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Quality of interaction-based predictive model for support of online learning in pandemic situations

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Abstract

Higher education institutions place a lot of importance on their electronic learning systems. Educational institutions in Pakistan and other countries have adopted learning management systems (LMS) due to the coronavirus (COVID-19) pandemic scenario. The learning management system (LMS) establishes a digital learning environment where evaluation and user learning behavior must be carefully analyzed. The “quality of interaction” (QoI) of students is one of the main issues in LMS. Based on various usage matrices (such as the number of logins, clicks, total time spent on the LMS, and actions taken), a student’s level of interaction with the LMS can be determined. QoI is an essential predictor of the accomplishment of students’ final grades. Normally, to examine the effectiveness of LMS usage on students’ learning performance, studies have relied on data gathered from users via surveys. However, the data gathered through surveys are typically associated with the risk of distortion or low quality. Therefore, in order to evaluate and predict the quality of interaction in terms of usage matrices, our proposed work analyzed data from the Moodle LMS at “Hazara University” (HU) for the law and English departments’ courses. This research aims to assess and forecast the quality of student interaction within an LMS by analyzing usage metrics. Unlike traditional survey-based approaches, we explored the predictive performance of LSTM (Long Short-Term Memory), Exponential Smoothing method (ETS), and ARIMA (Autoregressive Integrated Moving Average) methods to predict the weekly LMS usage factors of students. ARIMA and ETS produce better prediction results than LSTM for weekly predictions. Moreover, LSTM model training took considerable computational time for provided datasets.

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Funding

This research work was not supported by any funding.

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Authors and Affiliations

  1. Department of Computer Science and Information Technology, Hazara University, Mansehra, KPK, Pakistan

    Faiza Mumtaz, Ali Imran Jehangiri & Zulfiqar Ahmad

  2. Department of Telecommunication, Hazara University, Mansehra, KPK, Pakistan

    Waqar Ishaq

  3. Department of Networks and Communications, Faculty of Information Technology, Misurata University, Misurata, Libya

    Omar Imhemed Alramli

  4. Department of Computer Science, Suleyman Demirel University (SDU), Almaty, Kazakhstan

    Mohammed Alaa Ala’anzy

  5. Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia

    Rania M. Ghoniem

  6. Department of Computer, Mansoura University, Mansoura, 35516, Egypt

    Rania M. Ghoniem

Authors
  1. Faiza Mumtaz
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  2. Ali Imran Jehangiri
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  4. Zulfiqar Ahmad
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  5. Omar Imhemed Alramli
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  6. Mohammed Alaa Ala’anzy
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  7. Rania M. Ghoniem
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Contributions

F.M., A.I.J., and M.A.A. contributed to conceptualization, software, validation, and writing original draft. W.I., Z.A., R.M.G., and O.I.A. performed formal analysis, supervision, project administration, and review and editing article. All authors reviewed the manuscript.

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Correspondence to Zulfiqar Ahmad.

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Mumtaz, F., Jehangiri, A.I., Ishaq, W. et al. Quality of interaction-based predictive model for support of online learning in pandemic situations. Knowl Inf Syst 66, 1777–1805 (2024). https://doi.org/10.1007/s10115-023-01995-3

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