Weakly Supervised Models of Aspect-Sentiment for Online Course Discussion Forums

Abstract

Massive open online courses (MOOCs) are redefining the education system and transcending boundaries posed by traditional courses. With the increase in popularity of online courses, there is a corresponding increase in the need to understand and interpret the communications of the course participants. Identifying topics or aspects of conversation and inferring sentiment in online course forum posts can enable instructor interventions to meet the needs of the students, rapidly address course-related issues, and increase student retention. Labeled aspect-sentiment data for MOOCs are expensive to obtain and may not be transferable between courses, suggesting the need for approaches that do not require labeled data. We develop a weakly supervised joint model for aspectsentiment in online courses, modeling the dependencies between various aspects and sentiment using a recently developed scalable class of statistical relational models called hinge-loss Markov random fields. We validate our models on posts sampled from twelve online courses, each containing an average of 10,000 posts, and demonstrate that jointly modeling aspect with sentiment improves the prediction accuracy for both aspect and sentiment.

A Variational Approach to Weakly Supervised Document-Level Multi-Aspect Sentiment Classification

“…Meng et al (2018) proposed a weakly supervised text classification method which can take label surface names, class-related keywords, or a few labeled documents as supervision. Ramesh et al (2015) developed a weakly supervised joint model to identify aspects and the corresponding sentiment polarities in online courses. They treat aspect (sentiment) related seed words as weak supervision.…”

Exploring Students’ Engagement Patterns in SPOC Forums and their Association with Course Performance

“…Chaplot, Rhim, and Kim (2015) incorporated the emotional scores of posts into a neural network to predict student attrition in MOOCs. The joint modelling of emotion and topics has drawn increasing attention on capturing latent emotional feedback and requirements during the processes of problem-solving Ramesh, Dan, Huang, Daume, & Getoor, 2014;Ramesh et al, 2015). Besides, confusion has been viewed as an emotion to infer students' potential difficulties and final learning performance in online learning.…”

“…As a reflection of student learning experience in discussions, a variety of discourse behaviors could be explored through forum posts, such as interactive postings between peers (Chiu & Hew, 2018), exchanges of learning feelings (Ramesh, Kumar, Foulds, & Getoor, 2015;Wen et al, 2014), expressing opinions (Ekahitanond, 2014;Liu, Zhang, Sun, Cheng, Peng, & Liu, 2016), etc. To get some insights of students' engagement in SPOC forums, we will explore students' engagement patterns in forum discussions in terms of two major factors: behavior and emotion.…”

A Novel Cascade Model for Learning Latent Similarity from Heterogeneous Sequential Data of MOOC

“…For example, dropout predicting (Qiu et al, 2016), sentiment analysis of learning gains (Ramesh et al, 2015), instructor intervention (Chaturvedi et al, 2014) and answer recommendation (Jenders et al, 2016), etc. Particularly, (Agrawal et al, 2015) considers a similar task as ours, which is to recommend video clips to threads.…”

“…However their effectiveness is limited (Rossi and Gnawali, 2014), because learners have few incentives to tag threads. Recently, machine learning solutions have been proposed, e.g., content-related thread identification (Wise et al, 2016), confusion classification (Agrawal et al, 2015) and sentiment classification (Ramesh et al, 2015). However they are developed for specific research problems and cannot be applied to our problem.…”