Early Prediction of At-Risk Students in Higher Education Institutions Using Adaptive Dwarf Mongoose Optimization Enabled Deep Learning

The biggest problem with online learning nowadays is that students aren’t motivated to finish their coursework and other assignments. As a result, their performance suffers, which raises the dropout rate, necessitating the need for proactive measures to manage the dropout. Predictions of student performance assist in selecting the best programmers and designing efficient study schedules that are suited to their needs. Additionally, it aids in the development of observation and support tactics for students who require assistance in order to finish the course work by teachers and educational institutions. This paper proposed an efficient method using Adaptive Dwarf Mongoose Optimization (ADMOA)-based Deep Neuro Fuzzy Network (DNFN) for prediction of at-risk students in higher education institutions. Here, DNFN is working to forecast at-risk kids and prediction is carried out based on the most pertinent features collected utilizing the created ADMOA algorithm. Additionally, the effectiveness of the proposed ADMOA_DNFN is examined in light of a number of characteristics, including Root MSE (RMSE), Mean Square Error (MSE), Mean Absolute Error (MAE) and Mean Absoulte Percentage Error (MAPE), it attains best values of 0.049, 0.045, 0.212, and 0.022 respectively.