Relation between Student Engagement and Demographic Characteristics in Distance Learning Using Association Rules

Abstract

Distance learning has made learning possible for those who cannot attend traditional courses, especially in pandemic periods. This type of learning, however, faces a challenge in keeping students engaged and interested. Furthermore, it is important to identify students who are in need of help to ensure that their progress does not deteriorate. First, the research identifies students’ engagement based on their behaviors in Virtual Learning Environment (VLE) and their performances in assessments. This research goal is to investigate the association/relationship between demographic characteristics and engagement level. It identifies less engaged students by using an unsupervised clustering model based on VLE interactions and assessments of submission-derived features. According to results, the two-level clustering model outperforms other models in regard to cluster separation using silhouette coefficient. Apriori algorithm is utilized to obtain a set of rules that connect demographic features to student engagement. Results show gender, highest education, studied credits, and number of previous attempts have positive correlation with engagement level in distance-based learning.

Demographic Effect on Engaged Learning Practice of Undergraduate Students

“…It was found that student engagement differs by gender (Sontam & Gabriel, 2012). The study of Jawthari and Stoffa (2022) also established the gender difference in student engagement in learning. It was determined whether the amount of time and energy invested in educational practices differed between students from different racial and ethnic groups (Greene et, al.…”

A Model for Effective Mathematics Teaching With an Emphasis on the Development of Transversal Competencies in Technical and Economic Study Programs

“…By using different types of software, student flexibility was also trained. In addition, using the software can increase students' interest in studying and motivation, which is no less important [44], [45]. A detailed description of the model is given in the study [41].…”

A Learning Analytic Approach to Modelling Student-Staff Interaction From Students’ Perception of Engagement Practices

“…The formal definition of an association rule, as stated in [49], is as follows: Let A = {s1, s2, ..., sx} be a set of d attributes referred to as items, and B = {t1, t2, ..., ty} be a set of y transactions known as the database. Each transaction ts in B consists of a subset of the items in A.…”

“…To discover the interesting associative patterns, FP-Growth association rules mining algorithm was implemented using python programming language. In order to transform the dataset into a binomial structure that is compactible for the adopted FP-Growth Association rule mining algorithm each EI score was transformed into one of the following categories: poor (0-29), good (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44) or excellent (45)(46)(47)(48)(49)(50)(51)(52)(53)(54)(55)(56)(57)(58)(59)(60) depending on the EI score on the rescaled 60-point scale. This representation is consistent with the student academic performance grading system of both universities, with fails (0-49), passes and distinction (75-100).…”