A Robust Risk Model to Identify Factors that Affect Students’ Critical Achievement in Remote Lab Courses

Abstract

Purpose:The research objective is to address the problem of students' critical achievement in remote lab courses aiming at identifying the risk factors of students' failure by exploiting e-learning learning analytics. A robust risk model is developed to serve this purpose. The research is orented into NI-Elvis remote lab courses given that they offer a stable environment in the context of which automation lab courses could be effectively delivered. Design/methodology/approach: A robust risk model was developed by analyzing students' behavioral engagement data. The e-learning part of the remote lab course was used to provide the requisite dataset. In detail, a proper binary logistics regression scheme was employed to come up with the aforementioned risk model. The e-learning part was implemented by a specific e-learning platform that is suitable for NI-Elvis remote lab courses. The data was collected after the course completion. Findings: Factors that are related to students' engagement (number of theory exercises completed and number of messages sent) appeared to be decisive. Originality/value: The originality of our research lies in the fact that the issue of students' critical achievement in remote lab courses is not addressed in a fragmentary way by just carrying out a specific analysis and coming up with results, like many similar studies in the literature. Thereby, a concrete methodology was developed on the basis of an established generic risk management framework. The added value of our research is centered on the fact that our risk model could potentially be applied to any remote lab course to come up with the respective risk factors.

A Prediction Model for Remote Lab Courses Designed upon the Principles of Education for Sustainable Development

“…In the case of NI-ELVIS remote labs, one study has proved that the student's interaction with the NI-ELVIS LMS strongly predicts students' critical performance. The same study has pointed out that the activities' completion concerning the theoretical material and the theoretical exercises significantly affect students' outcomes [31]. As the studies mentioned above underline, no specific set of factors affects students' critical performance in any course.…”

“…Many studies indicate e-learning platforms' vital role in predicting at-risk students [25][26][27][28][29][30][31]. Previous studies have also indicated that a proper analysis of students' behavioral engagement could lead to developing competent risk models and generating respective prediction models.…”

“…Statistical and machine learning techniques have been employed to answer the individual purpose of these studies. For instance, some studies refer to risk models developed by employing binary logistic regression analysis [25][26][27][28]31], whereas a specific study refers to a risk model developed by employing other classification techniques. In parallel, one specific study underlines the contribution of the discriminant function analysis to the prediction model generation [26].…”

“…In this sense, the data reflected the students' interaction with the learning activities designed for the specific course. It is also essential to point out that a similar data set was used in some studies to reflect students' behavioral engagement [25][26][27][28][29][30][31].…”

“…Appropriate variables were defined, reflecting the data set. These variables and a binary variable termed rlstrisk (0: for students not at risk and 1: for students at risk) were employed in a binary logistic regression analysis to develop the risk model and the individual risk factors [25][26][27][28][29][30][31].…”