Student Performance Prediction using OLAMLTs: A Machine Learning Approach

Abstract

LMSs (Learning management systems) are widely used in educational institutions. They are software systems for cloud-based training that are offered locally, remotely, and on demand. As technological costs in higher education decrease, a significant obstacle to online learning is the high cost of creating its content. LMSs are very helpful to education during pandemics period. which significantly impacted worldwide education. In these situations, using LMSs in education offers clever substitutes for traditional classroom instruction and enables teachers to give specialized information, make use of different pedagogical approaches, and better engage their students in their studies. Globally, the current epidemic has caused unanticipated and quick transitions to remote learning and instruction, changes in both content and character. Academic performance largely depends on students' capacity to adapt and respond to disturbances. By focusing on how adaptability contributes to students' educational development and online learning, this study aims to identify factors affecting the adaptability of students in online learning scenarios. For these examinations, this paper suggests using MLTs (Machine Learning Techniques). The OLAMLTs (Online Learner Adaptability Assessment based on MLTs) suggested method evaluates aspects that influence online learners' adaptabilities to education while making recommendations for enhancements, and the recommendations achieve the highest classification accuracy when compared with other methods in evaluations.