MOOC Dropout Prediction using FIAR-ANN Model based on Learner Behavioral Features

Abstract

Massive Open Online Courses (MOOCs) are a transformative technology in digital learning that incorporates new techniques through video sessions, exams, activities, and conversations. Everyone leads a successful life in their professional and personal skills learning courses during COVID-19. The research concentrated on employing video interaction analysis to characterize crucial MOOC tasks, including predicting dropouts and student achievement. Our work consists of merely generating and picking the best characteristics based on the learner behavior for evaluating the dropout measure. To locate the frequent objects for feature creation, an association rule-FP growth approach is applied. The neural network is implemented using frequent itemset-3, which is used for feature selection. The evaluation metrics are calculated by using the Multilayer Perceptron (MLP) method. The metric values were then compared to the proposed model and some base supervised machine learning models namely Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), K-Nearest Neighbor (KNN), and Naive Bayes (NB). The FIAR (Feature Importance Association Rule)-ANN(Artificial Neural Network) dropout prediction model was tested on the KDD Cup 2015 dataset and it had a high accuracy of over 92.42, which is approximately 18% better than the MLP-NN model. With the optimized parameters, we are solely focused on lowering dropout rates and increasing learner retention.