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Génération automatique de sujets d'évaluation individuels en contexte universitaire

Abstract : This PhD work focuses on the evaluation of learning and especially the automatic generation of evaluation topics in universities. We rely on a base of source questions to create topic questions through algorithms that are able to construct differentiated assessment tests. This research has made it possible to develop a metric that measures this differentiation and to propose algorithms aimed at maximizing total differentiation on test collections, while minimizing the number of necessary patterns. The average performance of the latter depends on the number of patterns available in the source database (compared to the number of items desired in the tests), and the size of the generated collections. We focused on the possible differentiation in very small collections of subjects, and proposes methodological tracks to optimize the distribution of these differentiated subjects to cohorts of students respecting the constraints of the teacher. The rest of this work will eventually take into account the level of difficulty of a test as a new constraint, relying in part on the statistical and semantic data collected after each test. The goal is to be able to maximize the differentiation by keeping the equity between the Tests of a Collection, for an optimized distribution during the Events
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Submitted on : Tuesday, April 12, 2022 - 9:33:10 AM
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  • HAL Id : tel-03638132, version 1



Richardson Ciguene. Génération automatique de sujets d'évaluation individuels en contexte universitaire. Autre [cs.OH]. Université de Picardie Jules Verne, 2019. Français. ⟨NNT : 2019AMIE0046⟩. ⟨tel-03638132⟩



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