Set-to-Sequence Ranking-based Concept-aware Learning Path Recommendation

This paper proposes SRC, a set-to-sequence ranking model that generates personalized learning paths by recommending sequences of learning concepts tailored to individual students' needs and target learning objectives. The method captures correlations between concepts and uses knowledge tracing to evaluate learning effects, tested on educational datasets including university-level mathematics courses.

With the development of the online education system, personalized education recommendation has played an essential role. In this paper, we focus on developing path recommendation systems that aim to generating and recommending an entire learning path to the given user in each session. Noticing that existing approaches fail to consider the correlations of concepts in the path, we propose a novel framework named Set-to-Sequence Ranking-based Concept-aware Learning Path Recommendation (SRC), which

Source

Framework Categories

Tool Types

Tags