arXiv Machine Learning By Anderson L. de Paula, Pedro C. dos Santos, Renato A. Krohling

Analysis of Public Schools Educational Performance Based on Causal Models and Hierarchical Clustering

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arXiv:2607. 14124v1 Announce Type: cross Abstract: The increasing availability of large-scale educational datasets has expanded the use of quantitative methods for investigating school performance.

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arXiv AI
Aug 26

Causal Modelling of Support Interventions for Student Competency Assessment

The paper proposes a structural causal modelling framework for student competency assessment, moving beyond traditional probabilistic models like item response theory. It introduces a protocol for constructing such models, emphasizing the explicit representation of interventions (e.g., hints) and counterfactual analysis. The authors illustrate the approach with data from an assessment of compulsory school students’ algorithmic skills.

By Francesca Mangili, Alessandro Antonucci, Rafael Caba\~nas
arXiv Machine Learning
Jul 30

Archetypes or ability? Clustering for modelling student mathematical competence

arXiv:2607. 26063v1 Announce Type: cross Abstract: Personalised learning systems often assume that mathematical ability is combined of discrete abilities, acquired sequentially and dependent upon first acquiring foundational abilities, and students often report different strengths.

By Benjamin Mawdsley, Tom Quilter, Richard Turner, Sarah Jackson, Paul Edwards