Shaleeza Sohail¹, Atif Alvi^2,*, Aasia Khanum³

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3211-3225, 2022, DOI:10.32604/cmc.2022.023560 - 07 December 2021

Abstract Major issues currently restricting the use of learning analytics are the lack of interpretability and adaptability of the machine learning models used in this domain. Interpretability makes it easy for the stakeholders to understand the working of these models and adaptability makes it easy to use the same model for multiple cohorts and courses in educational institutions. Recently, some models in learning analytics are constructed with the consideration of interpretability but their interpretability is not quantified. However, adaptability is not specifically considered in this domain. This paper presents a new framework based on hybrid statistical More >

Suraya Hamid¹, Shahrul Nizam Ismail¹, Muzaffar Hamzah^2,*, Asad W. Malik³

Computer Systems Science and Engineering, Vol.42, No.1, pp. 335-350, 2022, DOI:10.32604/csse.2022.021927 - 02 December 2021

Abstract Learning analytics is an emerging technique of analysing student participation and engagement. The recent COVID-19 pandemic has significantly increased the role of learning management systems (LMSs). LMSs previously only complemented face-to-face teaching, something which has not been possible between 2019 to 2020. To date, the existing body of literature on LMSs has not analysed learning in the context of the pandemic, where an LMS serves as the only interface between students and instructors. Consequently, productive results will remain elusive if the key factors that contribute towards engaging students in learning are not first identified. Therefore, More >

Shahrul Nizam Ismail¹, Suraya Hamid^1,*, Muneer Ahmad¹, A. Alaboudi², Nz Jhanjhi³

Computer Systems Science and Engineering, Vol.37, No.1, pp. 73-87, 2021, DOI:10.32604/csse.2021.015261 - 05 February 2021

Abstract Learning analytics is a rapidly evolving research discipline that uses the insights generated from data analysis to support learners as well as optimize both the learning process and environment. This paper studied students’ engagement level of the Learning Management System (LMS) via a learning analytics tool, student’s approach in managing their studies and possible learning analytic methods to analyze student data. Moreover, extensive systematic literature review (SLR) was employed for the selection, sorting and exclusion of articles from diverse renowned sources. The findings show that most of the engagement in LMS are driven by educators. More >