IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v14y2022i8p218-d870808.html
   My bibliography  Save this article

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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Xieling Chen & Fu Lee Wang & Gary Cheng & Man-Kong Chow & Haoran Xie, 2022. "Understanding Learners’ Perception of MOOCs Based on Review Data Analysis Using Deep Learning and Sentiment Analysis," Future Internet, MDPI, vol. 14(8), pages 1-17, July.
  • Handle: RePEc:gam:jftint:v:14:y:2022:i:8:p:218-:d:870808
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/14/8/218/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1999-5903/14/8/218/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Park, Sangwon & Nicolau, Juan L., 2015. "Asymmetric effects of online consumer reviews," Annals of Tourism Research, Elsevier, vol. 50(C), pages 67-83.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Park, Sangwon & Nicolau, Juan L., 2017. "Effects of general and particular online hotel ratings," Annals of Tourism Research, Elsevier, vol. 62(C), pages 114-116.
    2. Ian Sutherland & Youngseok Sim & Seul Ki Lee & Jaemun Byun & Kiattipoom Kiatkawsin, 2020. "Topic Modeling of Online Accommodation Reviews via Latent Dirichlet Allocation," Sustainability, MDPI, vol. 12(5), pages 1-15, February.
    3. Shan, Wei & Qiao, Tong & Zhang, Mingli, 2020. "Getting more resources for better performance: The effect of user-owned resources on the value of user-generated content," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    4. Ghimire, Binam & Shanaev, Savva & Lin, Zhibin, 2022. "Effects of official versus online review ratings," Annals of Tourism Research, Elsevier, vol. 92(C).
    5. Liu, Xiao & Li, Ming-Yang, 2024. "Sustainable service product design method: Focus on customer demands and triple bottom line," Journal of Retailing and Consumer Services, Elsevier, vol. 80(C).
    6. Angela Aerry Choi & Daegon Cho & Dobin Yim & Jae Yun Moon & Wonseok Oh, 2019. "When Seeing Helps Believing: The Interactive Effects of Previews and Reviews on E-Book Purchases," Information Systems Research, INFORMS, vol. 30(4), pages 1164-1183, December.
    7. Guo, Yue & Barnes, Stuart J. & Jia, Qiong, 2017. "Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent dirichlet allocation," Tourism Management, Elsevier, vol. 59(C), pages 467-483.
    8. Sunyoung Hlee & Hanna Lee & Chulmo Koo, 2018. "Hospitality and Tourism Online Review Research: A Systematic Analysis and Heuristic-Systematic Model," Sustainability, MDPI, vol. 10(4), pages 1-27, April.
    9. Yani Wang & Jun Wang & Tang Yao, 2019. "What makes a helpful online review? A meta-analysis of review characteristics," Electronic Commerce Research, Springer, vol. 19(2), pages 257-284, June.
    10. Etienne Schraven & Elco van Burg & Marco van Gelderen & Enno Masurel, 2020. "Predictions of Crowdfunding Campaign Success: The Influence of First Impressions on Accuracy and Positivity," JRFM, MDPI, vol. 13(12), pages 1-16, December.
    11. Stefan Applis, 2022. "Crises around Concepts of Hospitality in the Mountainous Region of Svaneti in the North of Georgia," Tourism and Hospitality, MDPI, vol. 3(2), pages 1-19, May.
    12. Ariana Furtado & Ricardo F. Ramos & Bruno Maia & Joana Martinho Costa, 2022. "Predictors of Hotel Clients’ Satisfaction in the Cape Verde Islands," Sustainability, MDPI, vol. 14(5), pages 1-13, February.
    13. Ao Shen & Peng Wang & Yongyuan Ma, 2022. "When crowding‐in and when crowding‐out? The boundary conditions on the relationship between negative online reviews and online sales," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 43(6), pages 2016-2032, September.
    14. Kong, Juan & Lou, Chen, 2023. "Do cultural orientations moderate the effect of online review features on review helpfulness? A case study of online movie reviews," Journal of Retailing and Consumer Services, Elsevier, vol. 73(C).
    15. Liang, Sai & Li, Chunxiao & Zhang, Xiaoxia & Li, Hui, 2020. "The snowball effect in online travel platforms: How does peer influence affect review posting decisions?," Annals of Tourism Research, Elsevier, vol. 85(C).
    16. Young Joon Park & Jaewoo Joo & Charin Polpanumas & Yeujun Yoon, 2021. "“Worse Than What I Read?” The External Effect of Review Ratings on the Online Review Generation Process: An Empirical Analysis of Multiple Product Categories Using Amazon.com Review Data," Sustainability, MDPI, vol. 13(19), pages 1-22, September.
    17. Oun-Joung Park & Jong-hyun Ryu, 2019. "Cognitive fit effects of online reviews on tourists’ information search," Information Technology & Tourism, Springer, vol. 21(3), pages 313-335, September.
    18. Taekyung Kim & Hwirim Jo & Yerin Yhee & Chulmo Koo, 2022. "Robots, artificial intelligence, and service automation (RAISA) in hospitality: sentiment analysis of YouTube streaming data," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(1), pages 259-275, March.
    19. Battisti, Enrico & Giachino, Chiara & Iaia, Lea & Stylianou, Ioanna & Papatheodorou, Andreas, 2022. "Air transport and mood in younger generations: The role of travel significance and COVID-19," Journal of Air Transport Management, Elsevier, vol. 103(C).
    20. Cai, Gangwei & Xu, Binyan & Lu, Feidong & Lu, Ye, 2023. "The promotion strategies and dynamic evaluation model of exhibition-driven sustainable tourism based on previous/prospective tourist satisfaction after COVID-19," Evaluation and Program Planning, Elsevier, vol. 101(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:14:y:2022:i:8:p:218-:d:870808. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.