Why Do Teachers Matter? A Meta-Analytic Review of how Teacher Characteristics and Competencies Affect Students' Academic Achievement

Abstract

Key Aspects

• Research Focus and Scope: This meta-analysis investigates the impact of teacher characteristics and competencies on the academic performance of secondary school students. It synthesizes data from 40 studies conducted in the first two decades of the 21st century, encompassing a total of 202 effect sizes. The study focuses on secondary education and aims to quantify the overall relationship and identify specific influential factors.
• Statistical Methodology: The meta-analysis employs Fisher's transformation to estimate effect sizes and utilizes a random-effects model to account for heterogeneity among the included studies. This approach allows for a more robust and generalizable estimation of the overall effect. The use of a random-effects model acknowledges that the true effect may vary across different studies and contexts.
• Main Findings and Key Variables: The results indicate that teacher characteristics and competencies explain 9.2% of the variance in secondary school students' academic performance. This overall effect is considered moderate in magnitude. However, the study also reveals that certain teacher characteristics, such as reflective attitude, professional development, and teaching self-efficacy, exhibit larger effect sizes, suggesting their greater importance.
• Context and Significance: The abstract sets the stage for the study by referencing the Coleman report and subsequent research on effective schools and the importance of teacher characteristics. It highlights the ongoing debate and research surrounding teacher evaluation and its potential to improve educational quality. This contextualization establishes the significance of the meta-analysis within the broader field of educational research.

Strengths

• Clear Research Objective

  The abstract clearly states the research objective, which is to conduct a meta-analysis examining the relationship between teacher characteristics/competencies and secondary school students' academic performance.

  "This article presents a meta-analysis of 40 studies conducted during the first two decades of the 21st Century that have analyzed the relationship between teacher characteristics and compe- tencies and the academic performance of secondary school students, which contributed a total of 202 effects." (Page 1)
• Concise Methodological Summary

  The abstract concisely summarizes the methodology, including the number of studies, effect size estimation (Fisher's transformation), and the statistical model (random-effects).

  "The effect sizes were estimated using Fisher’s transformation and a random-effects model was proposed to study the summary effect." (Page 1)
• Quantified Main Finding

  The abstract presents the main finding, quantifying the overall effect of teacher characteristics and competencies on student performance.

  "The results show that teachers’ characteris- tics and competencies explain 9.2% of the differences in students’ performance." (Page 1)
• Highlights Specific Influential Factors

  It highlights specific teacher characteristics with larger effect sizes, providing valuable insights for targeted interventions.

  "Although the overall effect can be considered as moderate, a closer examination of specific teacher charac- teristics and competencies revealed some larger effect sizes. This is the case of teachers’ reflective attitude, their professional development, and teaching self-efficacy." (Page 1)

Suggestions for Improvement

• Specify Databases Searched

  High impact. This would enhance the completeness and informativeness of the abstract, providing readers with a more comprehensive understanding of the meta-analysis's scope. This is crucial for an abstract as it serves as a concise overview of the entire study.

  "This article presents a meta-analysis of 40 studies conducted during the first two decades of the 21st Century that have analyzed the relationship between teacher characteristics and compe- tencies and the academic performance of secondary school students, which contributed a total of 202 effects." (Page 1)

  Implementation: Include a brief phrase or sentence indicating the databases searched (e.g., "searching databases such as Web of Science, Scopus, ERIC, and APA PsycInfo").

• Include Search Date Range

  Medium impact. Including the date range would improve the clarity and reproducibility of the study. The abstract serves as a standalone summary, and this information helps readers quickly assess the study's temporal scope.

  "This article presents a meta-analysis of 40 studies conducted during the first two decades of the 21st Century that have analyzed the relationship between teacher characteristics and compe- tencies and the academic performance of secondary school students, which contributed a total of 202 effects." (Page 1)

  Implementation: Add a phrase specifying the search date range, such as "conducted between January 2000 and December 2019."

• Explicitly State "Secondary School Students"

  Low impact. This is a minor addition, but explicitly stating "secondary school students" reinforces the study's population and helps avoid ambiguity. This aligns with the abstract's purpose of clearly and precisely conveying key study details.

  "The results show that teachers’ characteris- tics and competencies explain 9.2% of the differences in students’ performance." (Page 1)

  Implementation: Slightly rephrase the existing sentence to explicitly include "secondary school students": "...explain 9.2% of the differences in *secondary school* students' performance."

Introduction

Key Aspects

• Contextual Background and Significance: The introduction establishes the context of the study by highlighting the importance of identifying factors that characterize effective schools, particularly focusing on teacher characteristics and professional competencies as key determinants of educational outcomes. This builds upon the foundation laid by the Coleman report and subsequent research, emphasizing the ongoing significance of teacher evaluation in improving educational quality.
• Definitions of Key Terms: The introduction clearly defines 'teacher characteristics' as attributes or qualities that influence teachers' beliefs, attitudes, and teaching practices, distinguishing between sociodemographic, acquired, and psychological characteristics. It also defines 'professional competencies' as the combination of knowledge, skills, attitudes, and behaviors necessary for effective teaching, differentiating between teaching/pedagogical and personal competencies.
• Literature Review and Identification of Knowledge Gaps: A comprehensive review of existing literature, including meta-analyses and systematic reviews, is presented. This review highlights the findings of previous studies on the relationship between teacher characteristics/competencies and student academic achievement, identifying both consistent findings (e.g., the positive impact of teacher training, knowledge, and experience) and inconsistencies (e.g., the effect of sociodemographic characteristics).
• Rationale for the Current Study: The introduction identifies specific knowledge gaps in the existing literature, such as the lack of meta-analyses comparing the influence of individual teacher variables and the limited exploration of teacher characteristics and competencies in depth. This establishes the rationale for the current meta-analysis.
• Research Purpose and Questions: The research purpose is clearly stated: to address the identified knowledge gaps by identifying the global effects of teacher characteristics and competencies on students' academic achievement and by studying and comparing the effects of individual variables. Three specific research questions are posed, focusing on which teacher characteristics/competencies affect academic performance, the strength of the association, and the moderating effect of study characteristics.
• Scope and Moderator Variables: The introduction specifies the scope of the meta-analysis, focusing on studies conducted in the first two decades of the 21st century and on secondary school students. It also mentions the consideration of moderator variables, such as the country's Human Development Index (HDI), educational level, and study design characteristics.

Strengths

• Strong Contextual Background

  The introduction effectively establishes the context by referencing the Coleman report and subsequent research on effective schools, highlighting the importance of teacher characteristics and competencies.

  "Publication of the renowned Coleman report (Coleman et al., 1966) marked the onset of a productive line of research aimed at identifying the factors that characterize effective schools; that is, those schools that contribute to the students overall development to a greater extent than would be expected based on their previous academic performance, or their personal and family characteristics, at the same time contributing to the progress of the educational community as a whole (Mu˜noz-Repiso & Murillo, 2010)." (Page 1)
• Clear Definitions of Key Terms

  The introduction clearly defines key terms, such as 'teacher characteristics' and 'professional competencies,' providing a framework for the subsequent analysis.

