Online Learning Path Recommender System for MOOCs

Abstract

Analysing behavioural data from massive open online courses (MOOCs) logs data can help in forming useful learning pathways for learners. In the existing literature, learning path recommender systems are designed using neural networks and deep learning. These recommender systems are complex and focus on designing accurate systems to recommend learning pathways to learners. In this work, we have designed a simple and fast online learning path recommender system that utilizes demographic and behavioural data of MOOC learners. This system identifies similar learners by utilizing demographic and behavioural data and recommends pathways of successful students to struggling students with the aim of helping learners to complete the MOOC. We evaluated our system pedagogically by interviewing the MOOC lecturer.