IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i3p2049-d1043098.html
   My bibliography  Save this article

Statistical Assessment on Student Engagement in Asynchronous Online Learning Using the k -Means Clustering Algorithm

Author

Listed:
  • Sohee Kim

    (Center for Teaching and Learning, Kyung Hee University, Seoul 02447, Republic of Korea)

  • Sunghee Cho

    (Center for Teaching and Learning, Kyung Hee University, Seoul 02447, Republic of Korea)

  • Joo Yeun Kim

    (Center for Teaching and Learning, Kyung Hee University, Seoul 02447, Republic of Korea)

  • Dae-Jin Kim

    (Department of Architectural Engineering, Kyung Hee University, Yongin 17104, Republic of Korea)

Abstract

In this study, statistical assessment was performed on student engagement in online learning using the k -means clustering algorithm, and their differences in attendance, assignment completion, discussion participation and perceived learning outcome were examined. In the clustering process, three features such as the behavioral, emotional and cognitive aspects of student engagement were considered. Data for this study were collected from undergraduate students who enrolled in an asynchronous online course provided by Kyung Hee University in Republic of Korea in the fall semester of 2021. The students enrolled in the asynchronous online course were classified into two clusters with low and high engagement perceptions. In addition, their differences in attendance, assignment completion, discussion participation, interactions and perceived learning outcome were analyzed. The results of this study indicate that quantitative indicators on students’ online behaviors are not sufficient evidence to measure the level of student engagement and the students enrolled in the asynchronous online course were classified into two groups with low and high engagement perceptions. It is recommended that online instructors consider various strategies to facilitate interaction for the students with low engagement perceptions.

