Unsupervised modeling for understanding MOOC discussion forums

Abstract

Massively Open Online Courses (MOOCs) have gained attention recently because of their great potential to reach learners. Substantial empirical study has focused on student persistence and their interactions with the course materials. However, most MOOCs include a rich textual dialogue forum, and these textual interactions are largely unexplored. Automatically understanding the nature of discussion forum posts holds great promise for providing adaptive support to individual students and to collaborative groups. This paper presents a study that applies unsupervised student understanding models originally developed for synchronous tutorial dialogue to MOOC forums. We use a clustering approach to group similar posts, compare the clusters with manual annotations by MOOC researchers, and further investigate clusters qualitatively. This paper constitutes a step toward applying unsupervised models to asynchronous communication, which can enable massive-scale automated discourse analysis and mining to better support students' learning.

Factors influencing peer learning and performance in MOOC asynchronous online discussion forum

“…One of the issues we need to address is that MOOCs lack a physical environment that allows real-time interaction between learners and instructors (Ezen-Can, Boyer, Kellogg, & Booth, 2015;Moon et al, 2014;Wong, Pursel, Divinsky, & Jansen, 2015), and hence online discussion forums in MOOCs play an important role in trying to bridge this gap (Moon et al, 2014). Researchers have also begun to examine the effects of forum participation on student learning.…”

An Exploratory Analysis of a Hybrid OSS Company's Forum in Search of Sales Leads

“…Over the past years, a large number of SA programs have been developed to discover the sentiment content of texts in various genres including movie reviews [15], student diaries [16], education forums [17], [18], and developer platforms. For example, in Parastou et al [19], they extracted the sentiment from user and developer mailing lists of two of the most successful and mature projects of the Apache software foundation.…”

Extraction of Relevant Terms and Learning Outcomes from Online Courses

“…This analysis using other models also based in NLP techniques may predict whether the learner will finish the course based on the comments posted in the forum. Another analysis proposed in [25] focused on classifying the posts depending on objective (question, reflection, reference, statement, feedback, etc. ).…”