure that the article is accurate, relevant, high quality, and well written.
• Randomized control trial:  See experimental. 
• Random effects model: A random effects model is a type of statistical model that measures how an independent variable affects a dependent variable across a number of different samples or studies. Unlike a fixed effects model, a random effects model accounts for variability between different groups in a dataset.

Cohen, J. (1988). Statistical power analysis for the behavioral sciences . Routledge.

Desimone, L. M. (2009). Improving impact studies of teachers’ professional development: Toward better conceptualizations and measures. Educational Researcher , 38 (3), 181–199.  https://doi.org/10.3102/0013189X08331140  

Didion, L., Toste, J. R., & Filderman, M. J. (2019). Teacher professional development and student reading achievement: A meta-analytic review of the effects. Journal of Research on Educational Effectiveness , 13 (1), 29–66.  https://doi.org/10.1080/19345747.2019.1670884  

Hattie, J. A. (2009). Visible learning: A synthesis of 800+ meta-analyses on achievement . Routledge.

Gersten, R., Dimino, J., Jayanthi, M., Kim, J. S., & Santoro, L. E. (2010). Teacher study group: Impact of the professional development model on reading instruction and student outcomes in first grade classrooms.  American Educational Research Journal ,  47 (3), 694-739.  https://doi.org/10.3102/0002831209361208  

Porter, S. B., Odegard, T. N., Farris, E. A., & Oslund, E. L. (2023). Effects of teacher knowledge of early reading on students’ gains in reading foundational skills and comprehension.  Reading and Writing , 1-17.  https://doi.org/10.1007/s11145-023-10448-w  

Soodla, P., Jõgi, A. L., & Kikas, E. (2017). Relationships between teachers’ metacognitive knowledge and students’ metacognitive knowledge and reading achievement.  European Journal of Psychology of Education ,  32 , 201-218.

• professional development
• professional learning communities
• Research Article of the Month

Research Article of the Month: June 2024

Research Article of the Month: May 2024

Research Article of the Month: April 2024

• Our Mission

Creating Meaningful and Productive PD

These ideas for creating effective professional learning help ensure that participants, and their students, reap long-term benefits.

When I ask teachers to join me for a professional learning experience, it’s important that I value their time by providing tools to impact practice and increase student learning. My goals are to model strategies, bring a sense of efficacy, and provide them an opportunity to build skills they can use immediately in their own classrooms.

Many of the ideas below work for professional development sessions and transfer over to coaching partnerships and facilitation moves in professional learning communities.

Plan With Intention

Developing a professional learning experience begins by devoting a block of time to careful, purposeful planning. Just as I do with lesson or unit planning for students, I begin with the end in mind, focusing first on the following questions:

• What is the goal for teacher learning?
• What is the goal for growing as a community? 
• What will success look like and sound like?

When I’m clear on the focus, I can prioritize and streamline the content with an appropriate amount of new learning and practice. Giving opportunities to practice new learning is of huge importance. The transfer of new ideas to instruction occurs when teachers have time to plan, rehearse with each other, and/or synthesize by applying the learning to their own context. 

Next, I consider agreements for the experience: What are the norms we are going to strive for during the session(s)? Depending on the group and the length of time we will spend together, I opt to either codevelop the agreements with the group or provide agreements and ask for participants to give a thumbs-up if they feel the agreements match the learning objectives and circumstances.

I also determine if we need to delve deeply into an agreement or two to brainstorm what it would sound like and look like in practice. Next, I decide if we will use roles to support any agreements. For example, if we’re focusing on using asset-based language, will we have one or more people responsible for reminders throughout the meeting?

While planning, I consider how I can reflect back to the group any prior feedback and steps I’ve taken to improve. For example, if teachers indicated that we went too fast at the last session, I can share with them that I’ve slowed down the pace and allowed more time to process information at this session.

 I also plan for a way to gather feedback at the end of the session, selecting from a variety of ways to share feedback, such as a Google Form, sticky notes on a “gots and wants” poster, etc. It’s important to gather feedback on both the process and content.

The final step of planning is to design the space to meet the collaboration and content goals.

• How will I invite people into the space?
• How will they sit?
• Will seating be assigned or random, and do I have a reason behind the decision? How will I communicate this decision to be transparent and intentional?
• What visuals will I use around the room to scaffold learning, and what materials will I bring to support the objectives?

