Statistical Analysis of Correlation Between Students’ Personal Characteristics and Academic Success in Engineering Mechanics Course
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BACKGROUND Understanding the factors which lead to student success or failure has long been an important matter for educators. Researchers like Zimmer et al. (1996) have focused on a particular science course to find the factors which lead to success, whereas others like Tynj䬤 et al. (2005) have examined an entire engineering program or degree to investigate the reasons behind students' performance. Although a number of factors have been identified by different scholars such as Cahan et al. (1989), there are still many aspects which have not yet been explored/examined. PURPOSE This research has aimed to focus on a particular engineering course to enable a better investigation tailored to engineering students. In this regard, students of two Engineering Mechanics classes (the 2012 and 2013 academic years) have been chosen at Griffith University and their personal characteristics have been explored to determine key factors leading to a satisfactory final mark in the mentioned course. The results would allow course convenors to more quickly identify vulnerable students. DESIGN/METHOD The parameters which have already been investigated by researchers are very broad. However, based on the available resources for this study and also considering the most important and effective parameters (inferred from Cahan et al. (1989) and Hoskins et al. (1997)), the following factors have been selected for detailed analysis: gender, age, first language, study program, prior grade point average (GPA) and overall positions (OP). Simple statistical analyses have been conducted for each of these parameters in light of the students' final mark. In addition, the correlation between scalar parameters (such as age) and final mark has also been observed. RESULTS Simple descriptive analysis has shown that there are no major differences between the 2012 and 2013 cohorts. The maximum, minimum and average marks for these classes were quite close. In particular, younger students achieved both the highest and lowest marks. Age did not affect the performance of mature students who were more evenly distributed in the middle range of results. Likewise, those from non-English speaking backgrounds were reasonably competitive with the others. More interestingly, no major difference was found between genders, although Hoskins et al. (1997) and Diaz (2003) both argued that there are differences in performance based on gender. Finally, the prior GPA and OP have shown a significant contribution to a better final mark. CONCLUSIONS The factors studied in this research have highlighted the important parameters for students' success. These should be noticed in the earliest stages of the semester to identify at-risk students to help them avoid becoming student-in-need later in the semester.
Proceedings of the 25th Annual Conference of the Australasian Association for Engineering Education
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