Personalized Mathematics Education with Intelligent Recommendation

Abstract

Differentiation, the process of individualizing education, remains a difficult endeavor that can overwork teachers and negatively affect students. Due to the tremendous upside of differentiation, this thesis examines the possibility of predicting student performance using Cross-Network LSTMs with additional data sources. After modifying the attention mechanism, the model proposed showed an improved performance in prediction with the inclusion of additional measurements already in the Junyi Academy Online Learning Activity Dataset. The modifications also showed the ability to separate problems into course units based on their similarity scores, and quickly make long term predictions. The development of this network as recommender system for differentiation is discussed as well as a working example using the AP Calculus BC curriculum.