Quality of interaction-based predictive model for support of online learning in pandemic situations
Mumtaz F. Jehangiri A.I. Ishaq W. Ahmad Z. Alramli O.I. Ala’anzy M.A. Ghoniem R.M.
March 2024Springer Science and Business Media Deutschland GmbH
Knowledge and Information Systems
2024#66Issue 31777 - 1805 pp.
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.
ARIMA , Coronavirus , Electronic learning , ETS , Hazara University , Higher education system , Learning management system , LSTM , Quality of interaction , RMSE
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Department of Computer Science and Information Technology, Hazara University, KPK, Mansehra, Pakistan
Department of Telecommunication, Hazara University, KPK, Mansehra, Pakistan
Department of Networks and Communications, Faculty of Information Technology, Misurata University, Misurata, Libya
Department of Computer Science, Suleyman Demirel University (SDU), Almaty, Kazakhstan
Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
Department of Computer, Mansoura University, Mansoura, 35516, Egypt
Department of Computer Science and Information Technology
Department of Telecommunication
Department of Networks and Communications
Department of Computer Science
Department of Information Technology
Department of Computer
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