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Mining and visualizing large-scale course reviews of LMOOCs learners through structural topic model.
1School of Foreign Languages, Yantai University, Yantai, Shandong, China.
Plos One
|May 3, 2023
Summary
Learners generally have positive perceptions of Language Massive Open Online Courses (LMOOCs). However, negative feedback varies by course level, focusing on teaching and expectations for advanced courses, and scholarship for introductory ones.
Area of Science:
- Educational Technology
- Computational Linguistics
- Learning Analytics
Background:
- Understanding learner perceptions is crucial for improving Language Massive Open Online Courses (LMOOCs).
- Effective instructional design and quality assurance depend on analyzing learner feedback.
- Previous research has not fully explored the nuances of learner evaluations in Chinese LMOOCs.
Purpose of the Study:
- To analyze subjective evaluations of LMOOCs learners in China.
- To identify common themes in both positive and negative learner reviews.
- To investigate how negative feedback differs across various LMOOC levels.
Main Methods:
- Analysis of 69,232 learner reviews from a Chinese MOOC platform.
- Utilized word frequency, co-occurrence analysis, and comparative keyword analysis.
- Employed structural topic modeling to identify thematic patterns.
Main Results:
- Overall learner perception of LMOOCs is strongly positive.
- Four distinct negative topics were more prevalent in negative reviews.
- Learners of high-level LMOOCs expressed concerns about teaching, expectations, and attitude.
- Learners of low-level LMOOCs focused criticism on scholarship ability.
Conclusions:
- Learner perceptions of LMOOCs are predominantly positive but nuanced.
- Negative feedback patterns differ significantly between high-level and low-level courses.
- This study provides data-driven insights into LMOOC learner experiences for course improvement.
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