  "Teacher characteristics refer to the attributes or qualities of teachers that explain their beliefs, attitudes, and teaching practices (Organisation for Economic Cooperation and Development [OECD], 2009, 2020; Zhang, 2006)." (Page 1)
• Comprehensive Literature Review

  The introduction comprehensively reviews relevant literature, including meta-analyses and systematic reviews, to identify existing knowledge gaps and justify the need for the current study.

  "Clearly, therefore, numerous empirical and review studies published in recent years have focused on analyzing the relationship between teacher competencies and characteristics and students academic achievement. However, a review of the literature also re- veals some knowledge gaps that must be addressed." (Page 2)
• Explicit Research Purpose and Questions

  The introduction explicitly states the research purpose and questions, providing a clear focus for the meta-analysis.

  "The purpose of the present meta-analysis, therefore, is to address these knowledge gaps, first, by identifying the global effects of teacher characteristics and competences on students academic achievements and, second, by studying and comparing the effects of the individual variables that constitute these characteristics and competences." (Page 2)

Suggestions for Improvement

• Explicitly Connect to the Abstract

  Low impact. The connection to the abstract is already strong, but explicitly mentioning that this section expands on the abstract's overview would further solidify the link. This is a minor stylistic improvement that reinforces the logical flow between sections.

  "1. Introduction This article presents a meta-analysis of 40 studies conducted during the first two decades of the 21st Century that have analyzed the relationship between teacher characteristics and compe- tencies and the academic performance of secondary school students, which contributed a total of 202 effects." (Page 1)

  Implementation: Add a sentence at the beginning of the introduction, such as: "Building upon the overview presented in the abstract, this introduction provides a more in-depth examination of the existing literature, identifies key knowledge gaps, and outlines the specific research questions addressed in this meta-analysis."

• Justify the Focus on Secondary School Students

  Medium impact. While the introduction mentions the focus on secondary school students, explicitly stating the rationale for this focus would strengthen the study's scope and justification. This is important for the Introduction section as it sets the boundaries of the research.

  "To this end, we have conducted a quantitative synthesis of the empirical studies that have analyzed the effect of teacher characteristics and competencies on the academic performance of secondary school students. Our study specifically focuses on" (Page 2)

  Implementation: Add a sentence or two explaining the rationale for focusing on secondary school students. For example: "This meta-analysis focuses specifically on secondary school students because this developmental stage is crucial for academic achievement and future educational attainment. Furthermore, existing research suggests potential differences in the impact of teacher characteristics across different educational levels, warranting a focused examination of the secondary level."

• Provide a Roadmap of the Paper's Structure

  Low impact. While the research questions are stated, adding a brief overview of the subsequent sections would enhance the introduction's roadmap function. This is a minor structural improvement that aids reader navigation.

  "In summary, through this meta-analysis we aim to answer the following questions: (1) Which teacher characteristics and com- petencies affect the academic performance of secondary school students? (2) How strong is the association between teacher charac- teristics and competencies and academic achievement? and (3) To what extent do the characteristics of the studies moderate the effect of this relationship?" (Page 2)

  Implementation: Add a final paragraph to the introduction that briefly outlines the structure of the paper. For example: "The remainder of this paper is organized as follows. Section 2 details the methodology employed in this meta-analysis, including the search strategy, inclusion and exclusion criteria, and data analysis procedures. Section 3 presents the results of the meta-analysis, addressing the research questions outlined above. Section 4 discusses the findings, considers their implications, and identifies limitations and future research directions."

Method

Key Aspects

• Systematic Review and Search Strategy: The study employed a systematic review and meta-analysis methodology, adhering to the PRISMA guidelines. This involved a comprehensive search of major international databases (Web of Science, Scopus, ERIC, and APA PsycInfo) using a three-level structural equation with specific search terms related to teacher competencies, academic performance, and secondary education.
• Inclusion and Exclusion Criteria: Strict inclusion and exclusion criteria were applied to select relevant studies. These criteria focused on the influence of teacher characteristics and competencies on secondary school students' academic achievement, empirical studies with quantitative or mixed designs, articles published in peer-reviewed journals, and publications in English or Spanish.
• Study Selection Process: The study selection process involved multiple stages, including removing duplicates, screening titles and abstracts, and assessing full-text articles for eligibility. This process was conducted by multiple authors to minimize selection errors and ensure objectivity, with a high level of agreement reached between independent reviewers.
• Coding of Variables: A detailed coding protocol was developed to extract relevant information from the selected articles. This included variables related to teacher characteristics and competencies (categorized into general descriptions, sociodemographic, acquired, psychological, teaching, and personal competencies), the source of information for the independent variable, measures of academic achievement, effect sizes, and sample characteristics.
• Effect Size Estimation and Statistical Modeling: Effect sizes were estimated using Fisher's transformation of correlation coefficients. A multilevel random-effects model was employed to account for the non-independence of effect sizes (multiple effect sizes within studies) and to estimate the overall effect. Robust variance estimation (RVE) was used to adjust the estimations.
• Moderator Analyses: Moderator analyses were conducted using a multilevel mixed-effects model to examine the influence of various study characteristics on the effect sizes. These moderator variables included the country's Human Development Index (HDI), educational level, type of academic achievement measure, subject tested, type of effect size reported, and the source of information for the independent variable.
• Assessment of Publication Bias: Publication bias was assessed using multiple methods, including funnel plots with the "trim and fill" procedure, rank correlation tests, fail-safe number calculations, and analysis of effect sizes relative to the quality of the journal (impact factor).

Strengths

• Adherence to PRISMA Guidelines

  The Method section clearly outlines the systematic review process, adhering to PRISMA guidelines, which enhances transparency and reproducibility.

  "This systematic review was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and taking into account the recommended control mechanisms to reduce risk of bias (Liberati et al., 2009; Moher et al., 2009)." (Page 3)
• Comprehensive Search Process Description

  The search process is comprehensively described, detailing the databases used, search terms, and search strategy, allowing for replication.

  "The systematic review process to identify relevant articles for the study was conducted by searching in the main international databases of multidisciplinary coverage, such as the core collection of the Web of Science and Scopus. The international databases ERIC and APA PsycInfo, specialized in Education and Psychology, respectively, were also consulted through the EBSCOhost platform. Searches of these databases were conducted using a three-level structural equation." (Page 3)
• Explicit Eligibility Criteria

  The eligibility criteria are explicitly stated, providing clear inclusion and exclusion criteria for study selection, ensuring objectivity and minimizing bias.

  "The studies identified in the previous phase were selected in accordance with our need for information. Therefore, those chosen to be included in the meta-analysis fulfilled the following inclusion criteria: 1. Topic: the influence of teacher characteristics and competencies on students academic achievement... 2. Type of study: empirical. 3. Study design: quantitative or mixed. 4. Population type: secondary school students... 5. Document type: article published in peer-reviewed journals. 6. Language: English ... or Spanish..." (Page 4)
• Meticulous Selection Process

  The selection process is meticulously documented, including the steps taken to exclude duplicates, screen articles, and assess full texts, ensuring transparency and minimizing selection bias.

