Learning Analytics: Analyzing Various Aspects of Learners’ Performance in Blended Courses. The Case of Kabul Polytechnic University, Afghanistan

Hamidullah Sokout, Tsuyoshi Usagawa, Sohail Mukhtar

Abstract


Learning performance is crucial in students’ academic lives because it opens opportunities for future professional development. However, conventional educational practices do not provide all the necessary skills for university instructors and students to succeed in today's educational context. In addition, due to poor information resources, ineffective ICT tool utilization and the teaching methodologies in developing countries, particularly Afghanistan, a large gap exists across curriculum plans and instructor practices. Learning analytics, as a new educational instrument, has made it possible for higher education actors to reshape the educational environment to be more effective and consistent. In this study, we analyzed multiple research approaches and the results of analytics of various learner aspects to address the aforementioned issues. The research methods were predominantly quantitative-cum-qualitative. Real (quantitative) data were collected based on learners’ explicit actions, such as completing assignments and taking exams, and implicit actions, such interacting and posting on discussion forums. Meanwhile, secondary (qualitative) data collection was conducted on-site at Kabul Polytechnic University (KPU); both blended and traditional class samples were included. The results of this study offer insight into various aspects of learners’ behaviors that lead to their success and indicate the best analytical model/s to provide the highest prediction accuracy. Furthermore, the results of this study could help educational organizations adopt learning analytics to conduct early assessments to evaluate the quality of teaching and learning and improve learners’ performance.

Keywords


Learning Analytics, Blended Learning, Higher Education, Teaching, Learning, Quantitative-cum-Qualitative, KPU

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Copyright (c) 2020 Hamidullah Sokout, Sohail Mokhtar, Tsuyoshi Usagawa


International Journal of Emerging Technologies in Learning (iJET) – eISSN: 1863-0383
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