How sensitive are the evaluations of a school's effectiveness to the selection of covariates in the applied value‑added model?

in Educational Assessment, Evaluation and Accountability (2022)

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 ... [more ▼]

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. [less ▲]

Are Value-Added Scores Stable Enough for High-Stakes Decisions?

Scientific Conference (2022, March)

Theoretical Background: Can we quantify the effectiveness of a teacher or a school with a single number? Researchers in the field of value-added (VA) models may argue just that (e.g., Chetty et al., 2014 ... [more ▼]

Theoretical Background: Can we quantify the effectiveness of a teacher or a school with a single number? Researchers in the field of value-added (VA) models may argue just that (e.g., Chetty et al., 2014; Kane et al., 2013). VA models are widely used for accountability purposes in education and quantify the value a teacher or a school adds to their students’ achievement. For this purpose, these models predict achievement over time and attempt to control for factors that cannot be influenced by schools or teachers (i.e., sociodemographic & sociocultural background). Following this logic, what is left must be due to teacher or school differences (see, e.g., Braun, 2005). To utilize VA models for high-stakes decision-making (e.g., teachers’ tenure, the allocation of funding), these models would need to be highly stable over time. School-level stability over time, however, has hardly been researched at all and the resulting findings are mixed, with some studies indicating high stability of school VA scores over time (Ferrão, 2012; Thomas et al., 2007) and others reporting a lack of stability (e.g., Gorard et al., 2013; Perry, 2016). Furthermore, as there is no consensus on which variables to use as independent or dependent variables in VA models (Everson, 2017; Levy et al., 2019), the stability of VA could vary between different outcome measures (e.g., language or mathematics). If VA models lack stability over time and across outcome measures, their use as the primary information for high-stakes decision-making is in question, and the inferences drawn from them could be compromised. Questions: With these uncertainties in mind, we examine the stability of school VA model scores over time and investigate the differences between language and mathematics achievement as outcome variables. Additionally, we demonstrate the real-life implications of (in)stable VA scores for single schools and point out an alternative, more constructive use of school VA models in educational research. Method: To study the stability of VA scores on school level over time and across outcomes, we drew on a sample of 146 primary schools, using representative longitudinal data from the standardized achievement tests of the Luxembourg School Monitoring Programme (LUCET, 2021). These schools included a heterogeneous and multilingual sample of 7016 students. To determine the stability of VA scores in the subject of mathematics and in languages over time, we based our analysis on two longitudinal datasets (from 2015 to 2017 and from 2017 to 2019, respectively) and generated two VA scores per dataset, one for language and one for mathematics achievement. We further analyzed how many schools displayed stable VA scores in the respective outcomes over two years, and compared the rank correlations of VA scores between language and mathematics achievement as an outcome variable. Results and Their Significance: Only 34-38 % of the schools showed stable VA scores from grade 1 to 3 with moderate rank correlations of r = .37 with language and r = .34 with mathematics achievement. We therefore discourage using VA models as the only information for high-stakes educational decisions. Nonetheless, we argue that VA models could be employed to find genuinely effective teaching or school practices—especially in heterogeneous student populations, such as Luxembourg, in which educational disparities are an important topic already in primary school (Hoffmann et al., 2018). Consequently, we contrast the school climate and instructional quality, which might be a driver of the differences between schools with stable high vs. low VA scores. Literature Braun, H. (2005). Using student progress to evaluate teachers: A primer on value-added models. Educational Testing Service. Chetty, R., Friedman, J. N., & Rockoff, J. E. (2014). Measuring the impacts of teachers I: Evaluating bias in teacher value-added estimates. American Economic Review, 104(9), 2593–2632. https://doi.org/10.1257/aer.104.9.2593 Everson, K. C. (2017). Value-added modeling and educational accountability: Are we answering the real questions? Review of Educational Research, 87(1), 35–70. https://doi.org/10.3102/0034654316637199 Ferrão, M. E. (2012). On the stability of value added indicators. Quality & Quantity, 46(2), 627–637. https://doi.org/10.1007/s11135-010-9417-6 Gorard, S., Hordosy, R., & Siddiqui, N. (2013). How unstable are “school effects” assessed by a value-added technique? International Education Studies, 6(1), 1–9. https://doi.org/10.5539/ies.v6n1p1 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. Research Paper. MET Project. Bill & Melinda Gates Foundation. https://files.eric.ed.gov/fulltext/ED540959.pdf Levy, J., Brunner, M., Keller, U., & Fischbach, A. (2019). Methodological issues in value-added modeling: An international review from 26 countries. Educational Assessment, Evaluation and Accountability, 31(3), 257–287. https://doi.org/10.1007/s11092-019-09303-w LUCET. (2021). Épreuves Standardisées (ÉpStan). https://epstan.lu Perry, T. (2016). English value-added measures: Examining the limitations of school performance measurement. British Educational Research Journal, 42(6), 1056–1080. https://doi.org/10.1002/berj.3247 Thomas, S., Peng, W. J., & Gray, J. (2007). Modelling patterns of improvement over time: Value added trends in English secondary school performance across ten cohorts. Oxford Review of Education, 33(3), 261–295. https://doi.org/10.1080/03054980701366116 [less ▲]

