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Measuring Ability-to-Learn Using Parametric Learning-Gain Functions
2020 (English)Conference paper, Published paper (Refereed)
Abstract [en]

One crucial function of a classroom, and a school more generally, is to prepare students for future learning. Students should have the capacity to learn new information and to acquire new skills. This ability to "learn" is a core competency in our rapidly changing world. But how do we measure ability to learn? And how can we measure how well a school has prepared their students to learn? In this paper we formally pose the problem, and introduce a grounded theory of how to measure ability to learn. Using simulations of students learning we provide initial evidence that this theory provides an elegant solution to this problem. We further validate our ideas using real world data from 70k middle-school students and show that our theory is more accurate and interpretable than current state-of-the-art models of learning gains. We consider our results a modest yet interesting first step for a novel type of test. [For the full proceedings, see ED607784.]

Place, publisher, year, edition, pages
International Educational Data Mining Society , 2020.
Keywords [en]
Academic Ability, Achievement Gains, Bayesian Statistics, Foreign Countries, Measurement, Middle School Students, Statistical Analysis, Undergraduate Students
National Category
Artificial Intelligence Probability Theory and Statistics Educational Sciences
Identifiers
URN: urn:nbn:se:kth:diva-367970OAI: oai:DiVA.org:kth-367970DiVA, id: diva2:1986516
Conference
International Conference on Educational Data Mining, July 10-13, Online
Note

QC 20250814

Available from: 2025-07-31 Created: 2025-07-31 Last updated: 2025-08-14Bibliographically approved

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Davis, Richard Lee

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CiteExportLink to record
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