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Abstract:

The learner model is an important foundation for effectively recommending personalized learning resources. Although it has widely integrated multiple dimensions of learner characteristics, it has not incorporated online learning self-efficacy, a key indicator for measuring learners‘ psychology and learning motivation.Therefore, self-efficacy of online learning is introduced and a comprehensive evaluation method that combines static and dynamic approaches is proposed, using scales and a series of designed behavioral indicators to achieve a comprehensive assessment of online learning self-efficacy. Diversified features such as learning interests,current levels, and learning styles is integrated to construct a multi-dimensional learner model and an improved neural collaborative filtering method that incorporates the learner model is designed. Experimental results show that the multi-dimensional learner model has significantly better performance in recommendation methods than the comparison methods that do not consider this factor, providing a new perspective for the construction of learner models and enabling more effective personalized and precise learning resource recommendations for learners.

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