Combining Elo Rating and Collaborative Filtering to improve Learner Ability Estimation in an e-learning Context

Abstract

This master thesis details a proof-of-concept software system for estimating student abilities. The system combines Elo rating and collaborative filtering in order to present students with tasks that best reflect their knowledge. The system was designed and developed successfully. A substantial amount of 8th-grade math tasks were added to the system, and the system was tested on a relevant student population without technical issues. However, the size of the experiment was not sufficient to reach a conclusion regarding whether or not the Elo rating algorithm performs better when combined with collaborative filtering.