During the Session

Beginnings matter. I show up early to the learning space to set up and prepare myself. I consider the energy level I need to bring to the session and strive to get myself fully focused to listen deeply, respond effectively, and pivot as needed during the session. I try to greet people as they arrive if possible; this helps me connect and get a sense of the energy level people are bringing.

When we begin the session, I set the tone by smiling, projecting enthusiasm, and offering an explanation of the goals so they know what to expect.

When presenting, I try to model facilitation moves that teachers can use in their classrooms. I point out the strategies by pausing, moving to a different space, and describing the what and the why of the strategy. When I physically move to a different space, teachers know I am shifting gears. Then I return to my original spot and continue modeling.

This strategy also works when I make language shifts. None of us is perfect, and we will make mistakes when we present. If I use unclear or negative language, I can pause, move, pause again, and then use language that clarifies what I meant to say.

It’s important to give participants time to process information. Here are some ideas for this: turn and talk to a neighbor, do Numbered Heads Together (participants talk with each other, then I call on one person per table to share out), create a poster to summarize learning, fill out a graphic organizer to summarize main points.

Intentional use of visuals, modeling, and gradual release is important during a professional learning opportunity. When providing gradual release, I show how to do something; then we do it together as a group before I have partners practice and then possibly individuals.

Endings matter. The ending is often what people remember when looking back at an experience. Depending on content and context, I often aim to add on a positive note, so that people feel celebratory and energized. I strive to leave time to close in a meaningful way. This can include reflecting on what we learned, how we will measure the impact on student learning, or the ways teachers feel efficacy.

When I work with children, I plan lessons with objectives and the student experience in mind; the process is very similar when I plan for adults. As an instructional coach, I consider it a privilege to work alongside teachers and to learn alongside them. Their time is valuable, so I need to show up prepared with strategies and learning opportunities they will find immediately useful.

Planning for meaningful adult learning takes time and focus, but it’s time well spent if teachers find it useful and it impacts student learning.

• Majors & Minors
• About Our Faculty
• Academic Experience
• Academic Support
• Graduate Programs
• Get Involved
• Athletics & Sports at UWEC
• Meet Blugolds
• Living in Eau Claire
• Music, Arts, & Culture
• First-Year Visits
• Transfer Visits
• Group Visits
• Plan Your Trip
• What to Expect
• Virtual Tour Options
• First-Year Student
• Transfer Student
• International Students
• High School Special Student
• Graduate Student
• Other Student
• UWEC Application
• Contact Admissions
• Tuition & Fees
• Financial Aid
• Scholarships
• Net Price Calculator
• University Mission
• Campus History
• Accreditation
• Campus Events and Calendars
• Collaborations and Partnerships
• Points of Pride
• Work at UW-Eau Claire 

Master of Education in Professional Development

Designed specifically for the professional development of educators. Curriculum is based on National Board Certification standards, offering mostly online classes.

Further Your Education

The master of education in professional development graduate program is a blended weekend and online 30-credit cohort program designed specifically for the professional development of educators. The two-year program is an excellent choice for those who seek a meaningful master's degree that will have a lasting, positive impact on their teaching, their leadership, and their students' engagement and learning.

All courses throughout the program focus on equity and justice, inclusion, critical thinking, and collaboration. Each course is co-taught with a UWEC faculty member and an experienced K-12 schoolteacher, with the goal of developing collaborative leaders. Coursework is intentionally designed for continuous application of theory to practice in your own classroom, providing ways for you to determine the practices that work best for you and your context. As part of the final capstone requirement, you’ll explore your own action research project and discover how you can develop your professional practice to improve student learning and create change in your school.

As a graduate of the MEPD program, you'll leave UW-Eau Claire with a variety of research-based strategies to apply to your own classroom. Additionally, you'll find that a better understanding of research and evidence-based practices enables you to make data-informed decisions and enhance student engagement and outcomes. Whether you seek new understandings, strategic ways to facilitate student learning, or the opportunity to re-envision your leadership, our renowned program will provide the experiences and skills necessary for change.

Program Details

The MEPD program was designed specifically for busy educators, with a predominantly online approach as well as a few opportunities to connect in person with colleagues. The rest of the program is online with peer engagement. Classes are scheduled year-round so that all educators are able to complete their degree in two years.

Supported by a cohort of professionals, you'll meet people who — like you — are passionate about education and motivating learners. Together, you'll share ideas, have rich discussions and build on your skills. Many graduates leave with an excellent teacher network and enhanced confidence as a leader.