  "The first step in the process to select articles that met the inclusion criteria was to exclude duplicates of studies and those published in a language other than English and Spanish, or in a format that did not correspond to a scientific article. This first screening was conducted by examining the metadata in the documents. After that, a screening of the first 1,453 articles based on the title and the abstract was conducted by one author...Then, two of the authors examined the complete texts of the 104 records selected, resulting in the exclusion of 64 articles, and the final inclusion of 40 articles with 203 effect sizes." (Page 4)
• Thorough Coding of Variables

  The coding of variables is thoroughly described, specifying the information extracted from each article, including independent and dependent variables, sample characteristics, and effect size data, promoting consistency and reliability.

  "For the synthesis of data from the selected articles, a codification protocol was created that specified all the information to be extracted from the articles. During this step, two researchers independently recorded the information from each article." (Page 4)
• Clear Data Analysis Procedures

  The data analysis procedures are clearly explained, including the use of Fisher's transformation, the random-effects model, robust variance estimation, and the handling of non-independent effect sizes, ensuring methodological rigor.

  "The effect sizes were estimated by applying Fisher's transformation to the correlation coefficients, so that the standard error and the 95% confidence interval around the effect size could be estimated...Taking into account the non-independence of individual effect sizes (a single study could have several effects), the following procedure was proposed to estimate the overall effect size. Firstly, a multilevel random-effects model was estimated...Secondly, estimations were adjusted by applying the robust variance estimation (RVE) method." (Page 6)
• Comprehensive Assessment of Publication Bias

  The section addresses the critical issue of publication bias using multiple methods, including funnel plots, rank correlation tests, and fail-safe number calculations, demonstrating a commitment to assessing the robustness of the findings.

  "Publication bias was evaluated by different procedures. Firstly, funnel plots were constructed with the "trim and fill" procedure proposed by Duval and Tweedie (2000)...The rank correlation test introduced by Begg and Mazumdar (1994) was also used...The robustness of the results obtained have also been estimated with the fail-safe number." (Page 6)

Suggestions for Improvement

• Explicitly State Boolean Operators

  Low impact. Although the search process is well-described, explicitly mentioning the use of Boolean operators (AND, OR) would further enhance clarity and reproducibility. The Methods section benefits from complete transparency in all procedures.

  "Searches of these databases were conducted using a three-level structural equation. Firstly, terms were included to locate teachers’ competencies (teach* NEAR/1 (“competenc*” OR “professional* competenc*” OR “characteristic*” OR “effecti*” OR “quali*” OR “skill*” OR “abilit*”)." (Page 3)

  Implementation: Include a sentence clarifying the use of Boolean operators within the three-level structural equation. For example: 'The three levels of the search equation were combined using the Boolean operator AND, while terms within each level were combined using OR and proximity operators.'

• Report Inter-rater Reliability for Coding

  Medium impact. While inter-rater reliability is mentioned in the selection process (93.5% agreement), reporting the inter-rater reliability for the coding of variables would strengthen the methodological rigor. The Methods section is where such quality control measures are reported.

  "For the synthesis of data from the selected articles, a codification protocol was created that specified all the information to be extracted from the articles. During this step, two researchers independently recorded the information from each article." (Page 4)

  Implementation: Calculate and report a measure of inter-rater reliability (e.g., Cohen's kappa) for the coding of variables. For example: 'Inter-rater reliability for the coding of variables was assessed using Cohen's kappa, resulting in a value of [insert kappa value], indicating [insert interpretation of kappa value] agreement.'

• Elaborate on the Multilevel Random-Effects Model

  Low impact. While the method for handling non-independent effect sizes is mentioned, providing a more detailed explanation of the multilevel random-effects model would enhance clarity, particularly for readers less familiar with this statistical technique. The Methods section should provide sufficient detail for understanding all analyses.

  "Taking into account the non-independence of individual effect sizes (a single study could have several effects), the following procedure was proposed to estimate the overall effect size. Firstly, a multilevel random-effects model was estimated using the “rma. vm” function of the metafor package (Viechtbauer, 2010) of the R software (version 4.2.1)." (Page 6)

  Implementation: Expand the explanation of the multilevel random-effects model. For example: 'A multilevel random-effects model was used to account for the nested structure of the data, where multiple effect sizes were nested within individual studies. This model allows for variation in effect sizes both within and between studies, providing a more accurate estimate of the overall effect and its uncertainty.'

• Specify All R Packages Used

  Medium impact. While the section mentions the use of R software, providing the specific packages used for each analysis (beyond 'metafor' and 'clubSandwich') would enhance reproducibility. The Methods section should allow for complete replication of the analysis.

  "Firstly, a multilevel random-effects model was estimated using the “rma. vm” function of the metafor package (Viechtbauer, 2010) of the R software (version 4.2.1). Secondly, estimations were adjusted by applying the robust variance estimation (RVE) method. To do this, the “coef_test” function from the clubSandwich package was used (Tipton & Pustejovsky, 2015) in R." (Page 6)

  Implementation: List all relevant R packages used for data analysis. For example: 'Data analysis was conducted using R (version 4.2.1) with the following packages: 'metafor' (Viechtbauer, 2010) for meta-analysis, 'clubSandwich' (Tipton & Pustejovsky, 2015) for robust variance estimation, [add any other packages used, e.g., 'dplyr' for data manipulation, 'ggplot2' for visualization].'

Non-Text Elements

Fig. 1. Flow diagram of study selection process.

Figure/Table Image (Page 5)

First Reference in Text

Following the PRISMA guidelines (Liberati et al., 2009; Moher et al., 2009), Fig. 1 shows the flow diagram of the entire selection process.

Description

• Overview of the Flow Diagram: The flow diagram illustrates the process used to select studies for a meta-analysis. It starts with an initial pool of 'records identified through database searching' which amounts to 2042 articles, and zero additional records from other sources. Then, the diagram shows how this pool is reduced step-by-step as the researchers apply specific criteria to determine which studies are suitable for inclusion in the final meta-analysis. The process includes removing duplicates and articles in other languages, screening records based on title and abstract, assessing full-text articles for eligibility, and finally, including studies in qualitative and quantitative synthesis (meta-analysis). The diagram also indicates the number of records excluded at each stage, along with the reasons for exclusion.
• Initial Screening: The diagram shows that after the initial search, 1453 records remained after duplicates, editorials, and other languages were removed. This indicates that a significant portion of the initial records were either duplicates or did not meet the language or format criteria.
• Title/Abstract Screening: Following the screening of titles and abstracts, 1349 records were excluded, leaving 104 full-text articles to be assessed for eligibility. The primary reason for exclusion at this stage was topic irrelevance, accounting for 1206 exclusions.
• Full-Text Assessment: After assessing the full-text articles, 64 were excluded, and the final quantitative synthesis (meta-analysis) included 40 studies. The main reasons for exclusion at this stage were no access to complete text (3), results published previously (2), DV different to academic achievement (10), IV different to teacher’s characteristic/competence (5), and do not provide correlations or standardized beta coefficient (44).

