Can MOOC Instructor Be Portrayed by Semantic Features? Using Discourse and Clustering Analysis to Identify Lecture-Style of Instructors in MOOCs

Frontiers in Psychology

Junyi Li¹  Ye Zhang¹  Kaiji Zhou¹  Lin Lu¹  Changcheng Wu⁴  Chunmei Lan⁵  Xuechen Ding⁶  Yingzhao Wang⁷ 

[1] College of Psychology, Sichuan Normal University, Chengdu, China;Department of Psychology, Shanghai Normal University, Shanghai, China;Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, China;School of Computer Science, Sichuan Normal University, Chengdu, China;School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China;The Research Base of Online Education for Shanghai Middle and Primary Schools, Shanghai, China;Xiaoping Executive Leadership Academy, Guangan, China;

关键词: semantic features;    lecture style;    MOOCs;    LIWC;    Coh-Metrix;   

DOI  :  10.3389/fpsyg.2021.751492

来源: DOAJ

【 摘 要 】

Nowadays, most courses in massive open online course (MOOC) platforms are xMOOCs, which are based on the traditional instruction-driven principle. Course lecture is still the key component of the course. Thus, analyzing lectures of the instructors of xMOOCs would be helpful to evaluate the course quality and provide feedback to instructors and researchers. The current study aimed to portray the lecture styles of instructors in MOOCs from the perspective of natural language processing. Specifically, 129 course transcripts were downloaded from two major MOOC platforms. Two semantic analysis tools (linguistic inquiry and word count and Coh-Metrix) were used to extract semantic features including self-reference, tone, effect, cognitive words, cohesion, complex words, and sentence length. On the basis of the comments of students, course video review, and the results of cluster analysis, we found four different lecture styles: “perfect,” “communicative,” “balanced,” and “serious.” Significant differences were found between the different lecture styles within different disciplines for notes taking, discussion posts, and overall course satisfaction. Future studies could use fine-grained log data to verify the results of our study and explore how to use the results of natural language processing to improve the lecture of instructors in both MOOCs and traditional classes.

【 授权许可】

Unknown