Grade Prediction Modeling in Hybrid Learning Environments for Sustainable Engineering Education

Abstract

Since mid-March 2020, due to the COVID-19 pandemic, higher education has been facing a very uncertain situation, despite the hasty implementation of information and communication technologies for distance and online learning. Hybrid learning, i.e., the mixing of distance and face-to-face learning, seems to be the rule in most universities today. In order to build a post-COVID-19 university education, i.e., one that is increasingly digital and sustainable, it is essential to learn from these years of health crisis. In this context, this paper aims to identify and quantify the main factors affecting mechanical engineering student performance in order to build a generalized linear autoregressive (GLAR) model. This model, which is distinguished by its simplicity and ease of implementation, is responsible for predicting student grades in online learning situations in hybrid environments. The thirty or so variables identified by a previously tested model in 2020–2021, in which distance learning was the exclusive mode of learning, were evaluated in blended learning spaces. Given the low predictive power of the original model, about ten new factors, specific to blended learning, were then identified and tested. The refined version of the GLAR model predicts student grades to within ±1 with a success rate of 63.70%, making it 28.08% more accurate than the model originally created in 2020–2021. Special attention was also given to students whose grade predictions were underestimated and who failed. The methodology presented is applicable to all aspects of the academic process, including students, instructors, and decisionmakers.

Systemic Analysis of the Use of Technological Tools in the University Learning Process

“…The authors of [13] and [14] examined students' attitudes toward web-based peer review and identified key factors related to students' use and appreciation of feedback. In addition, they present an analysis of the type of feedback for the purpose of assessing the written communication skills of students using online learning tools.…”

Visualizing Groundwater Dispersion: Laboratory Exercise on Dispersivity with Hands-on and Online Students

“…Future work should focus on improving the dispersivity lab for online students, perhaps by using interactive videos as proposed by [12] and [13]. Enhancing this lab for online students is important for sustainable hybrid learning [26], i.e., the mixing of distance and face-to-face students.…”

Assessment of Engineering Entrepreneurship Intentions Among Engineering Students of Osun State University, Nigeria

“…Nonparametric correlation tests are distribution-free hypothesis tests. They are used when samples are drawn from an unknown or nonnormal distribution [50]. Some of the most commonly used nonparametric tests include the chi-square test, Spearman's rank correlation test, and Wilcoxon's one-sample signed ranks test.…”

“…In this study, Spearman's rank correlation test was used. Spearman's correlation coefficient measures the strength and direction of the association between two ranked variables when the data are measured on ordinal, interval, or ratio scales [50]. The results of the nonparametric test for this study are presented in Table 2.…”