| dc.description.abstract | In universities all around Kenya, e-learning has grown in popularity, especially for postgraduate
programs. Students now find it simpler to access education from any location at any time thanks
to the use of technology in the delivery of courses and academic resources. However, dropout rates
continue to be a serious issue despite the many advantages of online learning. For a variety of
reasons, including a lack of desire, insufficient assistance, and trouble understanding the course
materials, students withdraw from online courses. Dropouts drive up educational institutions'
average cost per student because it typically costs more to retain a possible dropout than to enroll
a new student. The rise of online learning is hampered by the prevalence of school dropouts, which
waste the student's initial time and financial investment. Low graduation rates that follow high
dropout rates will surely damage the standing of educational institutions in the community and
eventually result in a downward loop of declining government support. To lower the dropout rate,
online educational institutions can employ this technology to quickly spot probable dropouts and
put retention measures in place before the dropout behavior takes place. The study's objective was
to create a hybrid machine learning prediction model for postgraduate E-learning students who
drop out utilizing the Support Vector Machine and Random Forest algorithms to improve
prediction accuracy. The researcher employed a descriptive survey and an experimental study
approach. The research methodology will be appropriate because the researcher trained the
Dropout Prediction Detection model using a machine learning technique. In 2024, 61.7% of
students are expected to graduate. With the aid of the data, the researcher was better able to
determine whether students had spent more time studying than was anticipated. 62.5% of the
respondent's price posed the most challenge to finishing the investigation, however 37.5% of the
fee posed no issue. In order to prepare students for postgraduate study, 68.3% strongly agreed that
undergraduates should be taught research techniques, and 29.2% also agreed. A 100% accuracy
rate for forecasting student dropout was demonstrated by the hybrid model. By using machine
learning to predict student attrition, educational institutions have a ground-breaking chance to
effectively address this pervasive problem. The study also recommended that the students choose
a study strategy that would best fit their schedules in order to prevent unneeded stress from juggling
numerous tasks at once. Deep learning models can be strengthened by techniques like Synthetic
Minority Over-sampling Technique to handle the unbalanced datasets typical in dropout prediction
problems | en_US |
