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Predicting Young Students' Self-Evaluation Deficits Through Their Activity Traces


Self-evaluation is a key self-regulatory process that can already be mastered by young children. In order to assess self-evaluation skills of children, we introduced a random prompt asked randomly after 1 out of 15 exercises into a literacy web-application for primary school student, in order to evaluate the perceived difficulty [Too easy, Good, Too difficult] of the exercise they just solved. Comparing students' actual performance with their responses to this prompt can provide information about their ability to self-evaluate, and thus detect students who could improve their self-evaluation skills. We collected more than 1,000,000 responses from 300,000 students and used these data as well as performance data on each question of each exercise to predict a student's response to the next prompt, thereby estimating how likely they are to having a self-evaluation deficit. The results show (a) that a student's past responses to self-evaluation statements impacts the quality of future predictions (b) that the impact of past responses-vs their current performance-is greater when the student has low capacity for self-evaluation (c) that including older student data (answers from several sessions ago) helps in improving the accuracy of the prediction. These results pave the way (1) for adaptive polling by identifying when the model is unreliable, giving them the statement then instead of randomly, (2) for adaptive feedback, by knowing the students the most likely to show a deficit, to provide remediation.
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hal-03353186 , version 1 (23-09-2021)


  • HAL Id : hal-03353186 , version 1


Thomas Sergent, Morgane Daniel, François Bouchet, Thibault Carron. Predicting Young Students' Self-Evaluation Deficits Through Their Activity Traces. 14th International Conference on Educational Data Mining, IEDMS, Jun 2021, Paris, France. pp.667-671. ⟨hal-03353186⟩
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