Suggested Citation

  • Sohee Kim & Sunghee Cho & Joo Yeun Kim & Dae-Jin Kim, 2023. "Statistical Assessment on Student Engagement in Asynchronous Online Learning Using the k -Means Clustering Algorithm," Sustainability, MDPI, vol. 15(3), pages 1-14, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:2049-:d:1043098
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/3/2049/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/3/2049/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Józef Ober & Anna Kochmańska, 2022. "Remote Learning in Higher Education: Evidence from Poland," IJERPH, MDPI, vol. 19(21), pages 1-35, November.
    2. Maryam Alavi & George M. Marakas & Youngjin Yoo, 2002. "A Comparative Study of Distributed Learning Environments on Learning Outcomes," Information Systems Research, INFORMS, vol. 13(4), pages 404-415, December.
    3. Sohee Kim & Dae-Jin Kim, 2021. "Structural Relationship of Key Factors for Student Satisfaction and Achievement in Asynchronous Online Learning," Sustainability, MDPI, vol. 13(12), pages 1-14, June.
    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. Benedikt Berger & Martin Adam & Alexander Rühr & Alexander Benlian, 2021. "Watch Me Improve—Algorithm Aversion and Demonstrating the Ability to Learn," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 63(1), pages 55-68, February.
    2. Mohamed Zine & Fouzi Harrou & Mohammed Terbeche & Mohammed Bellahcene & Abdelkader Dairi & Ying Sun, 2023. "E-Learning Readiness Assessment Using Machine Learning Methods," Sustainability, MDPI, vol. 15(11), pages 1-22, June.
    3. Zhou Yan & Meng Na & Syed Shah Alam & Mohammad Masukujjaman & Ye Xiao Lu, 2022. "Teacher Competencies and School Improvement Specialist Coaching (SISC+) Programme in Malaysia as a Model for Improvement of Quality Education in China," Sustainability, MDPI, vol. 14(23), pages 1-17, December.
    4. Hüseyin Uzunboylu & Gönül Akçamete & Nilgün Sarp & Mukaddes Demirok, 2022. "Primary Schoolteachers’ Opinions about Gifted Education Programmes in Distance Education," Sustainability, MDPI, vol. 14(24), pages 1-25, December.
    5. Lorena Espina-Romero & Doile Ríos Parra & José Gregorio Noroño-Sánchez & Gloria Rojas-Cangahuala & Luz Emerita Cervera Cajo & Pedro Alfonso Velásquez-Tapullima, 2024. "Navigating Digital Transformation: Current Trends in Digital Competencies for Open Innovation in Organizations," Sustainability, MDPI, vol. 16(5), pages 1-19, March.
    6. Fenglong Yu & Qian Chen & Bing Hou, 2021. "Understanding the Impacts of Chinese Undergraduate Tourism Students’ Professional Identity on Learning Engagement," Sustainability, MDPI, vol. 13(23), pages 1-16, December.
    7. Ching-Yeh Tsai & Der-Chiang Li, 2024. "Enterprise Implementation of Educational Technology: Exploring Employee Learning Behavior in E-Learning Environments," Sustainability, MDPI, vol. 16(4), pages 1-21, February.
    8. Jiying Han & Xiaohui Geng & Qinxiang Wang, 2021. "Sustainable Development of University EFL Learners’ Engagement, Satisfaction, and Self-Efficacy in Online Learning Environments: Chinese Experiences," Sustainability, MDPI, vol. 13(21), pages 1-14, October.
    9. Der-Chiang Li & Ching-Yeh Tsai, 2020. "An Empirical Study on the Learning Outcomes of E-Learning Measures in Taiwanese Small and Medium-Sized Enterprises (SMEs) Based on the Perspective of Goal Orientation Theory," Sustainability, MDPI, vol. 12(12), pages 1-23, June.
    10. Jenalyn Shigella G. Yandug & Erika Mae D. Costales & Ardvin Kester S. Ong, 2023. "A Conjoint Analysis Approach, Implications, and Mitigation Plans in Analyzing Students’ Preferences for Online Learning Delivery Types during the COVID-19 Pandemic for Engineering Students: A Case Stu," Sustainability, MDPI, vol. 15(6), pages 1-15, March.
    11. Kwiatkowska-Ciotucha Dorota & Załuska Urszula, 2023. "Remote Education during the Covid-19 Pandemic in the Opinion of Students," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 27(2), pages 1-20, June.
    12. Kwok-Kee Wei & Hock-Hai Teo & Hock Chuan Chan & Bernard C. Y. Tan, 2011. "Conceptualizing and Testing a Social Cognitive Model of the Digital Divide," Information Systems Research, INFORMS, vol. 22(1), pages 170-187, March.
    13. Nissim Avissar, 2023. "Emergency Remote Teaching and Social–Emotional Learning: Examining Gender Differences," Sustainability, MDPI, vol. 15(6), pages 1-14, March.
    14. Bernard Fallery & Roxana Ologeanu-Taddei & Sylvie Gerbaix, 2010. "Acceptance and Appropriation of Videoconferencing for E-training: an Empirical Investigation," Post-Print hal-00773583, HAL.
    15. Radhika Santhanam & Sharath Sasidharan & Jane Webster, 2008. "Using Self-Regulatory Learning to Enhance E-Learning-Based Information Technology Training," Information Systems Research, INFORMS, vol. 19(1), pages 26-47, March.
    16. Aleksandra Kuzior & Dariusz Krawczyk & Kateryna Onopriienko & Yuriy Petrushenko & Iryna Onopriienko & Volodymyr Onopriienko, 2023. "Lifelong Learning as a Factor in the Country’s Competitiveness and Innovative Potential within the Framework of Sustainable Development," Sustainability, MDPI, vol. 15(13), pages 1-12, June.
    17. Yogi Tri Prasetyo & Ardvin Kester S. Ong & Giero Krissianne Frances Concepcion & Francheska Mikaela B. Navata & Raphael Andrei V. Robles & Isaiash Jeremy T. Tomagos & Michael Nayat Young & John Franci, 2021. "Determining Factors Affecting Acceptance of E-Learning Platforms during the COVID-19 Pandemic: Integrating Extended Technology Acceptance Model and DeLone & McLean IS Success Model," Sustainability, MDPI, vol. 13(15), pages 1-16, July.
    18. Lei Zhu & Izak Benbasat & Zhenhui Jiang, 2010. "Let's Shop Online Together: An Empirical Investigation of Collaborative Online Shopping Support," Information Systems Research, INFORMS, vol. 21(4), pages 872-891, December.
    19. Kevin Bauer & Moritz von Zahn & Oliver Hinz, 2023. "Expl(AI)ned: The Impact of Explainable Artificial Intelligence on Users’ Information Processing," Information Systems Research, INFORMS, vol. 34(4), pages 1582-1602, December.
    20. Oluwatoyin Motunrayo ATOYEBI & Samuel Babajide ATOYEBI, 2022. "Do Technology-based Approaches Reduce Mathematics Anxiety? A Systematic Literature Review," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 6(10), pages 502-509, October.

    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:jsusta:v:15:y:2023:i:3:p:2049-:d:1043098. 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.