Tracking Student Performance Tool for Predicting Students EBPP in Online Courses
DOI:
https://doi.org/10.3991/ijet.v16i23.25503Keywords:
EBP Predictive Model, SQU-SLMS framework, Student Engagement, Student Behavior, Student Personality, Student Performance, Moodle Log.Abstract
Our motivation in this paper is to predict student Engagement (E), Behavior (B), Personality (P) and Performance (P) via designing a Tracking Student Perfor-mance Tool (TSPT) that obtained data directly from Moodle logs of any selected courses. The proposed tool follows the predictive EBP model that focuses mainly on student's EBP and Performance where the instructor could use it to monitor the overall performance of his/her students during the course. The results of test-ing the tool show that the developed tool gives the same as manual results analy-sis. Analyzing Moodle log of any course using such a tool is supposed to help with the implementation of similar courses and helpful for the instructor in re-designing it in a way that is more beneficial to the students. This paper sheds light on the importance of studying student's EBPP and provides interesting possibili-ties for improving student performance with a specific focus on designing online learning environments or contexts.
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Copyright (c) 2021 Iman Rashid Al-Kindi, Zuhoor Al-Khanjari
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