Learning path recommendation in university environments based on sequence mining
○馬 博軒,谷口雄太,木實新一(九大)
Learning path recommendation system efficiently guides learners by constructing appropriate learning sequences from recommended learning materials to reach their goals. However, supporting active learning in the learning path recommendation systems for university environments is different from conventional mechanisms for recommending relevant online courses such as MOOCs. This paper analyzed different learning path patterns of students at Kyushu University and discussed the challenges to recommend appropriate learning sequence in university learning environments. Then we proposed an approach to address the challenges by designing a learning path recommendation mechanism based on sequence mining.

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