ML Techniques Integration in Digital Learning Platforms: Students’ Dataset Statistical Analysis

Abstract

With the use of technology-enhanced learning platforms and an abundance of available educational data, it is possible to analyze student learning behavior and solve problems, improve the learning environment, and make data-driven decisions. A virtual learning environment effectively provide datasets for analyzing and reporting student learning, as well as its reflection and participation in their individual performances, which complements the learning analytics paradigm. This work is intended to explain the use of AI-based approaches in online learning, with a particular focus in offering a statistical approach on students VLE dataset. The study uses quantitative methodology to highlight the association between the variables in the obtained dataset. The purpose of this research is to examine the correlation and dependency of the dataset variables in order to observe the relationship between these variables and the effect that these attributes may have on students' performance in a digital learning environment. According to the findings of this study, there is a correlation between student performance and a number of different factors, such as resource (page) views, course modules, assessment type, assessment weight and sum of clicks in a VLE.