Stability of Value-Added Models: Comparing Classical and Machine Learning Approaches

Scientific Conference (2021, September)

Background: What is the value that teachers or schools add to the evolution of students’ performance? Value-added (VA) modeling aims to answer this question by quantifying the effect of pedagogical ... [more ▼]

Background: What is the value that teachers or schools add to the evolution of students’ performance? Value-added (VA) modeling aims to answer this question by quantifying the effect of pedagogical actions on students’ achievement, independent of students’ backgrounds (e.g., Braun, 2005). A plethora of VA models exist, and several outcome measures are in use to estimate VA scores, yet without consensus on the model specification (Everson, 2017; Levy et al., 2019). Furthermore, it is unclear whether the most frequently used VA models (i.e., multi-level, linear regression, and random forest models) and outcome measures (i.e., language and mathematics achievement) indicate a similar stability of VA scores over time. Objectives: Drawing from the data of a highly diverse and multilingual school setting, where leveling out the influence of students’ backgrounds is of special interest, we aim to (a) clarify the stability of school VA scores over time; (b) shed light on the sensitivity toward different statistical models and outcome variables; and (c) evaluate the practical implications of (in)stable VA scores for individual schools. Method: Utilizing the representative, longitudinal data from the Luxembourg School Monitoring Programme (LUCET, 2021), we examined the stability of school VA scores. We drew on two longitudinal data sets of students who participated in the standardized achievement tests in Grade 1 in 2014 or 2016 and then again in Grade 3 two years later (i.e., 2016 and 2018, respectively), with a total of 5875 students in 146 schools. School VA scores were calculated using classical approaches (i.e., linear regression and multilevel models) and one of the most commonly used machine learning approaches in educational research (i.e., random forests). Results and Discussion: The overall stability over time across the VA models was moderate, with multilevel models showing greater stability than linear regression models and random forests. Stability differed across outcome measures and was higher for VA models with language achievement as an outcome variable as compared to those with mathematics achievement. Practical implications for schools and teachers will be discussed. [less ▲]

Can machine learning methods lead to more precise measures of school effectiveness? An application of various machine learning approaches in the estimation of school value-added scores

Scientific Conference (2020, July)

Circadian preference as a typology: Latent-class analysis of adolescents' morningness/eveningness, relation with sleep behavior, and with academic outcomes

in Learning and Individual Differences (2020), 78

Similarities and differences of value-added scores from models with different covariates: A cluster analysis

Scientific Conference (2019, November 06)

Value-added modeling in primary school: What covariates to include?

Scientific Conference (2019, August)

The use of value-added models for the identification of schools that perform “against the odds”

Poster (2019, July)

Value-added (VA) modeling aims to quantify the effect of pedagogical actions on students’ achievement, independent of students’ backgrounds. VA modeling is primarily used for accountability and high ... [more ▼]

Value-added (VA) modeling aims to quantify the effect of pedagogical actions on students’ achievement, independent of students’ backgrounds. VA modeling is primarily used for accountability and high-stakes decisions. To date, there seems to be no consensus concerning the calculation of VA models. Our study aims to systematically analyze and compare different school VA models by using longitudinal large-scale data emerging from the Luxembourg School Monitoring Programme. Regarding the model covariates, first findings indicate the importance of language (i.e., language(s) spoken at home and prior language achievement) in VA models with either language or math achievement as a dependent variable, with the highest amount of explained variance in VA models for language. Concerning the congruence of different VA approaches, we found high correlations between school VA scores from the different models, but also high ranges between VA scores for single schools. We conclude that VA models should be used with caution and with awareness of the differences that may arise from methodological choices. Finally, we discuss the idea that VA models could be used for the identification of schools that perform “against the odds”, especially for those schools that have positive VA scores over several years. [less ▲]

Wat droen d’Schoulen zur Leeschtung vu Schülerinnen a Schüler bäi? E systemateschen Iwwerbléck iwwer déi aktuell Fuerschungslag an Uwennung u lëtzebuergesche Längsschnëtt-Donnéeën

Scientific Conference (2018, November 08)

Évaluation de l'efficacité du système éducatif par le calcul de valeur ajoutée (VA) : Revue de littérature et exploration de modèles de VA dans un contexte scolaire hautement hétéroclite.

Scientific Conference (2018, July)