Multilayer Perceptron Neural Network Approach to Classifying Learning Modalities Under the New Normal

Abstract

Because of community quarantines and lockdowns during COVID–19 times, the Philippine’s Department of Education (DepEd) implemented blended learning (BL) both online and offline distance learning modalities (LM) among basic educational institutions in the hope of continuing learners’ learning experiences amidst the pandemic. Learners’ LM are classified through the use of an Algorithm for Learning Delivery Modality as recommended by DepEd. Based on initial investigation, mismatches in learners’ LM were, however, observed, resulting in learners’ massive shifting from one LM to another in the middle of the school year. In this study, we introduced an approach to classifying learner’s LM using machine learning (ML) techniques. We compared the effectiveness of five ML classifiers, namely the random forest (RF), multilayer perceptron neural network (MLP NN), K-nearest neighbor (KNN), support vector machine (SVM), and Naïve Bayes (NB). Learner’s enrolment and survey form (LESF) data from the repository of a local private high school in the Philippines is used in model formulation. We also compared three existing feature selection (FS) algorithms (recursive feature elimination (RFE), Boruta algorithm (BA), and ReliefF)–integrated into the five ML classifiers as data feature reduction techniques. Results show that the combination of MLP NN and BA yielded a considerably high performance among the rest of the formulated models. Sensitivity analysis revealed that asynchronous LM is most sensitive to “existing health condition” feature, modified asynchronously, is highly characterized by low educational attainment and unstable employment status of parents or guardians, while synchronous learners have high socio–economic status as compared to other LM.