A difficulty ranking approach to personalization in E-learning.

• On-line courses have 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 e-learning systems. • We present a new algorithm called EduRank for personalizing educational content to students in e-learning systems that combines collaborative filtering with social choice theory. • EduRank constructs a difficulty ranking for each student by aggregating the rankings of similar students using different aspects of their performance on common questions. • The algorithm was tested on two data sets containing thousands of students and a million records and was able to outperform the state-of-the-art ranking approaches as well as a domain expert. • EduRank was embedded in a real classroom. It was shown to lead students to solve more difficult questions than an ordering by a domain expert, without reducing their performance. The prevalence of e-learning systems and on-line courses has made educational material widely accessible to students of varying abilities and backgrounds. There is thus a growing need to accommodate for individual differences in e-learning systems. This paper presents an algorithm called EduRank for personalizing educational content to students that combines a collaborative filtering algorithm with voting methods. EduRank constructs a difficulty ranking for each student by aggregating the rankings of similar students using different aspects of their performance on common questions. These aspects include grades, number of retries, and time spent solving questions. It infers a difficulty ranking directly over the questions for each student, rather than ordering them according to the student's predicted score. The EduRank algorithm was tested on two data sets containing thousands of students and a million records. It was able to outperform the state-of-the-art ranking approaches as well as a domain expert. EduRank was used by students in a classroom activity, where a prior model was incorporated to predict the difficulty rankings of students with no prior history in the system. It was shown to lead students to solve more difficult questions than an ordering by a domain expert, without reducing their performance. [ABSTRACT FROM AUTHOR]