EduRank: A Collaborative Filtering Approach to Personalization in E-learning

A preliminary of this work was presented at HAIDM 2014. The 2014 workshop on Human-Agent Interaction Design and Models was co-organised by SmartSociety.

This work was the winner of the “Best Student Paper Award” at the Seventh International Conference on Educational Data Mining (EDM 2014).

Abstract: The growing prevalence of e-learning systems and on-line courses has made educational material widely accessible to students of varying abilities, backgrounds and styles. There is thus a growing need to accommodate for individual differences in such e-learning systems. This paper presents a new algorithm for personalizing educational content to students that combines collaborative filtering algorithms with social choice theory. The algorithm constructs a “difficulty” ranking over questions for a target student by aggregating the ranking of similar students, as measured by different aspects of their performance on common past questions, such as grades, number of retries, and time spent solving questions. It infers a difficulty ranking directly over the questions for a target student, rather than ordering them according to predicted performance, which is prone to error. The algorithm was tested on two large real world data sets containing tens of thousands of students and a million records. Its performance was compared to a variety of personalization methods as well as a non-personalized method that relied on a domain expert. It was able to significantly outperform all of these approaches according to standard information retrieval metrics. Our approach can potentially be used to support teachers in tailoring problem sets and exams to individual students and students in informing them about areas they may need to strengthen..

Citation: EduRank: A Collaborative Filtering Approach to Personalization in E-learning Avi Segal, Ziv Katzir, Ya’akov Gal, Guy Shani and Bracha Shapira. EduRank: A Collaborative Filtering Approach to Personalization in E-learning. Seventh International Conference on Educational Data Mining (EDM 2014), London, England, July 2014.

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About P. Andreadis

Pre-Doctoral Research Assistant in AI and Social Computation @ University of Edinburgh.

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