PREDICTING STUDENT PERFORMANCE IN ONLINE LEARNING PLATFORMS: ANALYZING ENGAGEMENT METRICS WITH MACHINE LEARNING MODELS

ijar journal; Aggarwal, Riyan

doi:10.21474/ijar01/19648

The emergence of online learning platforms harbours both opportunities and challenges, this paper focuses on machine learning algorithms for deep diving into key predictors of students' success in online environments, captured by engagement metrics such as quiz scores, participation in forums, and time spent on learning tasks. Data was collected through a survey of 50 students and then run through the Random Forest model. Major predictors of success include the assignment approach, quiz score, and motivation. The prediction from this model can be interesting because it can pick important features, but the overall accuracy is quite low at about 20%, which implies that larger datasets and more features might be necessary to enhance the predictions. Time management, motivation, and staying in touch are such variables that the research holds key to determining results for students. The results are important not only for online learning platforms but also for interventions suggested, which include early warning systems, personalised learning paths, and elements of gamification to support performance for improved student support at the right time for those most at risk. Future studies should consider more sophisticated machine learning models and the use of more objective performance data in their attempt to make more refined predictions.

PREDICTING STUDENT PERFORMANCE IN ONLINE LEARNING PLATFORMS: ANALYZING ENGAGEMENT METRICS WITH MACHINE LEARNING MODELS

Abstract

Statistics