Evaluating the Effectiveness of Interactive Video Learning by Examining Machine Learning Classifiers Models: Graduate Students’ Perspectives

Abstract

Important elements had an impact on how traditional learning was implemented and motivated researchers to develop Interactive Video Learning Effectiveness (IVL-E). These variables range from price to learning-environment to learner perspective, among others. This paper’s major objectives are to: (i) assess the effectiveness of Interactive Video Learning (IVL-E) using classification techniques and considering graduate students’ viewpoints, (ii) establish appropriate classification parameters to choose the optimum classifier model, and (iii) review prior works pertaining to IVL-E assessment. The study dataset is a sample of 63 datapoints randomly chosen by a survey performed at the College of Education, University of Bisha, Bisha, Saudi Arabia. A total of 123 registered postgraduate students made up the study population when using Google’s online questionnaire method, after all the respondents voluntarily agreed to fill out and submit the questionnaire. This study develops a reliable machine learning classifier’s model for classifying IVL-E. The created models use a backpropagation algorithm and are a type of multilayer classification perceptron. The best classification output was “interactive video learning performance measure”, which provided the highest results under: 1) support vector machine-based classifier (SVC), 2) decision tree (DT), and 3) light gradient-boosting machine classifier (lgb.LGBMClassifier). Regarding classification measures like balanced accuracy (high BCCR = 0.875), balanced error rate (low BER = 0.125), and optimization precision (highest OP = 0.999), our models performed extremely well according to the literature review.