MEPD program curriculum is taught by full-time UW-Eau Claire faculty who are experienced in teaching adult learners, as well as outstanding K-12 educators. The opportunity to present your final capstone project to administrators and the public enables you to teach — and learn from — other experts in the field.

Blugold Stories

Enhance learning in your classroom. Practice action research. Make data-driven decisions. Reignite your passion for teaching through collaboration. 

The master of education in professional development program is a 30-credit, 20-month master's program comprised of 10 courses.

Curriculum is focused on improving learning outcomes for your K-12 students. Aligned with the National Board for Professional Teaching Standards, coursework will encourage you to become a better leader, researcher, and teacher. Throughout the 10 courses, expert faculty will connect common themes, providing the foundation for connected future learning. Every course will model between five to 20 classroom practices that teachers can immediately apply in their own classrooms.

Here are a few courses in Master of Education in Professional Development at UW-Eau Claire.

Collaborative Leadership: Building Effective Relationships

Designed to help students build and align their knowledge, skills and dispositions as Teacher and Collaborative Leader in order to act in concert with colleagues, administrators, students and their parents.

Cultural and Social Foundations of Learning

Designed to provide students the knowledge, skills and dispositions needed to help all students learn more effectively in the school setting. Philosophical underpinnings will provide the basis for learning across the behaviorist to constructivist continuum.

Curricular Design and Innovation

This course investigates curricular models aligned with state and national standards, with a focus on how curricular design promotes learning and innovation in the classroom setting.

Related Programs

Thinking about studying master of education in professional development? You might also be interested in exploring these related programs.

What's Next?

University of Wisconsin-Eau Claire

105 Garfield Avenue  P.O. Box 4004  Eau Claire, WI 54702-4004 

715-836-4636

3 Things Principals Can Do to Make Teacher PD Better

• Share article

There is a wide gap between how teachers and school leaders view professional development. For many teachers, PD conjures up images of boring, one-sided lectures that have little to do with their classroom reality.

In fact, almost half of the 1,498 teachers surveyed by the EdWeek Research Center in October 2023 said they found their PD “irrelevant.” In stark contrast, only 16 percent of the 659 school leaders surveyed during the same period thought the same about teacher PD.

School leaders have tried different things to make the PD they offer more relevant and engaging for teachers. Some have encouraged teachers to pick a topic they’re passionate about, while others have moved mandatory PD modules online for teachers to complete at their convenience. Some school leaders believe frequent follow-up check-ins with teachers can help them apply what they learn in their PD sessions.

Still, it’s a struggle for school leaders to design the PD teachers want as they juggle district-mandated trainings and initiatives needed to meet their schools’ goals, said Brooklyn Joseph, a lead program facilitator with Lead by Learning, a program at Northeastern University where she partners with schools to design professional learning.

With all this information coming at them, teachers feel like they’re ingesting a lot of content that doesn’t always link back to their classroom practice, Joseph said during an Education Week K-12 Essentials Forum on school leadership last month.

Getting PD right isn’t just a time or resource challenge, Renee Gugel, an assistant professor of teacher leadership at the National Louis University in Chicago, said during the forum. To make PD fun and engaging for teachers, principals also need to build their own capacity.

“Sometimes, [the obstacle] is not knowing how to go about it,” Gugel added.

Gugel and Joseph made three key recommendations to school leaders on designing PD that’s useful to teachers. Their session can be viewed in the above video.

Start with the right information

Surveys at the start of the school year are a good way to pick up information on the kind of PD teachers want. The challenge is that school leaders seldom share or reflect on the results with teachers, Gugel said.

“It can be hard [for school leaders] to share the results. Teachers are going to say stuff you don’t agree with or feel offended by, because you planned the PD [sessions],” Gugel said.

But if school leaders can be transparent about the feedback in staff meetings, it can signal to teachers that they’ve been heard and their concerns are being addressed. “It’s an immediate climate shifter,” Gugel said.

The information loop shouldn’t be restricted to surveys. Joseph recommends creating “design teams” of veteran and new teachers across grades and subject areas who can help school leaders plan PD based on past survey feedback. Teachers may respond to PD better if their peers help plan it, Joseph said, and design teams can help make these sessions more relevant to their needs.

“School leaders don’t have to plan all the PD by themselves in a vacuum,” Joseph said.

Teachers should also have the option to answer survey questions anonymously, Gugel said, if they are nervous about openly critiquing a PD session planned by their principal.

