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; Haoran XIE

doi:10.3390/fi14080218

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, Haoran XIE

^*Corresponding author for this work

Research output: Journal Publications › Journal Article (refereed) › peer-review

9 Citations (Scopus)

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.

Original language	English
Article number	218
Number of pages	17
Journal	Future Internet
Volume	14
Issue number	8
Early online date	25 Jul 2022
DOIs	https://doi.org/10.3390/fi14080218
Publication status	Published - 25 Jul 2022

Bibliographical note

Funding Information:
This research was funded by the Research Grants Council of the Hong Kong Special Adminstrative Region, China (UGC/FDS16/E01/19), The One-off Special Fund from Central and Faculty Fund in Support of Research from 2019/20 to 2021/22 (MIT02/19-20), The Interdisciplinary Research Scheme of Dean’s Research Fund 2021/22 (FLASS/DRF/IDS-3) of The Education University of Hong Kong, and The Faculty Research Grants (DB22B4), Lingnan University.

Publisher Copyright:
© 2022 by the authors.

Keywords

MOOCs
deep learning
learners’ perceptions
review data analysis
sentiment analysis

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.3390/fi14080218Licence: CC BY

Cite this

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title = "Understanding Learners{\textquoteright} Perception of MOOCs Based on Review Data Analysis Using Deep Learning and Sentiment Analysis",

abstract = "Massive open online courses (MOOCs) have exploded in popularity; course reviews are important sources for exploring learners{\textquoteright} 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{\textquoteright} 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{\textquoteright} review data from Class Central are analyzed to automatically identify the key factors related to course design and implementation and the learners{\textquoteright} 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{\textquoteright} learning experiences the most are decided. In addition, learners{\textquoteright} 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.",

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author = "Xieling CHEN and WANG, {Fu Lee} and Gary CHENG and Man-Kong CHOW and Haoran XIE",

note = "Funding Information: This research was funded by the Research Grants Council of the Hong Kong Special Adminstrative Region, China (UGC/FDS16/E01/19), The One-off Special Fund from Central and Faculty Fund in Support of Research from 2019/20 to 2021/22 (MIT02/19-20), The Interdisciplinary Research Scheme of Dean{\textquoteright}s Research Fund 2021/22 (FLASS/DRF/IDS-3) of The Education University of Hong Kong, and The Faculty Research Grants (DB22B4), Lingnan University. Publisher Copyright: {\textcopyright} 2022 by the authors.",

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AU - XIE, Haoran

N1 - Funding Information: This research was funded by the Research Grants Council of the Hong Kong Special Adminstrative Region, China (UGC/FDS16/E01/19), The One-off Special Fund from Central and Faculty Fund in Support of Research from 2019/20 to 2021/22 (MIT02/19-20), The Interdisciplinary Research Scheme of Dean’s Research Fund 2021/22 (FLASS/DRF/IDS-3) of The Education University of Hong Kong, and The Faculty Research Grants (DB22B4), Lingnan University. Publisher Copyright: © 2022 by the authors.

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N2 - 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.

AB - 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.

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

Abstract

Bibliographical note

Keywords

UN SDGs

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Cluster-level Social Emotion Classification Across Domains

Cite this

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

Abstract

Bibliographical note

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Projects

Cluster-level Social Emotion Classification Across Domains

Cite this