SBTM: A joint sentiment and behaviour topic model for online course discussion forums

Abstract

Large quantities of textual posts are increasingly generated in course discussion forums, and the accumulation of these data greatly increases the cognitive loads on online participants. It is imperative for them to automatically identify the potential semantic information derived from these textual discourse interactions. Moreover, existing topic models can discover the latent topics or sentimental polarities from textual data, but these models typically ignore the interactive ways of discussing topics, thus making it difficult to further construct topics’ semantic space from the perspective of document generation. To solve this issue, we proposed a joint sentiment and behaviour topic model called SBTM, which was an unsupervised approach for automatic analysis of learners’ discussed posts. The results demonstrated that SBTM was quantitatively effective on both model generalisation and topic exploration, and rich topic content was qualitatively characterised. Furthermore, the model can be potentially employed in some practical applications, such as information summarisation and behaviour-oriented personalised recommendation.

Conflicted about online learning?: Using sentiment analysis to explore learner approach-avoidance motivation

“…Sentiment analysis is increasingly used in research publications, increasing from one in 2005 to nearly 2,000 in 2019 (Hajiali, 2020). Originally widely used in consumer behavior research, SA is now applied in educational settings to generate a rich understanding of learner attitudes (Peng et al. , 2020).…”

“…Sentiment analysis is increasingly used in research publications, increasing from one in 2005 to nearly 2,000 in 2019 (Hajiali, 2020). Originally widely used in consumer behavior research, SA is now applied in educational settings to generate a rich understanding of learner attitudes (Peng et al, 2020). As a general practice, this approach classifies text comments into positive, negative and neutral categories, comparing ratios or extracting meaning (Rani and Kumar, 2017;Toço glu and Onan, 2020).…”

Effective interactive engagement strategies for MOOC forum discussion: A self-efficacy perspective

“…Some have attempted to predict MOOC learners' achievement using behavioral indicators such as the number of comments and replies [5], the number of likes [7], and downloads [6]. Second, linking outcomes to MOOC learners' behaviors may have some methodological flaws because the actual contributions of participating in online discussion forums can be difficult to measure without a baseline study [8,9]. Third, its positive contributions to learning outcomes [10] have attracted increased attention from researchers, with more studies currently being conducted in the fields of sciences, economics, and business-related subjects than in the arts and humanities, such as biology [11], medicine [12], statistics [13], and business strategy [7].…”