# A difficulty ranking approach to personalization in E-learning

## TL;DR

This paper introduces EduRank, an algorithm that personalizes e-learning content by ranking question difficulty for each student, improving engagement and performance by leveraging collaborative filtering and student performance data.

## Contribution

The paper presents EduRank, a novel difficulty ranking algorithm that personalizes educational content by aggregating similar students' performance data, outperforming existing methods and a domain expert.

## Key findings

- EduRank outperforms state-of-the-art ranking methods.
- Students using EduRank solve more difficult questions.
- EduRank effectively personalizes content for students with no prior history.

## Abstract

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.