EduRank: A Collaborative Filtering Approach to Personalization in E-learning.
Avi Segal, Ziv Katzir, Kobi Gal, Guy Shani, Bracha Shapira · 2014
The growing prevalence of e-learning systems and on-line courses has made educational material widely accessible to students of vary-ing abilities, backgrounds and styles. There is thus a growing need to accomodate for individual differences in such e-learning sys-tems. This paper presents a new algorithm for personliazing educa-tional content to students that combines collaborative filtering algo-rithms with social choice theory. The algorithm constructs a “dif-ficulty ” 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, num-ber 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 ex-pert. 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. 1.