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Modelling and optimizing mathematics learning in children


Käser, Tanja; Busetto, Alberto Giovanni; Solenthaler, Barbara; Baschera, Gian-Marco; Kohn, Juliane; Kucian, Karin; Aster, Michael; Gross, Markus (2013). Modelling and optimizing mathematics learning in children. International Journal of Artificial Intelligence in Education, 23(1-4):115-135.

Abstract

This study introduces a student model and control algorithm, optimizing mathematics learning in children. The adaptive system is integrated into a computer-based training system for enhancing numerical cognition aimed at children with developmental dyscalculia or difficulties in learning mathematics. The student model consists of a dynamic Bayesian network which incorporates domain knowledge and enables the operation of an online system of automatic control. The system identifies appropriate tasks and exercise interventions on the basis of estimated levels of accumulated knowledge. Student actions are evaluated and monitored to extract statistical patterns which are useful for predictive control. The training system is adaptive and personalizes the learning experience, which improves both success and motivation. Comprehensive testing of input data validates the quality of the obtained results and confirms the advantage of the optimized training. Pilot results of training effects are included and discussed.

Abstract

This study introduces a student model and control algorithm, optimizing mathematics learning in children. The adaptive system is integrated into a computer-based training system for enhancing numerical cognition aimed at children with developmental dyscalculia or difficulties in learning mathematics. The student model consists of a dynamic Bayesian network which incorporates domain knowledge and enables the operation of an online system of automatic control. The system identifies appropriate tasks and exercise interventions on the basis of estimated levels of accumulated knowledge. Student actions are evaluated and monitored to extract statistical patterns which are useful for predictive control. The training system is adaptive and personalizes the learning experience, which improves both success and motivation. Comprehensive testing of input data validates the quality of the obtained results and confirms the advantage of the optimized training. Pilot results of training effects are included and discussed.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > University Children's Hospital Zurich > Medical Clinic
Dewey Decimal Classification:610 Medicine & health
Language:English
Date:2013
Deposited On:03 Feb 2014 13:49
Last Modified:05 Apr 2016 17:15
Publisher:International AIED Society ; IOS Press
ISSN:1560-4292
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1007/s40593-013-0003-7

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