Strike a balance between teacher agency and a school’s instructional goals

Effective PD should focus on one or two key topics chosen by teachers, Joseph said.

“Just like we provide structures and routines for students in classrooms to do their own independent learning, once we allow that choice [to teachers], we find that they want to explore [more] about their instructional practices. [Teachers] have to care about what they’re learning,” Joseph said.

Gugel added that PD should be actionable—teachers should be able to apply practices they learn during PD in their classrooms shortly after the session takes place.

The PD that emerges from this process, though, should not be completely detached from the school’s instructional goals.

The process to find the best PD should be grounded in a school’s data, Joseph said. School leaders and teachers can look at test scores as well as internal school indicators like student behavior. Teachers and school leaders should look at these data together and determine areas for improvement.

By doing that, Joseph said, teachers have the agency to choose their own PD but are still guided by the school’s overall instructional goals.

Some school leaders can be wary of giving too much choice to teachers. Gugel warned against this: “When teachers hear that their school leaders trust them to use their [PD] time well, that’s motivating in itself.”

The most popular form of teacher PD

The most exciting form of PD, both experts agreed, is when teachers can learn from each other.

Teachers learn from each other informally through observing classrooms or catching up over instructional strategies in their free time. But Gugel and Joseph recommended that school leaders also create more formal PD spaces for such sharing.

Then, teachers can share their experiences trying out new teaching methods and discuss new patterns of student learning. For instance, teachers can use these spaces to drill down on specific tactics like how to best organize a classroom to encourage student learning in smaller groups.

These “collaborative groupings"—as part of smaller professional learning communities or larger PD sessions—can also help newer teachers get a feel for what’s going on in their peers’ classrooms, and how they can adapt some of these instructional strategies in their own teaching, Joseph said. This type of PD is a useful way, too, for veteran teachers to share their experiences, instead of spending time going over trainings they’ve already had.

Arranging this opportunity for PD might be yet another task on a leader’s to-do list, but Joseph said it’s worth the effort.

“We want to have a vision for where we are taking teachers,” she said. “But we also want to provide space and time for teachers to take us on a different journey … [to] the place where they are feeling inspired and passionate.”

Sign Up for The Savvy Principal

Edweek top school jobs.

Sign Up & Sign In

• News & Stories

EHD Researchers will Conduct Impact Study of First Tee Youth Programming

National nonprofit organization First Tee turns to researchers at Youth-Nex to evaluate the program’s model and youth development outcomes during out-of-school time.

Leslie Booren

August 14, 2024

(Photo contributed by First Tee.)

It is estimated that 10.2 million young people participate in out-of-school time (OST) activities including sports programs. Researchers at Youth-Nex will be partnering with First Tee, the national youth development organization, to evaluate their youth programming.

First Tee began in 1997 as a partnership among the LPGA, the Masters Tournament, the PGA of America, the PGA TOUR, and the US Golf Association (USGA) to make golf affordable for and accessible to all kids. The First Tee model introduces the game of golf and includes a life skills curriculum that supports the development of character strengths built through the game of golf, including positive self-identity, using good judgment and collaborating with others.

“We are excited to engage in a collaborative co-design process with First Tee,” said Ashlee Sjogren, a research assistant professor and principal investigator of the project. “We hope to better understand how the First Tee model relates to participant outcomes by examining metrics like program attendance, dosage, engagement, and frequency.”

The proposed study is an outcome evaluation that utilizes mixed methods research to investigate the model and associated outcomes for youth, like relationship quality and academic achievement.

Nancy Deutsch, director of Youth-Nex and the associate dean for faculty affairs at the School of Education and Human Development, is a co-principal investigator of this study and was part of the first impact study of the First Tee programming in 2005, also conducted at the University of Virginia.

“There is deep expertise in youth development, mentorship, and OST programming here at Youth-Nex,” Sjogren said. “This will be extremely valuable to both support and validate the work First Tee is doing across the United States with young people.”

This project is expected to continue into the Spring of 2026.

News Information

Media contact.

Audrey Breen

[email protected]

Research Center or Department

Featured faculty.

• Ashlee Lester Sjogren
• Nancy L. Deutsch

In the Field

News Topics

• Child & Adolescent Development

Related News

Q&A: Understanding the Emotions of Adolescence, From Movies to Real Life

Q&A: Social Media’s Impact on Teens Can Be Both Good and Bad

Boredom Can Be Great for Kids

Honoring Indigenous Childhood in Haiti