Understanding Learners’ Perception of MOOCs Based on Review Data Analysis Using Deep Learning and Sentiment Analysis

Xieling Chen, Fu Lee Wang, Gary Cheng, Man-Kong Chow and Haoran Xie
Additional contact information
Xieling Chen: School of Information Technology in Education, South China Normal University, Guangzhou 510631, China
Fu Lee Wang: School of Science and Technology, Hong Kong Metropolitan University, Hong Kong 999077, China
Gary Cheng: Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong 999077, China
Man-Kong Chow: STEAM Education & Research Centre, Lingnan University, Hong Kong 999077, China
Haoran Xie: Department of Computing and Decision Sciences, Lingnan University, Hong Kong 999077, China

Future Internet, 2022, vol. 14, issue 8, 1-17

Abstract: Massive open online courses (MOOCs) have exploded in popularity; course reviews are important sources for exploring learners’ perceptions about different factors associated with course design and implementation. This study aims to investigate the possibility of automatic classification for the semantic content of MOOC course reviews to understand factors that can predict learners’ satisfaction and their perceptions of these factors. To do this, this study employs a quantitative research methodology based on sentiment analysis and deep learning. Learners’ review data from Class Central are analyzed to automatically identify the key factors related to course design and implementation and the learners’ perceptions of these factors. A total of 186,738 review sentences associated with 13 subject areas are analyzed, and consequently, seven course factors that learners frequently mentioned are found. These factors include: “Platforms and tools”, “Course quality”, “Learning resources”, “Instructor”, “Relationship”, “Process”, and “Assessment”. Subsequently, each factor is assigned a sentimental value using lexicon-driven methodologies, and the topics that can influence learners’ learning experiences the most are decided. In addition, learners’ perceptions across different topics and subjects are explored and discussed. The findings of this study contribute to helping MOOC instructors in tailoring course design and implementation to bring more satisfactory learning experiences for learners.

Keywords: learners’ perceptions; MOOCs; review data analysis; deep learning; sentiment analysis (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1999-5903/14/8/218/pdf (application/pdf)
https://www.mdpi.com/1999-5903/14/8/218/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jftint:v:14:y:2022:i:8:p:218-:d:870808

Access Statistics for this article

Future Internet is currently edited by Ms. Grace You

More articles in Future Internet from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().