Scientific Validity

• Adherence to PRISMA Guidelines: The flow diagram adheres to the PRISMA guidelines, which are an evidence-based set of items for reporting in systematic reviews and meta-analyses. This ensures that the study selection process is transparent and reproducible.
• Audit Trail: The diagram provides a clear audit trail of the study selection process, which is crucial for assessing the potential for selection bias. By documenting the number of records excluded at each stage and the reasons for exclusion, the authors provide a clear rationale for their final sample of studies.
• Exclusion Categories: The categories for reasons of exclusion appear comprehensive, covering the key reasons why studies might be excluded from a meta-analysis (e.g., topic irrelevance, study design, data availability). However, the "DV different to academic achievement" and "IV different to teacher's characteristic/competence" categories could potentially be more granular to provide further insight into the types of studies excluded.

Communication

• Overall Clarity: The flow diagram clearly presents the study selection process, which enhances the transparency and reproducibility of the meta-analysis. The use of PRISMA guidelines is explicitly mentioned, providing context for the diagram's structure. The diagram is easy to follow, with clear labels and sequential steps.
• Quantitative Information: The diagram effectively communicates the number of records at each stage of the selection process (e.g., records identified, records screened, full-text articles assessed). This quantitative data provides a clear understanding of the attrition rate and the reasons for exclusion.
• Reasons for Exclusion: The reasons for excluding studies at each stage are clearly listed, which enhances the transparency of the selection process. This allows readers to understand the criteria used to select studies for inclusion in the meta-analysis.

Results

Key Aspects

• Overall Mean Effect Size: The overall mean effect size for the 202 effect sizes included in the meta-analysis is 0.313 (R2 = 0.092), which is statistically significant (Z = 6.89, p < .001) and considered a medium effect size according to Cohen's (1992) criteria. This indicates that, overall, teacher characteristics and competencies have a moderate positive association with student academic achievement.
• Heterogeneity of Effect Sizes: The study found high heterogeneity among the effect sizes, indicating substantial variability in the relationship between teacher characteristics/competencies and student achievement across different studies. This heterogeneity is visually represented in a forest plot (Figure 2) and is further explored through moderator analyses.
• Effect Sizes for Specific Teacher Characteristics and Competencies: Table 1 presents a detailed breakdown of mean effect sizes for different categories of teacher characteristics and competencies. The largest effect is observed for studies providing a general assessment of teaching quality or professional skills (Zr = 0.394, R2 = 0.140). Other notable effects are found for teachers' psychological characteristics (Zr = 0.339, R2 = 0.107) and acquired characteristics (Zr = 0.304, R2 = 0.087). Among specific variables, teachers' reflective attitude (Zr = 0.581, R2 = 0.274), professional development (Zr = 0.426, R2 = 0.162), and self-efficacy (Zr = 0.386, R2 = 0.142) show the largest effects.
• Moderator Analysis Results: Moderator analyses (Table 2) reveal statistically significant differences in effect sizes based on several study characteristics. The effect of teacher characteristics and competencies is greater in countries with a lower Human Development Index (HDI) (Zr = 0.443) compared to countries with a higher HDI (Zr = 0.256). The effect is also larger for studies reporting correlations (Zr = 0.421) compared to those reporting standardized beta coefficients (Zr = 0.114). The curricular area assessed also moderates the effect, with the largest effect found for general academic achievement (Zr = 0.564).
• Publication Bias Assessment: Publication bias was assessed using multiple methods, including funnel plots (Figure 3), rank correlation tests, and fail-safe number calculations. The results suggest a lack of substantial publication bias. The funnel plots show a relatively symmetrical distribution, the rank correlation test is non-significant (Tau = 0.044, p = .36), and the fail-safe number is very large (980,894).
• Effect Sizes and Journal Quality: Analysis of effect sizes relative to the quality of the publication (Figure 4) shows that studies published in journals not indexed in JCR or SJR report the highest mean effect size (0.52), followed by SJR-indexed journals (0.35) and JCR-indexed journals (0.22). However, these differences are not statistically significant.

Strengths

• Clear Presentation of Overall Effect Size

  The Results section clearly presents the overall mean effect size (0.313) of teacher characteristics and competencies on student academic achievement, providing a quantifiable measure of the relationship.

  "The overall mean for the 202 effect sizes included in the sample is 0.313 (R2 = 0.092)." (Page 7)
• Detailed Breakdown of Effect Sizes

  The section provides a detailed breakdown of effect sizes for different categories of teacher characteristics and competencies (both overall dimensions and specific variables), allowing for a nuanced understanding of their relative importance.

  "Table 1 shows the mean effect size of teacher characteristics and competencies on students academic achievement, both globally and broken down into the general and specific categories in which the independent variable is codified." (Page 8)
• Inclusion of Moderator Analyses

  The Results section reports the results of moderator analyses, examining the influence of various study characteristics (e.g., country HDI, educational level, type of measure) on the effect sizes, providing insights into potential contextual factors.

  "In an attempt to try to explain the high heterogeneity observed, the possible differences in effect sizes have been analysed, broken down by the different moderator variables..." (Page 9)
• Assessment of Publication Bias

  The section addresses the issue of publication bias using multiple methods (funnel plots, rank correlation, fail-safe number), demonstrating a commitment to assessing the robustness of the findings.

  "The funnel plots represented in Fig. 3 reflect a similar distribution to the effect sizes reported by studies conducted on larger, and those carried out on smaller samples...Other evidence to support the lack of publication bias is provided by the rank correlation of Begg and Mazumdar...In the third place, the fail-safe number was examined..." (Page 10)
• Use of Visual Representations

  The section uses figures (forest plot, funnel plots, and effect size plots) to visually represent the data, enhancing clarity and facilitating understanding.

  "This is reflected in the forest plot (Fig. 2) that represents the 20 largest positive effects and the 20 largest negative effects, together with the confidence interval in each case." (Page 7)
• Objective Presentation of Results

  The section presents results in an objective and factual manner, avoiding interpretation or discussion, which is appropriate for a Results section.

  "3. Results" (Page 6)

Suggestions for Improvement

• Introduce and Explain Tables

  Medium impact. This would improve the clarity and flow of the results, particularly for readers who may not be familiar with all the abbreviations and statistical terms used in the tables. The Results section should be as self-contained and understandable as possible.

  "Table 1 Mean Effect Size for Teacher Characteristics and Competencies on Academic Output." (Page 8)

  Implementation: Include a brief introductory paragraph before presenting Table 1 and Table 2 that explains the structure of the tables and defines any abbreviations or statistical terms used (e.g., m, k, Zr, SE, CI). For example: "Table 1 presents the mean effect sizes (Zr) for teacher characteristics and competencies, along with their standard errors (SE) and 95% confidence intervals (CI). The number of studies (m) and effect sizes (k) contributing to each mean effect size are also reported."

