NR4DER: Neural Re-ranking for Diversified Exercise Recommendation

TL;DR

NR4DER is a neural re-ranking approach that improves exercise recommendations for online learners by enhancing diversity and personalization, especially for inactive students, leading to better learning outcomes.

Contribution

The paper introduces NR4DER, a novel neural re-ranking framework that combines mLSTM, sequence enhancement, and re-ranking to improve diversity and personalization in exercise recommendations.

Findings

NR4DER outperforms existing methods on real-world datasets.

It effectively caters to diverse learning paces of students.

The approach improves recommendation accuracy and diversity.

Abstract

With the widespread adoption of online education platforms, an increasing number of students are gaining new knowledge through Massive Open Online Courses (MOOCs). Exercise recommendation have made strides toward improving student learning outcomes. However, existing methods not only struggle with high dropout rates but also fail to match the diverse learning pace of students. They frequently face difficulties in adjusting to inactive students' learning patterns and in accommodating individualized learning paces, resulting in limited accuracy and diversity in recommendations. To tackle these challenges, we propose Neural Re-ranking for Diversified Exercise Recommendation (in short, NR4DER). NR4DER first leverages the mLSTM model to improve the effectiveness of the exercise filter module. It then employs a sequence enhancement method to enhance the representation of inactive students, accurately matches students with exercises of appropriate difficulty. Finally, it utilizes neural re-ranking to generate diverse recommendation lists based on individual students' learning histories. Extensive experimental results indicate that NR4DER significantly outperforms existing methods across multiple real-world datasets and effectively caters to the diverse learning pace of students.