Exploring The Learning Analytics Of Skill-Based Course Using Machine Learning Classification Models

Abstract

The extensive usage of simulation software to meet the demand for online learning has become the subject of learning analytics as the core of performance assessment in different program courses. This study shall determine and discover the learning analytics of students in the computer-based System Drafting and Design course using LabVIEW simulation software. Different machine learning techniques are applied and tested to evaluate the performance level of students in the computer-based System Drafting and Design course. The dataset for analysis is the students' performance and topic impressions. Machine learning algorithms are applied to determine the correlation of the datasets to determine the learning analytics. The resulting correlation coefficient presents a promising result that supports defining the learning outcomes the student acquired. Techniques that consistently respond to outcomes suggest that the student experience and teachers' assessment correlate with learning. This technique proved that using LabVIEW as a simulation software can promote the minimum required skills in an electronics design course and not only the theoretical foundation. Thus, the result can be a basis for future instruction quality improvement plans of laboratory-oriented courses.