The Interpretable Machine Learning among Students and Lectures during the COVID-19 Pandemic

Elihami Elihami (Universitas Muhammadiyah Enrekang, Indonesia)
Van David (Hunan University, Japan)
Mohd. Melbourne (Philiphines University,Philipphines)

JURNAL IQRA´
Vol 6 No 1 (2021): Jurnal Iqra' : Kajian Ilmu Pendidikan

This study explored the interpretable E-learning teachers of online learning in a program at Universitas Muhammadiyah Enrekang. The Data were collected through surveys and semi-structured interviews with 150 lectures at Universitas Muhammadiyah Enrekang. Data analysis used thematic analysis of qualitative data. The analysis results found four main themes, namely, instructional strategies, challenges, support, and motivation of lectures. E-learning used as solutions to refer specifically to full online learning between conventional campus education and online learning (e-learning) outside the campus facing covid-19. The virtual Learning management system (LMS) provided an opportunity to promote online learning (e-learning). This research contributes to the literature of online collaborative learning between teachers, parents, and schools that impact student success. Keywords: E-Learning, Machine Learning, Learning Management System