The Discriminant Analysis Approach for Evaluating Effectiveness of Learning in an Instructor-Led Virtual Classroom

Abstract

The effective learning requires putting down various associations of new ideas to old ones to integrate some innovative thoughts. The learners must change the associations among the things they already know, or even reject some long-held attitude about the world. The choice to the essential reformation is to deform the new information to fit their old ideas or to reject the new information entirely. Learners come to the classroom with their own ideas, some may be correct and some may not be, concerning roughly each topic they are expected to come across. If their perception and misunderstanding are unnoticed or discharged out of control, it affects the learning of a learner. The learners must be encouraged to build up new observation by seeing how such observation helps them make better sense of the world. The objective of this research paper is to put down the fundamentals of learning that promotes effective learning in an instructor-led virtual classroom and to analyze the learners' learning performance using the Discriminant Analysis, a data mining technique. The Discriminant Analysis uses statistically significant determinants to predict learners' learning in a classroom.

A Literature Review on Intelligent Services Applied to Distance Learning

“…The analyzed works propose systems that identify the characteristics and profiles of students and predict their behavior (Zaoudi and Belhadaoui [50]; Thai-Nghe et al [47]), cognitive skills (Angeline et al [53]), academic success (Kim and Kim [42]; Hashim et al [54]), performance (Iatrellis et al [70]), learning styles (El Fouki et al [34]), and potential dropout or failure (Manhães et al [45]; Chen et al [67];Freitas et al [56]).…”

“…Zhang et al [52] SCOPUS Learning analysis using Moodle plugins to discover possibilities to improve the learning process and reduce the number of under performing students. Angeline et al [53] SCOPUS Discriminant analysis to measure student performance. Hashim et al [54] SCOPUS Student performance prediction model based on supervised machine learning algorithms (decision tree, Naïve Bayes, logistic regression, support vector machine, K-nearest neighbor, and minimal and neural sequential optimization Network).…”

“…Angeline et al [53] used discriminant analysis to measure student performance. The data-mining technique identified students' cognitive skills and their associated behaviors in a virtual instructor-led classroom.…”

A Literature Review on Intelligent Services Applied to Distance Learning

“…Iatrellis et al [65] Two-stage machine learning approach to predict student outcomes. Angeline et al [48] Discriminant analysis to measure student performance.…”

“…Angeline et al [48] used discriminant analysis to measure student performance. The data mining technique identified students' cognitive skills and their associated behaviors in a virtual instructor-led classroom.…”