Urgency Analysis of Learners’ Comments: an Automated Intervention Priority Model for MOOC

Abstract

Recently, the growing number of learners in Massive Open Online Course (MOOC) environments generate a vast amount of online comments via social interactions, general discussions, expressing feelings or asking for help. Concomitantly, learner dropout, at any time during MOOC courses, is very high, whilst the number of learners completing (completers) is low. Urgent intervention and attention may alleviate this problem. Analysing and mining learner comments is a fundamental step towards understanding their need for intervention from instructors. Here, we explore a dataset from a FutureLearn MOOC course. We find that (1) learners who write many comments that need urgent intervention tend to write many comments, in general. (2) The motivation to access more steps (i.e., learning resources) is higher in learners without many comments needing intervention, than that of learners needing intervention. (3) Learners who have many comments that need intervention are less likely to complete the course (13%). Therefore, we propose a new priority model for the urgency of intervention built on learner histories – past urgency, sentiment analysis and step access.