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
Index Terms—Interactive learning assessment, video learning classification, Saudi interactive learning
Omar Abdullah Omar Alshehri and Amer Mutrik Sayaf are with Educational Technology Department, College of Education, University of Bisha, Bisha 61922, Saudi Arabia.
Elrasheed Ismail Mohommoud Zayid is with Information Systems Department, College of Sciences and Arts-Alnamas, University of Bisha, Alnamas 61977, Saudi Arabia.
*Correspondence: email@example.com (E.I.M.Z.)
Cite: Omar Abdullah Omar Alshehri, Elrasheed Ismail Mohommoud Zayid*, and Amer Mutrik Sayaf, "Evaluating the Effectiveness of Interactive Video Learning by Examining Machine Learning Classifiers Models: Graduate Students’ Perspectives," International Journal of Information and Education Technology vol. 13, no. 10, pp. 1625-1637, 2023.Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).