• State the Null Hypothesis for the Wald Test

  Low impact. While the text mentions the Wald test, explicitly stating the null hypothesis being tested would enhance clarity and methodological rigor. The Results section should clearly state the statistical tests performed and their purpose.

  "Finally, in spite of the differences in effect sizes estimated for each of the teacher characteristics or competencies measured, the results of the Wald test for both cases (overall dimensions and specific variables) show that these differences are not statistically significant." (Page 8)

  Implementation: Add a sentence clarifying the null hypothesis for the Wald test used in the moderator analyses. For example: "The Wald test was used to test the null hypothesis that there were no significant differences in effect sizes between the different levels of each moderator variable."

• Report All Test Statistics

  High impact. While the tables present the statistical significance (p-values), reporting the actual test statistics (e.g., Z-values, F-values) for all comparisons would provide a more complete picture of the results and allow for easier comparison with other studies. The Results section is the primary location for reporting all statistical findings.

  "Z-test p-value" (Page 8)

  Implementation: Include the actual Z-values for the overall effect size and the specific teacher characteristics in Table 1, and the F-values and degrees of freedom for the moderator analyses in Table 2, in addition to the p-values. Ensure consistency in reporting (e.g., always report degrees of freedom for F-tests).

• Include a Complete Forest Plot

  Medium impact. While Figure 2 presents a forest plot of the 20 largest positive and negative effects, including a forest plot of *all* effect sizes (perhaps in an appendix or supplementary material) would provide a more complete visual representation of the data distribution and heterogeneity. The Results section, or its associated appendices, should provide a complete record of the data.

  "This is reflected in the forest plot (Fig. 2) that represents the 20 largest positive effects and the 20 largest negative effects, together with the confidence interval in each case." (Page 7)

  Implementation: Create a forest plot showing all 202 effect sizes and include it as an appendix or supplementary material. Refer to this appendix in the main text when discussing heterogeneity.

• Visualize Moderator Analysis Results

  Low impact. While Table 2 reports the results of the moderator analyses, providing a visual representation of these results (e.g., a forest plot grouped by moderator levels) could enhance understanding and facilitate comparison. The Results section can benefit from visual aids to summarize complex data.

  "Table 2 Moderator Analyses for the Effects of Teacher Characteristics and Competencies on Academic Performance." (Page 9)

  Implementation: Create a figure (e.g., a forest plot) that visually represents the mean effect sizes and confidence intervals for each level of the significant moderator variables. This could be included in the main text or as an appendix.

Non-Text Elements

Fig. 2. Fisher's transformation and confidence interval for studies that report...

Full Caption

Fig. 2. Fisher's transformation and confidence interval for studies that report greater and smaller effect sizes.

Figure/Table Image (Page 7)

First Reference in Text

This is reflected in the forest plot (Fig. 2) that represents the 20 largest positive effects and the 20 largest negative effects, together with the confidence interval in each case.

Description

• Overall Structure and Purpose: This figure is a forest plot, which is a graphical way to show the results of multiple scientific studies that address the same question. In this case, it shows the effects of teacher characteristics and competencies on student achievement. Instead of showing all the studies, this plot specifically highlights the 20 studies that found the largest positive effects (meaning teacher qualities had a big, beneficial impact) and the 20 studies that found the largest negative effects (meaning teacher qualities had a big, detrimental impact). The x-axis represents the effect size, which is a number that indicates the strength and direction of the relationship between teacher characteristics and student outcomes. The effect sizes have been transformed using Fisher's transformation, a statistical technique used to make the distribution of correlation coefficients more normal, which is important for meta-analysis. Each study is represented by a horizontal line, with the dot in the middle indicating the effect size and the line representing the confidence interval, which is a range of values within which the true effect size is likely to fall.
• Interpretation of Horizontal Lines: Each horizontal line in the plot represents a single study. The position of the line on the x-axis indicates the study's effect size. Studies to the right of zero suggest a positive effect (teacher characteristics improving student achievement), while studies to the left of zero suggest a negative effect (teacher characteristics hindering student achievement).
• Fisher's Transformation: The caption notes that a 'Fisher's transformation' has been applied. Fisher's z-transformation converts correlation coefficients (a measure of the strength and direction of a relationship between two variables) into z-scores, which are easier to work with in statistical analyses. This transformation is particularly useful when performing a meta-analysis, as it helps to normalize the distribution of correlation coefficients, making statistical inferences more accurate.
• Confidence Intervals: Each study also has a confidence interval. A confidence interval (CI) provides a range of values within which the true effect size is likely to fall. Narrower confidence intervals indicate more precise estimates, while wider intervals indicate less precise estimates. If the confidence interval crosses zero, it means that the study's results are not statistically significant, as the true effect size could be zero (i.e., no effect).

Scientific Validity

• Potential for Bias: Presenting only the largest positive and negative effects can be misleading, as it does not provide a complete picture of the distribution of effect sizes. This approach may overemphasize extreme findings and underrepresent the central tendency of the data. A standard forest plot showing all effect sizes would be more informative.
• Clarity on Transformation: While the figure caption mentions Fisher's transformation, it does not explicitly state whether the confidence intervals are also based on this transformation. It is important to clarify this, as the interpretation of confidence intervals depends on the scale used.
• Justification for Selection: The decision to present only the extreme effects should be justified. If the purpose is to illustrate the heterogeneity (variability) of the effect sizes, this should be explicitly stated. However, a more comprehensive analysis of heterogeneity, such as examining the I-squared statistic and conducting subgroup analyses, would be more appropriate.

Communication

• Visual Representation of Effect Sizes: The forest plot provides a visual representation of the range of effect sizes observed in the included studies. By focusing on the 20 largest positive and 20 largest negative effects, the plot highlights the extreme ends of the distribution, which may be useful for illustrating the variability in the data. However, it omits the majority of effect sizes, potentially skewing the reader's perception of the overall effect.
• Confidence Intervals: The inclusion of confidence intervals provides information about the precision of the effect size estimates for each study. Wider confidence intervals indicate less precise estimates, while narrower intervals indicate more precise estimates. This helps readers to assess the reliability of the individual study findings.
• Study Labels: The study labels are somewhat truncated, making it difficult to identify the full study citation. The plot could benefit from a clearer labeling system, such as using a numerical index to refer to a table with full study details.
• Caption Clarity: The caption is reasonably informative, but it could be improved by explicitly stating that the plot shows a selection of the largest positive and negative effects, rather than all effects.

Table 1 Mean Effect Size for Teacher Characteristics and Competencies on...

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Table 1 Mean Effect Size for Teacher Characteristics and Competencies on Academic Output.

