Journal of East China Normal University(Natural Science) ›› 2022, Vol. 2022 ›› Issue (6): 87-101.doi: 10.3969/j.issn.1000-5641.2022.06.010

• Computer Science • Previous Articles     Next Articles

Personalized course recommendations based on a learner’s knowledge and personality

Qimin BAN1, Wen WU1,*(), Wenxin HU2, Hui LIN3, Wei ZHENG4, Liang HE1   

  1. 1. School of Computer Science and Technology, East China Normal University, Shanghai 200062, China
    2. School of Data Science and Engineering, East China Normal University, Shanghai 200062, China
    3. Shanghai Liulishuo Information Technology Co. Ltd., Shanghai 200090, China
    4. Information Technology Services, East China Normal University, Shanghai 200062, China
  • Received:2020-11-01 Online:2022-11-25 Published:2022-11-22
  • Contact: Wen WU


Adaptive learning is an educational method that uses computer algorithms to coordinate interaction with learners, and provides customized learning resources and learning activities to address the unique needs of each learner. With the impact of COVID-19, adaptive learning has become increasingly important. One of the challenges with adaptive learning is how to provide personalized learning resources for learners—i.e., how to generate personalized recommendation for learners from a large set of learning resources. Existing methodologies mainly generate recommendations based on a learner’s knowledge level; however, this approach has some limitations. Firstly, when assessing a learner’s knowledge level, learners’ forgetting phenomenon has to date not been well modeled. Secondly, recommendations are generated separately from knowledge tracing tasks, ignoring the interconnectedness between these aspects. In addition, learners’ preferences for the type of learning resources and learning strategies is normally ignored if the knowledge level alone is used. To solve the aforementioned problems, this paper proposes a knowledge and personality incorporated multi-task learning framework (KPM) to boost course recommendations (i.e., the above-mentioned learning resources); the proposed method regards an enhanced knowledge tracing task (EKTT) as an auxiliary task to assist the primary course recommendation task (CRT). Specifically, using EKTT, we design a personalized forgetting controller to enhance the deep knowledge tracing model for accurately assessing a learner’s knowledge level. With CRT, we combine the learner’s knowledge level and sequential behavior with their personality adapted to the specific context to obtain learner’s profile; this data is subsequently used to generate a course recommendation list. Experimental results on real-world educational datasets demonstrate the superiority of our proposed method in terms of hit ratio (HR), normalized discounted cumulative gain (NDCG), and precision, indicating that our method can generate more personalized recommendations.

Key words: course recommendation, knowledge tracing, multi-task learning, personality, adaptive learning

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