A New Web-Based Personalized Learning System Improves Student Learning Outcomes

Abstract

Although there is increasing acceptance that personalization improves learning outcomes, there is still limited experimental evidence supporting this claim. The aim of this study was to implement and evaluate the effectiveness of an adaptive recommendation system for Singapore primary and secondary education. The system leverages users trace data and learning analytics to generate assessment worksheets customized to individual learner’s proficiency. Analysis of online data is used to measure students’ skill levels in specific knowledge domains and monitor their progress. Notably, online measurements correlate positively with offline academic outcomes. A randomized controlled trial conducted on forty-three primary school students revealed statistically significant improvement on academic performance of a group of students receiving personalized content over a control group using non-adaptive material. The authors conclude that the reported recommender system successfully helps students improve their academic achievements.