Causal Modelling of Support Interventions for Student Competency Assessment

Francesca Mangili, Alessandro Antonucci, Rafael Cabañas de Paz
Proceedings of the 13th International Conference on Probabilistic Graphical Models, PMLR 343:559-574, 2026.

Abstract

Accurate assessment of students’ competencies is essential for enabling educators to identify individual needs, design targeted interventions, and evaluate the effectiveness of educational strategies. Empirical assessment procedures are typically grounded in psychometric models, such as \emph{item response theory}, which relate students’ competence levels to their performance on assessment tasks. In this paper, we advocate adopting a structural causal modelling approach to educational assessment, moving beyond probabilistic belief updating toward a framework that explicitly supports interventional and counterfactual reasoning. We propose a corresponding protocol for its construction and analyse the practical relevance of forms of reasoning that remain inaccessible to standard associative models, including the explicit modelling of interventions such as hints and the related counterfactual scenario analysis. Although our protocol requires the structural equations to be elicited from experts, the necessary information is purely logical and does not rely on probabilistic, less tenable assumptions. We illustrate the approach using data from an assessment that employs complex tasks designed to measure compulsory school students’ algorithmic skills.

Cite this Paper

BibTeX
@InProceedings{pmlr-v343-mangili26a, title = {Causal Modelling of Support Interventions for Student Competency Assessment}, author = {Mangili, Francesca and Antonucci, Alessandro and {Caba\~nas de Paz}, Rafael}, booktitle = {Proceedings of the 13th International Conference on Probabilistic Graphical Models}, pages = {559--574}, year = {2026}, editor = {Camps-Valls, Gustau and Leonelli, Manuele and Varando, Gherardo}, volume = {343}, series = {Proceedings of Machine Learning Research}, month = {09--11 Sep}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v343/main/assets/mangili26a/mangili26a.pdf}, url = {https://proceedings.mlr.press/v343/mangili26a.html}, abstract = {Accurate assessment of students’ competencies is essential for enabling educators to identify individual needs, design targeted interventions, and evaluate the effectiveness of educational strategies. Empirical assessment procedures are typically grounded in psychometric models, such as \emph{item response theory}, which relate students’ competence levels to their performance on assessment tasks. In this paper, we advocate adopting a structural causal modelling approach to educational assessment, moving beyond probabilistic belief updating toward a framework that explicitly supports interventional and counterfactual reasoning. We propose a corresponding protocol for its construction and analyse the practical relevance of forms of reasoning that remain inaccessible to standard associative models, including the explicit modelling of interventions such as hints and the related counterfactual scenario analysis. Although our protocol requires the structural equations to be elicited from experts, the necessary information is purely logical and does not rely on probabilistic, less tenable assumptions. We illustrate the approach using data from an assessment that employs complex tasks designed to measure compulsory school students’ algorithmic skills.} }
Endnote
%0 Conference Paper %T Causal Modelling of Support Interventions for Student Competency Assessment %A Francesca Mangili %A Alessandro Antonucci %A Rafael Cabañas de Paz %B Proceedings of the 13th International Conference on Probabilistic Graphical Models %C Proceedings of Machine Learning Research %D 2026 %E Gustau Camps-Valls %E Manuele Leonelli %E Gherardo Varando %F pmlr-v343-mangili26a %I PMLR %P 559--574 %U https://proceedings.mlr.press/v343/mangili26a.html %V 343 %X Accurate assessment of students’ competencies is essential for enabling educators to identify individual needs, design targeted interventions, and evaluate the effectiveness of educational strategies. Empirical assessment procedures are typically grounded in psychometric models, such as \emph{item response theory}, which relate students’ competence levels to their performance on assessment tasks. In this paper, we advocate adopting a structural causal modelling approach to educational assessment, moving beyond probabilistic belief updating toward a framework that explicitly supports interventional and counterfactual reasoning. We propose a corresponding protocol for its construction and analyse the practical relevance of forms of reasoning that remain inaccessible to standard associative models, including the explicit modelling of interventions such as hints and the related counterfactual scenario analysis. Although our protocol requires the structural equations to be elicited from experts, the necessary information is purely logical and does not rely on probabilistic, less tenable assumptions. We illustrate the approach using data from an assessment that employs complex tasks designed to measure compulsory school students’ algorithmic skills.
APA
Mangili, F., Antonucci, A. & Cabañas de Paz, R.. (2026). Causal Modelling of Support Interventions for Student Competency Assessment. Proceedings of the 13th International Conference on Probabilistic Graphical Models, in Proceedings of Machine Learning Research 343:559-574 Available from https://proceedings.mlr.press/v343/mangili26a.html.

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