IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i22p14795-d968011.html
   My bibliography  Save this article

Students’ Academic Performance and Engagement Prediction in a Virtual Learning Environment Using Random Forest with Data Balancing

Author

Listed:
  • Khurram Jawad

    (College of Computing and Informatics, Saudi Electronic University, Riyadh 11673, Saudi Arabia)

  • Muhammad Arif Shah

    (Department of IT & Computer Science, Pak-Austria Fachhochshule Institute of Applied Sciences & Technology, Haripur 22650, Pakistan)

  • Muhammad Tahir

    (College of Computing and Informatics, Saudi Electronic University, Riyadh 11673, Saudi Arabia)

Abstract

Virtual learning environment (VLE) is vital in the current age and is being extensively used around the world for knowledge sharing. VLE is helping the distance-learning process, however, it is a challenge to keep students engaged all the time as compared to face-to-face lectures. Students do not participate actively in academic activities, which affects their learning curves. This study proposes the solution of analyzing students’ engagement and predicting their academic performance using a random forest classifier in conjunction with the SMOTE data-balancing technique. The Open University Learning Analytics Dataset (OULAD) was used in the study to simulate the teaching–learning environment. Data from six different time periods was noted to create students’ profiles comprised of assessments scores and engagements. This helped to identify early weak points and preempted the students performance for improvement through profiling. The proposed methodology demonstrated 5% enhanced performance with SMOTE data balancing as opposed to without using it. Similarly, the AUC under the ROC curve is 0.96, which shows the significance of the proposed model.

Suggested Citation

  • Khurram Jawad & Muhammad Arif Shah & Muhammad Tahir, 2022. "Students’ Academic Performance and Engagement Prediction in a Virtual Learning Environment Using Random Forest with Data Balancing," Sustainability, MDPI, vol. 14(22), pages 1-15, November.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:22:p:14795-:d:968011
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/22/14795/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/22/14795/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Daniel Burgos, 2019. "Background Similarities as a Way to Predict Students’ Behaviour," Sustainability, MDPI, vol. 11(24), pages 1-13, December.
    2. Diego Buenaño-Fernández & David Gil & Sergio Luján-Mora, 2019. "Application of Machine Learning in Predicting Performance for Computer Engineering Students: A Case Study," Sustainability, MDPI, vol. 11(10), pages 1-18, May.
    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. Shah Hussain & Muhammad Qasim Khan, 2023. "Student-Performulator: Predicting Students’ Academic Performance at Secondary and Intermediate Level Using Machine Learning," Annals of Data Science, Springer, vol. 10(3), pages 637-655, June.
    2. Arto O. Salonen & Annukka Tapani & Sami Suhonen, 2021. "Student Online Activity in Blended Learning: A Learning Analytics Perspective of Professional Teacher Education Studies in Finland," SAGE Open, , vol. 11(4), pages 21582440211, October.
    3. Shan Chen & Yuanzhao Ding, 2023. "A Machine Learning Approach to Predicting Academic Performance in Pennsylvania’s Schools," Social Sciences, MDPI, vol. 12(3), pages 1-13, February.
    4. Milan Ranđelović & Aleksandar Aleksić & Radovan Radovanović & Vladica Stojanović & Milan Čabarkapa & Dragan Ranđelović, 2022. "One Aggregated Approach in Multidisciplinary Based Modeling to Predict Further Students’ Education," Mathematics, MDPI, vol. 10(14), pages 1-23, July.
    5. Anjeela Jokhan & Aneesh A. Chand & Vineet Singh & Kabir A. Mamun, 2022. "Increased Digital Resource Consumption in Higher Educational Institutions and the Artificial Intelligence Role in Informing Decisions Related to Student Performance," Sustainability, MDPI, vol. 14(4), pages 1-17, February.
    6. Patricio Ramírez-Correa & Ari Mariano-Melo & Jorge Alfaro-Pérez, 2019. "Predicting and Explaining the Acceptance of Social Video Platforms for Learning: The Case of Brazilian YouTube Users," Sustainability, MDPI, vol. 11(24), pages 1-11, December.
    7. Adriano Bressane & Marianne Spalding & Daniel Zwirn & Anna Isabel Silva Loureiro & Abayomi Oluwatobiloba Bankole & Rogério Galante Negri & Irineu de Brito Junior & Jorge Kennety Silva Formiga & Liliam, 2022. "Fuzzy Artificial Intelligence—Based Model Proposal to Forecast Student Performance and Retention Risk in Engineering Education: An Alternative for Handling with Small Data," Sustainability, MDPI, vol. 14(21), pages 1-14, 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:14:y:2022:i:22:p:14795-:d:968011. 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.