Figure/Table Image (Page 8)

First Reference in Text

Bearing this in mind, Table 1 shows the mean effect size of teacher characteristics and competencies on students' academic achievement, both globally and broken down into the general and specific categories in which the independent variable is codified.

Description

• Global: This table presents a summary of how much different qualities and skills of teachers affect students' academic performance. It's organized into rows and columns, where each row represents a specific teacher characteristic or competence (like 'teaching competencies' or 'self-efficacy'), and the columns show different statistical measures that help us understand the effect size. The table shows the overall effect (
• Categorization of Variables: The table breaks down the analysis into different categories of teacher characteristics and competencies. It distinguishes between 'Type of teacher characteristic or competence (Overall dimensions)' and 'Type of teacher characteristic or competence (Specific variables)'. The 'Overall dimensions' include broader categories like 'General description', 'Acquired characteristics', and 'Psychological characteristics'. The 'Specific variables' section provides a more detailed breakdown, such as 'Initial training', 'Professional experience', and 'Self-efficacy'.
• Key Statistics: For each characteristic or competence, the table provides several key statistics. 'm' represents the number of studies that examined that particular characteristic. 'k' represents the number of effect sizes included for that characteristic. The effect size is a way to quantify the size of the effect, that is, the magnitude of the impact that the teacher characteristic has on the student's academic output. 'Zr' is Fisher's Z, a transformed correlation coefficient used to stabilize the variance and allow for more accurate statistical analysis. 'SE' is the standard error, which measures the precision of the estimated effect size. '95% CI' is the 95% confidence interval, which provides a range of values within which the true effect size is likely to fall. 'Z-test' is the test statistic for a Z-test, which is used to determine the statistical significance of the effect size. 'p-value' is the probability of observing the obtained results (or more extreme results) if there is no true effect.
• Variance Components: The table also includes 'σₘ' and 'σₑ'. 'σₘ' represents the variance between studies, indicating the heterogeneity (variability) of the effect sizes across different studies. 'σₑ' represents the variance between effect sizes, indicating the variability of the effect sizes within studies.
• Significance Levels: The table includes significance levels indicated by asterisks: *Statistically significant at confidence level of 90%, ** 95%, *** 99%.

Scientific Validity

• Statistical Rigor: The table presents key statistics for each category and variable, including the number of studies (m), number of effect sizes (k), mean effect size (Zr), standard error (SE), confidence interval (CI), Z-test statistic, and p-value. This allows readers to assess the statistical significance and precision of the results.
• Heterogeneity: The inclusion of variance components (σₘ and σₑ) provides information about the heterogeneity of the effect sizes. This is important for interpreting the results, as high heterogeneity may suggest that the effect sizes vary across different contexts or populations.
• Effect Size Measure: The table uses Fisher's Z transformation (Zr) as the measure of effect size. While this is a common practice in meta-analysis, it is important to consider the limitations of this transformation and whether it is appropriate for the data.
• Sample Size Threshold: The table includes a note cautioning readers about interpreting subcategories with effect sizes derived from less than five studies. This is a good practice, as estimates from small samples can be unreliable. However, it would be helpful to provide a more specific rationale for this threshold.

Communication

• Overall Summary: The table effectively summarizes the mean effect sizes for different categories of teacher characteristics and competencies. The breakdown into overall dimensions and specific variables provides a comprehensive overview of the findings. The inclusion of confidence intervals and p-values allows readers to assess the statistical significance of the results.
• Readability: The use of clear and concise labels for each category and variable enhances the readability of the table. The footnotes provide helpful explanations of the abbreviations used, such as 'CI' for confidence interval.
• Caveats: The table includes a note cautioning readers about interpreting subcategories with effect sizes derived from less than five studies. This is a good practice, as estimates from small samples can be unreliable.

Table 2 Moderator Analyses for the Effects of Teacher Characteristics and...

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Table 2 Moderator Analyses for the Effects of Teacher Characteristics and Competencies on Academic Performance.

Figure/Table Image (Page 9)

First Reference in Text

The results of the moderator analysis are included in Table 2.

Description

• Overall Purpose and Structure: This table shows how certain factors might change the relationship between what teachers are like or can do (their characteristics and competencies) and how well their students perform in school (academic performance). These factors are called 'moderators' because they change or 'moderate' the size or direction of the relationship. For instance, one moderator is the 'Country' where the study took place, broken down by its Human Development Index (HDI).
• List of Moderators: The table presents several moderators: 'Country' (categorized by low/medium HDI vs. high/very high HDI), 'Educational level' (primary and secondary education vs. secondary education), 'DV measurement' (standardized vs. non-standardized tests), 'DV category' (general achievement, mathematics, language arts, sciences, other curricular subjects), 'Type of effect' (correlation vs. beta), and 'IV information source' (students, teachers, schools). Each moderator is split into its different levels.
• Statistical Measures: For each moderator and its levels, the table provides several key statistics: 'm' (number of studies), 'k' (number of effect sizes), 'F(DF)' (F-statistic and degrees of freedom from the Wald test, which tests whether the moderator significantly affects the relationship), 'σₘ' (variance between studies), 'Zr' (Fisher's Z, a measure of effect size), 'SE' (standard error), '95% CI' (95% confidence interval), 'Z-test' (Z-test statistic), and 'p-value' (probability value).
• Interpretation of P-values: The p-values indicate the statistical significance of the moderator effect. For example, the p-value for 'Country' is less than 0.001, meaning that the country's HDI significantly moderates the relationship between teacher characteristics and student academic performance.
• Significance Levels: The table includes significance levels indicated by asterisks: *Statistically significant at confidence level of 90%, ** 95%, *** 99%.

Scientific Validity

• Statistical Methodology: The table presents the results of moderator analyses using a multilevel mixed-effects model, which is an appropriate approach for accounting for the nested structure of meta-analytic data (i.e., effect sizes within studies). The use of the Wald test to assess the significance of the moderator effects is also appropriate.
• Choice of Moderators: The choice of moderators appears reasonable, based on theoretical considerations and previous research. However, the rationale for selecting these specific moderators should be explicitly stated in the methods section.
• Degrees of Freedom: The degrees of freedom for the F-statistic are often fractional (e.g., F(1; 13.9)), which suggests the use of a Satterthwaite approximation or similar method to adjust the degrees of freedom for small sample sizes or unequal variances. This is a good practice, as it helps to ensure the accuracy of the p-values.
• Sample Size Considerations: As with Table 1, the table includes a note cautioning readers about interpreting subcategories with effect sizes from less than five studies. This is a good practice, as estimates from small samples can be unreliable.

Communication

• Overall Clarity and Readability: The table clearly presents the results of the moderator analyses, allowing readers to quickly identify which study characteristics significantly influence the relationship between teacher characteristics/competencies and student academic performance. The use of distinct rows for each moderator and its levels enhances readability.
• Statistical Information: The inclusion of the F-statistic (or other appropriate test statistic), degrees of freedom, p-value, and effect size (Zr) for each moderator allows readers to assess the statistical significance and magnitude of the moderating effect. Providing the 95% confidence intervals for Zr further aids in interpreting the results.
• Caveats and Consistency: The table includes a note cautioning readers about interpreting subcategories with effect sizes from less than five studies. This is a good practice, as estimates from small samples can be unreliable. This is consistent with the note in Table 1.

