Exploring the relationship between clout and cognitive processing in MOOC discussion forums

Abstract

The purpose of this study was to explore the relationship between clout and cognitive processing in massive open online course (MOOC) discussion forum posts. Cognitive processing, a category variable generated by the automated text analysis tool, Linguistic Inquiry Word Count (LIWC), is made up of six sub‐scores (insight, causation, discrepancy, tentativeness, certainty and differentiation). Clout is a nontransparent summary variable in LIWC that can be used to understand the level of confidence conveyed in the text. Because clout is nontransparent, we do not know the algorithm used to calculate its value. To better understand this variable, this study examined cognitive processing alongside clout. In this study, a series of linear mixed models were fitted to evaluate, after controlling for gender, degree and type of post, if the focal relationship between each sub‐score of cognitive processing as the predictor variables and clout as the dependent variable changed across courses with different pacing (self‐paced or instructor‐paced). Next, the focal relationship between each predictor and clout was examined with or without regard to pacing. Findings showed words classified as showing discrepancy, certainty or differentiation were negatively associated with clout scores.

Looking at MOOC discussion data to uncover the relationship between discussion pacings, learners’ cognitive presence and learning achievements

“…Instead, the learner-paced discussion might mainly focus on learners' needs such as confusion and curiosity in the learning process. According to the previous study (Moore et al, 2021), the different discussion pacing types might result in different cognitive levels which is related to learners' high order thinking and learning outcomes. Thus, to better engage learners and improve their cognitive levels and learning achievements in MOOCs, discussion pacings need to be designed and considered (Lee & Recker, 2021).…”

“…On the other hand, to analyze the cognitive levels in interactive discussion data, Garrison et al (2001) also provide an efficient instrument based on the CoI (Community of Inquiry) framework, which has been very widely used to analyze discussions data, especially in MOOCs (Hu et al, 2020). Furthermore, discussion pacings (i.e., instructor-paced or learner-paced) in a MOOC forum typically have a key effect on learning performance and cognitive processing (Moore et al, 2021). The instructor-paced discussion means that after completing a lesson, the instructor will post a discussion task related to course content for promoting learners' interaction and reflection.…”

Contrasting compulsive behaviour: Computerized text analysis of compulsion narratives

“…While the values for clout, both for those with compulsive gambling and buying fall below 50, they are both relatively low, however the significant difference between the groups indicates that those with compulsive buying exhibit more confidence and possibly greater expertise (Pennebaker et al, 2015) in the narratives of their compulsion. It should be noted that the use of the words confidence or tentative , is not to pass any judgment on the content of the text or to suggest that certain narratives are more accurate than others, but rather it considers how comfortable or confident the author is when discussing the subject that they are writing about (Moore et al, 2020).…”

“…Their narratives are seen as being very authentic and honest and present a personal and vulnerable account (Pennebaker, 2011) of the individuals’ compulsive behaviours. The experience-related narratives of those with compulsive gambling typically express doubt and uncertainty, as evidenced by the low clout indicator (Moore et al, 2020). The tone used in the narratives of those with a compulsive gambling condition is particularly negative, consistent with the fact that nearly 3% of all words used in the narrative present negative emotions, using words such as ‘hate’ and ‘worthless’ (Bilgihan et al, 2017).…”

Implications of cognitive‐oriented language within posttrauma nightmare narratives

“…The negative relationship between cognitive processing words in PTNM and PTCI scores at baseline can mostly be understood by the behavior of the subscales within cognitive processing, namely tentative, discrepancy, and insight words. Lower levels of tentative and discrepancy words would indicate that a person may be more confident, or have a perceived greater clarity in their understanding, about what they are saying (Moore et al, 2020). The lower levels of tentative and discrepancy words in the PTNM may also support the finding of a higher analytic tone found in the PTNM.…”