Smart analysis of learners performance using learning analytics for improving academic progression: a case study model.

Krishnan, Reshmy; Nair, Sarachandran; Saamuel, Baby Sam; Justin, Sheeba; Iwendi, Celestine; Biamba, Cresantus; Ibeke, Ebuka

Abstract

In the current COVID-19 pandemic era, Learning Management Systems (LMS) are commonly used in e-learning for various learning activities in Higher Education. Learning Analytics (LA) is an emerging area of LMS, which plays a vital role in tracking and storing learners’ activities in the online environment in Higher Education. LA treats the collections of students’ digital footprints and evaluates this data to improve teaching and learning quality. LA measures the analysis and reports learners’ data and their activities to predict decisions on every tier of the education system. This promising area, which both teachers and students can use during this pandemic outbreak, converges LA, Artificial Intelligence, and Human-Centered Design in data visualization techniques, semantic and educational data mining techniques, feature data extraction, etc. Different learning activities of learners for each course are analysed with the help of LA plug-ins. The progression of learners can be monitored and predicted with the help of this intelligent analysis, which aids in improving the academic progress of each learner in a secured manner. The Object-Oriented Programming course and Data Communication Network are used to implement our case studies and to collect the analysis reports. Two plug-ins, local and log store plug-ins, are added to the sample course, and reports are observed. This research collected and monitored the data of the activities each students are involved in. This analysis provides the distribution of access to contents from which the number of active students and students’ activities can be inferred. This analysis provides insight into how many assignment submissions and quiz submissions were on time. The hits distribution is also provided in the analytical chart. Our findings show that teaching methods can be improved based on these inferences as it reflects the students’ learning preferences, especially during this COVID-19 era. Furthermore, each student’s academic progression can be marked and planned in the department.

Citation

KRISHNAN, R., NAIR, S., SAAMUEL, B.S., JUSTIN, S., IWENDI, C., BIAMBA, C. and IBEKE, E. 2022. Smart analysis of learners performance using learning analytics for improving academic progression: a case study model. Sustainability [online], 14(6), article 3378. Available from: https://doi.org/10.3390/su14063378

• Journal Article Type: Article
• Acceptance Date: Jan 31, 2022
• Online Publication Date: Mar 14, 2022
• Publication Date: Mar 31, 2022
• Deposit Date: Feb 11, 2022
• Publicly Available Date: Feb 11, 2022
• Journal: Sustainability
• Electronic ISSN: 2071-1050
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 14
• Issue: 6
• Article Number: 3378
• DOI: https://doi.org/10.3390/su14063378
• Keywords: Learning management system; Learning analytics; Artificial intelligence; Data visualization techniques; LA plug-ins; Teaching; Learning
• Public URL: https://rgu-repository.worktribe.com/output/1584502