Fig. 3. Funnel plots.

Figure/Table Image (Page 10)

First Reference in Text

The funnel plots represented in Fig. 3 reflect a similar distribution to the effect sizes reported by studies conducted on larger, and those carried out on smaller samples.

Description

• Funnel Plot Basics: A funnel plot is a graph used in meta-analysis to check for publication bias, which is the tendency for studies with statistically significant or positive results to be more likely to be published than studies with non-significant or negative results. The plot gets its name from its shape: if there is no publication bias, the studies should be distributed symmetrically around the mean effect size, forming a shape resembling an inverted funnel. The x-axis represents the effect size (in this case, Fisher's Z transformed correlation coefficient), and the y-axis represents the standard error, which is a measure of the precision of the effect size estimate.
• Two Plots and Trim and Fill: The figure actually contains two funnel plots: one on the left and one on the right. The plot on the left shows the original data, while the plot on the right shows the data after applying the "trim and fill" method. The "trim and fill" method is a statistical technique used to estimate the number of missing studies due to publication bias and to adjust the overall effect size accordingly. The solid circles represent the original studies included in the meta-analysis, while the open circles represent the studies that were imputed by the "trim and fill" method.
• Interpretation of Asymmetry: If there is publication bias, the funnel plot will be asymmetrical, with a gap in one of the bottom corners. This indicates that studies with small sample sizes and non-significant results are missing from the meta-analysis. In this case, the plots appear to be somewhat asymmetrical, with a gap on the right side, suggesting that there may be some publication bias.

Scientific Validity

• Appropriateness of Method: The use of funnel plots is an appropriate method for visually assessing publication bias. However, visual inspection of funnel plots is subjective, and it is important to supplement this with statistical tests for publication bias.
• Trim and Fill Method: The "trim and fill" method is a commonly used technique for adjusting for publication bias. However, it is important to acknowledge that this method makes assumptions about the missing data mechanism and may not always be appropriate.
• Interpretation Clarity: The interpretation of the funnel plots is somewhat vague. The authors state that the plots "reflect a similar distribution to the effect sizes reported by studies conducted on larger, and those carried out on smaller samples." This statement is not very informative. A more specific interpretation of the asymmetry (or lack thereof) would be helpful.

Communication

• Axes Labels and Visual Aids: The funnel plots are presented with clear axes labels, making it easy to understand the variables being plotted (Fisher's Z on the x-axis and Standard Error on the y-axis). The plots also include a visual aid (dotted line) representing the expected shape of the funnel, which helps to assess the presence of asymmetry.
• Data Differentiation: The plots distinguish between original data and imputed data (using solid and open circles, respectively), which enhances transparency. However, the legend could be more prominent to ensure that readers notice this distinction.
• Caption Informativeness: The caption is concise but could be more informative. It should explicitly state that the funnel plots are used to assess publication bias and that the "trim and fill" method was applied.

Fig. 4. Mean effect size relative to the quality of the publication.

Figure/Table Image (Page 10)

First Reference in Text

Fig. 4 shows the mean effect size estimated for studies relative to the quality of the publication.

Description

• Overall Purpose: This figure is a plot that compares the average effect sizes from studies based on where they were published, as a proxy for publication quality. The researchers divided the studies into three groups: those published in journals with an impact factor in Journal Citation Reports (JCR), those published in journals with an impact factor in Scimago Journal & Country Rank (SJR) but not JCR, and those published in journals not included in either JCR or SJR. JCR and SJR are different systems that rank scientific journals based on how often their articles are cited by other researchers, with higher rankings generally indicating more prestigious and influential journals.
• Plot Elements: The plot shows the mean (average) effect size for each of these three groups, with horizontal lines representing the mean and error bars representing the standard error. The standard error indicates the precision of the mean estimate; smaller error bars indicate more precise estimates, while larger error bars indicate less precise estimates.
• Key Values: The figure shows that the mean effect size is highest for studies published in journals not indexed in JCR or SJR (0.52 ± 0.26), followed by studies published in journals indexed in SJR (0.35 ± 0.17), and lowest for studies published in journals indexed in JCR (0.22 ± 0.10). However, as stated in the main text, these differences are not statistically significant.

Scientific Validity

• Proxy for Publication Quality: Using journal indexing (JCR and SJR) as a proxy for publication quality is a reasonable approach, as these indices are widely used and reflect the impact and visibility of journals. However, it is important to acknowledge that these indices are not perfect measures of quality, and other factors, such as methodological rigor and reporting standards, may also be important.
• Missing Sample Sizes: The figure presents the mean effect size and standard error for each category. However, it does not provide information about the number of studies included in each category. This information is important for assessing the reliability of the estimates.
• Statistical Significance: The authors should explicitly acknowledge that the differences in mean effect sizes across the publication quality categories were found to be statistically non-significant based on the Wald test. This is important for avoiding overinterpretation of the results.

Communication

• Visual Clarity: The figure presents a clear visual comparison of the mean effect sizes across different categories of publication quality. The use of error bars provides information about the uncertainty associated with each estimate.
• Labeling: The figure uses readily understandable labels for the publication quality categories (JCR, SJR, Others). However, it could be helpful to explicitly define these categories in the figure caption or a footnote.
• Key Trend: The figure effectively communicates the trend that studies published in journals not indexed in JCR or SJR tend to report higher effect sizes. However, it is important to note that the Wald test found these differences to be statistically non-significant.

Discussion

Key Aspects

• Summary of Main Findings: The discussion reiterates the main finding that teacher characteristics and competencies, as a whole, explain a significant portion (9.19%) of the variance in secondary school students' academic performance. This overall effect is considered moderate, but it increases to 17.32% in countries with a medium/low Human Development Index (HDI).
• Differential Impact of Teacher Characteristics and Competencies: The discussion highlights the differential impact of various teacher characteristics and competencies. Psychological characteristics, particularly reflective attitude, are identified as having the greatest influence on student achievement. Acquired characteristics, especially professional development, also show a substantial impact. Teaching competencies have a greater effect than personal competencies.
• Interpretation in Relation to Existing Literature: The discussion interprets the findings in the context of existing literature. It notes that the moderate influence of teacher efficacy beliefs aligns with previous meta-analyses. The minor impact of technological knowledge and initial training is discussed in relation to the gap between pre-service teacher training and actual practice, highlighting the importance of professional development.
• Heterogeneity and Moderator Analysis: The discussion addresses the observed heterogeneity among effect sizes, referencing the moderator analysis. It notes that teacher factors have a greater impact on academic performance in countries with a lower HDI, potentially due to greater variability in teacher quality and access to the profession in those contexts. Differences in effect sizes based on the statistic used (correlation vs. regression) are also discussed.
• Limitations of the Study: The discussion acknowledges the limitations of the meta-analysis, including the difficulty of isolating the effects of teacher characteristics and competencies due to the prevalence of correlational data, the limited number of experimental studies, and the potential for omitted studies due to language restrictions.
• Implications for Teacher Professional Development: The discussion emphasizes the importance of teacher professional development programs that focus on promoting reflective attitude, teaching competencies, and other variables with a demonstrated influence on academic performance. It suggests that initial teacher training should also emphasize the application of knowledge and the development of autonomy in the training process.
• Novel Contribution: Publication Bias Analysis: The discussion offers a novel contribution by proposing and implementing an additional criterion to evaluate publication bias: analyzing differences in effect sizes based on the journal's impact factor. While the differences were not statistically significant, the results suggest a potential relationship between effect size variability and journal quality control.
• Implications for Future Research: The discussion concludes by outlining implications for future research, suggesting the need for more experimental studies, in-depth analyses of teacher professional development programs, and further exploration of teacher characteristics and competencies at other educational levels. It also calls for more consistent conceptualization and classification of teacher characteristics and competencies in future research.

