Affection-enhanced Personalized Question Recommendation in Online Learning

Abstract

With the popularity of online learning, intelligent tutoring systems are starting to become mainstream for assisting online question practice.Surrounded by abundant learning resources, some students struggle to select the proper questions.Personalized question recommendation is crucial for supporting students in choosing the proper questions to improve their learning performance.However, traditional question recommendation methods (i.e., collaborative filtering (CF) and cognitive diagnosis model (CDM)) cannot meet students' needs well.The CDM-based question recommendation ignores students' requirements and similarities, resulting in inaccuracies in the recommendation.Even CF examines student similarities, it disregards their knowledge proficiency and struggles when generating questions of appropriate difficulty.To solve these issues, we first design an enhanced cognitive diagnosis process that integrates students' affection into traditional CDM by employing the non-compensatory bidimensional item response model (NCB-IRM) to enhance the representation of individual personality.Subsequently, we propose an affection-enhanced personalized question recommendation (AE-PQR) method for online learning.It introduces NCB-IRM to CF, considering both individual and common characteristics of students' responses to maintain rationality and accuracy for personalized question recommendation.Experimental results show that our proposed method improves the accuracy of diagnosed student cognition and the appropriateness of recommended questions.