Value-added (VA) modeling can be used to quantify teacher and school effectiveness by estimating the effect of pedagogical actions on students' achievement. It is gaining increasing importance in educational evaluation, teacher accountability, and high-stakes decisions. We analyzed 370 empirical studies on VA modeling, focusing on modeling and methodological issues to identify key factors for improvement. The studies stemmed from 26 countries (68% from the USA). Most studies applied linear regression or multilevel models. Most studies (i.e., 85%) included prior achievement as a covariate, but only 2% included noncognitive predictors of achievement (e.g., personality or affective student variables). Fifty-five percent of the studies did not apply statistical adjustments (e.g., shrinkage) to increase precision in effectiveness estimates, and 88% included no model diagnostics. We conclude that research on VA modeling can be significantly enhanced regarding the inclusion of covariates, model adjustment and diagnostics, and the clarity and transparency of reporting.Keywords Value-added modeling . Literature review . Primary and secondary education . Teacher effectiveness . School effectiveness What is the added value from attending a certain school or being taught by a certain teacher? To answer this question, the value-added (VA) model was developed. In this model, the actual achievement attained by students attending a certain school or being

There is no consensus on which statistical model estimates school value-added (VA) most accurately. To date, the two most common statistical models used for the calculation of VA scores are two classical methods: linear regression and multilevel models. These models have the advantage of being relatively transparent and thus understandable for most researchers and practitioners. However, these statistical models are bound to certain assumptions (e.g., linearity) that might limit their prediction accuracy. Machine learning methods, which have yielded spectacular results in numerous fields, may be a valuable alternative to these classical models. Although big data is not new in general, it is relatively new in the realm of social sciences and education. New types of data require new data analytical approaches. Such techniques have already evolved in fields with a long tradition in crunching big data (e.g., gene technology). The objective of the present paper is to competently apply these "imported" techniques to education data, more precisely VA scores, and assess when and how they can extend or replace the classical psychometrics toolbox. The different models include linear and non-linear methods and extend classical models with the most commonly used machine learning methods (i.e., random forest, neural networks, support vector machines, and boosting). We used representative data of 3,026 students in 153 schools who took part in the standardized achievement tests of the Luxembourg School Monitoring Program in grades 1 and 3. Multilevel models outperformed classical linear and polynomial regressions, as well as different machine learning models. However, it could be observed that across all schools, school VA scores from different model types correlated highly. Yet, the percentage of disagreements as compared to multilevel models was not trivial and real-life implications for individual schools may still be dramatic depending on the model type used. Implications of these results and possible ethical concerns regarding the use of machine learning methods for decision-making in education are discussed.

There is no final consensus regarding which covariates should be used (in addition to prior achievement) when estimating value-added (VA) scores to evaluate a school’s effectiveness. Therefore, we examined the sensitivity of evaluations of schools’ effectiveness in math and language achievement to covariate selection in the applied VA model. Four covariate sets were systematically combined, including prior achievement from the same or different domain, sociodemographic and sociocultural background characteristics, and domain-specific achievement motivation. School VA scores were estimated using longitudinal data from the Luxembourg School Monitoring Programme with some 3600 students attending 153 primary schools in Grades 1 and 3. VA scores varied considerably, despite high correlations between VA scores based on the different sets of covariates (.66 < r < 1.00). The explained variance and consistency of school VA scores substantially improved when including prior math and prior language achievement in VA models for math and prior language achievement with sociodemographic and sociocultural background characteristics in VA models for language. These findings suggest that prior achievement in the same subject, the most commonly used covariate to date, may be insufficient to control for between-school differences in student intake when estimating school VA scores. We thus recommend using VA models with caution and applying VA scores for informative purposes rather than as a mean to base accountability decisions upon.

No abstract

No abstract