Strengths

• Effective Summary of Main Findings

  The Discussion section effectively summarizes the main findings of the meta-analysis, highlighting the overall effect of teacher characteristics and competencies on student achievement and the variations across different factors.

  "This meta-analysis presents a quantitative synthesis of the 202 effect sizes provided by 40 studies published between 2000 and 2019 that had analyzed the effect of teacher characteristics and competencies on the academic achievement of secondary school students. The results obtained show that, taken together, teacher characteristics and competencies explain 9.19% of the differences in students’ performance in secondary education, and that this percentage rises to reach 17.32% in countries with a medium/low HDI." (Page 11)
• Appropriate Interpretation in Relation to Previous Research

  The section appropriately interprets the findings in relation to previous research, citing relevant studies and meta-analyses to support the interpretations and explain discrepancies.

  "Regarding teacher competencies, both teaching and personal competencies overall present low-medium effects on academic performance. These results partially coincide with the findings of the meta-analysis conducted by Danisman et al. (2019), which reported small but significant effect sizes for variables related to teaching practises." (Page 11)
• Discussion of Implications for Teaching Practices

  The section discusses the implications of the findings for teaching practices and educational improvement, suggesting potential interventions and areas for future research.

  "These findings are important for the overall improvement of teaching practices, as the association of both these characteristics with other aspects of the teaching profession, such as planning and implementing teaching, provide the foun-dations to transform the teaching practice and to implement quality teaching (Didion et al., 2019; Šeďová et al., 2020)." (Page 11)
• Acknowledgment of Limitations

  The section acknowledges the limitations of the meta-analysis, addressing potential biases and constraints on the generalizability of the findings.

  "Despite the interest of these findings, this study is not without its limitations. Firstly, as mentioned above, is the difficulty to isolate the effects of teacher characteristics and competencies on student achievement, as most of the effect sizes were obtained from cor-relations —which only report the association between the variables under study and do not isolate the effects of teacher characteristics and competencies on student achievement—." (Page 12)
• Connection to Broader Context

  The discussion connects the findings back to the broader context established in the introduction, referencing key concepts like reflective practice and the importance of professional development.

  "Hence, the teacher characteristics with the greatest influence on students’ academic performance correspond to their psychological characteristics, and within these, teachers’ reflective attitude is especially noteworthy. This refers to a teacher’s predisposition to reconsider their own beliefs and knowledge in an active, persistent and careful way in order to foment a more conscious and deliberate teaching practice." (Page 11)

Suggestions for Improvement

• Discuss Alternative Explanations

  Medium impact. This would strengthen the discussion by providing a more balanced perspective and acknowledging alternative viewpoints or explanations for the observed findings. The Discussion section should critically evaluate the results and consider different interpretations.

  "Firstly, as mentioned above, is the difficulty to isolate the effects of teacher characteristics and competencies on student achievement, as most of the effect sizes were obtained from cor-relations —which only report the association between the variables under study and do not isolate the effects of teacher characteristics and competencies on student achievement—." (Page 12)

  Implementation: Include a paragraph discussing potential alternative explanations for the findings, such as the influence of unmeasured variables or the limitations of correlational data. For example: "While this meta-analysis provides strong evidence for the impact of teacher characteristics and competencies on student achievement, it is important to acknowledge that other factors not included in this study, such as student motivation, socioeconomic status, and school resources, may also play a significant role. Furthermore, the reliance on correlational data in many of the included studies limits our ability to draw causal inferences."

• Integrate Moderator Analysis Findings More Explicitly

  Low impact. This would enhance the clarity and flow of the discussion by explicitly linking the findings of the moderator analyses to the main discussion points. The Discussion section should integrate all results into a cohesive narrative.

  "Firstly, when examining the effect of the development level of the countries in which the studies were performed, teacher factors have less impact on academic performance in countries with a higher HDI than in countries with a lower HDI." (Page 11)

  Implementation: Integrate the findings of the moderator analyses more explicitly into the main discussion. For example, when discussing the importance of teacher characteristics in countries with a lower HDI, refer back to the moderator analysis results: "Consistent with the moderator analysis finding that teacher effects are stronger in countries with a lower HDI (Zr = 0.443 vs. 0.256, p < 0.001), these results suggest that targeted interventions focused on teacher development may be particularly impactful in these contexts."

• Provide More Specific Recommendations for Future Research

  Medium impact. This would strengthen the discussion by providing more specific and actionable recommendations for future research. The Discussion section should identify future research directions based on the study's findings and limitations.

  "This research also leaves the door open to further systematic reviews and meta-analyses aimed at studying in greater depth the effect of teacher characteristics and competences on academic achievement at other educational levels." (Page 12)

  Implementation: Provide more specific recommendations for future research, such as: "Future research should employ experimental or quasi-experimental designs to investigate the causal impact of specific teacher characteristics and competencies on student achievement. Longitudinal studies are also needed to examine the long-term effects of teacher development programs. Furthermore, future meta-analyses could explore the influence of other potential moderator variables, such as school type, student demographics, and specific teaching practices."

• Improve the Organization and Flow of the Discussion

  Low impact. This would improve the flow and organization of the discussion by grouping related findings and interpretations together. The Discussion section should present a coherent and well-structured narrative.

  "Hence, the teacher characteristics with the greatest influence on students’ academic performance correspond to their psychological characteristics, and within these, teachers’ reflective attitude is especially noteworthy...Regarding teacher acquired characteristics, which arise as the second most influential group of characteristics on academic per-formance, it is interesting to note the minor impact of teachers’ technological knowledge and initial training..." (Page 11)

  Implementation: Reorganize the discussion to group related findings and interpretations together. For example, discuss all findings related to teacher psychological characteristics in one paragraph, followed by a separate paragraph discussing findings related to acquired characteristics. This would improve the logical flow and make it easier for readers to follow the main arguments.