Mining and visualizing large-scale course reviews of LMOOCs learners through structural topic model

Abstract

Understanding Language Massive Online Open Courses (LMOOCs) learners’ subjective evaluation is essential for language teachers to improve their instructional design, examine the teaching and learning effects, and promote course quality. The present research uses word frequency and co-occurrence analysis, comparative keyword analysis, and structural topic modeling to analyze 69,232 reviews from one Massive Online Open Courses (MOOCs) platform in China. Learners hold a strongly positive overall perception of LMOOCs. Four negative topics appear more commonly in negative reviews as compared to positive ones. Additionally, variations in negative reviews across course types are examined, indicating that learners’ main concerns about high-level LMOOCs include teaching/learning problems, learner expectation, and learner attitude, whereas learners of low-level courses are more critical in the topic of scholarship ability. Our study contributes to the LMOOCs study by providing a better understanding of learners’ perceptions using rigorous statistical techniques.

Exploring Mental Stress Expressions in Online Communities: A Subreddit Analysis

“…Our study employed functions from the quanteda package, specifically designed for handling text data, to uncover themes within stress-related posts. Our approach aligns with similar methods in other studies utilizing the quanteda package for theme extraction [13,[33][34][35][36][37][38]. The current study considered the imbalance in the total number of posts across different subreddits and addressed this by identifying themes for each subreddit before and after January 2020.…”

“…A post with a negative score is considered a stress post. The average polarity score is mapped to the stress level scales [26], including very low stress (0-20), low stress (21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40), moderate stress (41-60), high stress (61-80), and very high stress (81-100). It is worth noting that the average polarity score has an interval of [-1.0, 1.0].…”

Data-driven analytics for student reviews in China’s higher vocational education MOOCs: A quality improvement perspective

“…Hew et al [ 26 ] analyze the relationship between students’ implicit demands and satisfaction in MOOC reviews. Yang [ 27 ] uses word frequency and co-occurrence analysis, comparative keyword analysis, and structural topic modeling to analyze reviews from one Massive Online Open Courses (MOOCs) platform in China.…”

Research and Analysis of Blended Teaching based on the Community of Inquiry Model

“…To explore the current research status of COI theory, this article uses keyword cooccurrence analysis to discover relevant research topics in this field.Keyword cooccurrence analysis involves counting the number of times a group of keywords appear together in the same literature, and conducting cluster analysis on these keywords to reflect their interrelationships [7].This article mainly selects keywords with a frequency greater than or equal to 5, and uses VOSviewer to visually analyze the cooccurrence relationship of keywords in COI related literature from 2019 to 2023. The resulting co-occurrence network of keywords is shown in the Fig4.Each circle in the figure represents a keyword node.…”