Abstract

Several studies have been conducted to understand the predictors of academic performance of various levels of high school and undergraduate students as quantified by the grade point average. This study focuses specifically on engineering students as they differ from other undergraduate students in their background and expectations. We focus on quantifying essential predictors of the performance of engineering students in an advanced mathematics course. We collected data from 72 participants recruited from engineering students enrolled in the advanced engineering mathematics (AEM) course in a research university. We chose this course to represent a standard engineering mathematics course covering several essential topics. We consider several factors in our analysis, such as cellphone usage and the academic background, e.g., the academic year, number of minors, and majors, performance in prerequisite courses. We perform several regression analyses to understand the effects of cellphone usage, course schedule, and academic background on performance in the AEM course and its prerequisites. In particular, we use the stepwise regression technique using forward selection and backward elimination procedures. We discovered a few interesting findings in this case study. Firstly, for the participants in this study, we find that the daily average “screen time” on their cellphones is not a statistically significant predictor of student performance. This finding contradicts some prior studies on this participant and may indicate adaption and integration of the technology by the new generation of students in recent years. We also found that the lecturing schedule was not an influential factor for their academic performance. These findings are especially relevant during the COVID-19 pandemic, as they suggest that advanced engineering students have adapted to the use of technology and are flexible concerning lecture schedules. Another unexpected finding is that this study brings new evidence that the number of minors taken by the participants is a negative predictor of their grade in the AEM course. This observation may indicate that course-work from non-major classes may adversely impact their performance in mathematical engineering courses.

Highlights

  • This paper aims to find and quantify the factors affecting academic success in an undergraduate-level course in advanced engineering mathematics (AEM)

  • This model accounts for 66% (R2 0.66, R2adj 0.59, F(12, 59) 9.34, p < 0.001) of the variability in grades obtained in the course

  • The variables Calc3, NMnr, Calc2, Differential Equations (Diffeq), and iphone are retained in the stepwise regression model

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Summary

Introduction

This paper aims to find and quantify the factors affecting academic success in an undergraduate-level course in advanced engineering mathematics (AEM). Our focus in this work is on factors available to the instructors to improve the quality of the course. In this cross-sectional case study (Yin, 1994), we enlist 72 students enrolled in the “Advanced Engineering Course” in a private university in the United States. In recent years mobile technology has become universally accessible, with several researchers studying the impact of cellphones on academic achievements. Since this topic is vast, the above list is by no means an exhaustive one. A meta-analysis of 200 studies (White, 1982) showed that socioeconomic status and academic achievements are positively correlated. A replica of the study (Sirin, 2005) across 128 school districts showed a medium to strong SES–achievement relation

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