Reference : Contrasting Classical and Machine Learning Approaches in the Estimation of Value-Adde...
Scientific journals : Article
Social & behavioral sciences, psychology : Education & instruction
http://hdl.handle.net/10993/44313
Contrasting Classical and Machine Learning Approaches in the Estimation of Value-Added Scores in Large-Scale Educational Data
English
Levy, Jessica mailto [University of Luxembourg > Faculty of Language and Literature, Humanities, Arts and Education (FLSHASE) > Luxembourg Centre for Educational Testing (LUCET) >]
Mussack, Dominic mailto [University of Luxembourg > Faculty of Language and Literature, Humanities, Arts and Education (FLSHASE) > Education, Culture, Cognition and Society (ECCS) >]
Brunner, Martin mailto [University of Potsdam]
Keller, Ulrich mailto [University of Luxembourg > Faculty of Language and Literature, Humanities, Arts and Education (FLSHASE) > Luxembourg Centre for Educational Testing (LUCET) >]
Cardoso Leite, Pedro mailto [University of Luxembourg > Faculty of Language and Literature, Humanities, Arts and Education (FLSHASE) > Education, Culture, Cognition and Society (ECCS) >]
Fischbach, Antoine mailto [University of Luxembourg > Faculty of Language and Literature, Humanities, Arts and Education (FLSHASE) > Luxembourg Centre for Educational Testing (LUCET) >]
2020
Frontiers in Psychology
Frontiers Media S.A.
11
Transdisciplinary Research on Learning and Teaching: Chances and Challenges
Article 2190
Yes (verified by ORBilu)
International
1664-1078
Pully
Switzerland
[en] value-added modeling ; school effectiveness ; machine learning ; model comparison ; longitudinal data
[en] 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.
http://hdl.handle.net/10993/44313
10.3389/fpsyg.2020.02190
FnR ; FNR10921377 > Adelheid Hu > CALIDIE > Capitalising on Linguistic Diversity in Education > 15/01/2017 > 14/07/2023 > 2016

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