Comparison of Students' Learning Style in Engineering Mechanics and Fluid Mechanics courses

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Shaeri, Saeed
Etemad Shahidi, Amir
Guan, Hong
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Aman Oo, Arun Patel, Tim Hilditch, Siva Chandran

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Geelong, Australia


BACKGROUND OR CONTEXT Despite the degree of progress reached in regard to different learning theories, as well as students’ approaches to learning, some aspects of students’ attitudes are still unknown. In fact, the total mark is the only indicator of the level of success or failure in the majority of current tertiary institutions. Hence, access to the details of individual student’s marks during the course of a semester, can be useful in providing a guideline for the educator or the student advisor to target particular, at-risk students. Such intermediate summative marks include assignment tasks, laboratory reports and mid-semester exam marks, which although may not follow a constant and similar trend, could assist the educator in recognising the atrisk students. Apart from the information obtained from a current course, some relationships can possibly be found between the current and the previous/prerequisite courses that the students have completed. Since the teacher or the student advisor usually does not have prior awareness of an individual student performance, detailed assessment marks, can likewise assist them in better identifying attitudes and learning styles of at-risk students. PURPOSE OR GOAL This study aims to propose a straightforward framework by which, identifying at-risk students at early stages of a semester can be achieved. Knowing about such students helps the teacher and/or the student advisor to more purposefully invest their time and energy in those who need additional support and assistance compared to the other average or above average students. Some students struggle with regular assignment submissions and some others have difficulty working in groups, either on an assignment or a laboratory activity. There would also be students who have some sort of issues with exams (such as exam anxiety and the like). Although there might be a countless number of cases, it is believed that, more or less, at-risk cases can be identified through some similar factors related to their elements of total marks. One of the factors considered in this study, is any kind of similarity between two courses or any type of pre-requisite/post-requisite relationship between them. APPROACH In order to establish the aforementioned framework, six cohorts of students at the Griffith School of Engineering, Griffith University were selected. A total of about 1470 students were investigated, of which approximately 90% were enrolled in a Bachelor of Engineering program, 7% were enrolled in double degree programs which included a Bachelor of Engineering, and the remaining 3% were enrolled in a variety of programs which had no connection with engineering. The students attended Engineering Mechanics (EM) course in the second semesters of 2012, 2013 and 2014 with 263, 310 and 258 students, respectively. The majority of these students then attended Fluid Mechanics and Hydraulic (FM) course in the first semesters of 2013, 2014 and 2015 with 157, 237 and 243 students, respectively. The available data include summative marks for different assignments, laboratory reports, mid-semester exams and final exams which all are used with no reference to an individual student to guarantee the anonymity of records. For the simplicity of performing the statistical analyses, as well as comparability of the mentioned courses (EM and FM), all the marks for assignments and laboratories were collated and named ‘assignment mark’. Consequently, the statistical relationships between the marks for the assignments, mid-semester exams and final exams for each of the individual semesters were statistically tested to find possible relationship or dependency. Moreover, similar marks (for instance final exam marks) for each course, taken by individuals, were statistically tested to determine possible correlations. DISCUSSION Comparing the pass rates of the two courses for the selected years, the FM showed on average a higher pass rate. This could be attributed to the fact that students of the FM course are more mature in their study journey. Accordingly, the percentage of students who achieved credit or distinction marks were almost the same in different years. The majority of students obtained final marks which were ±10% different in these courses. This means that the majority performed almost the same across the years in these courses. However, there were moderate correlations (about 0.5 in 2013 and 0.65 in 2014, with one representing a strong relationship) between the marks for the courses for an individual student. This means knowing the average of the cohort could hardly be a good indicator of the individual performance. Such relationships were weaker for elements of marks (i.e. assignments, midsemester exam and final exam) compared to the final mark of individual students. Nevertheless, in all six classes, the correlation coefficients between the final mark and final exam mark was 0.93 compared to 0.78 and 0.67 for the mid-semester and assignment marks. Hence, it seems that the students achieve the majority of their final mark through their final exam (although in all the years and courses the weight of the final exam was about 50- 55%). This highlights that in order to promote a better and deeper learning approach, more emphases should be focused on improvement of the intermediate marks than the final exam mark. RECOMMENDATIONS/IMPLICATIONS/CONCLUSION In order to better identify at-risk students and provide a suitable assistance for them towards successful completion of an individual course, it is argued here that knowledge gained through prior/pre-requisite courses can provide a significant source of information. Such information is not limited to the student final mark. In fact, the elements of the total marks; i.e. assignments, laboratory report, mid-semester and final exam marks, enhanced the degree of certainty in recognising the attitude and learning style of at-risk students. The relationships and correlations between each element of the marks for two courses, i.e., Engineering Mechanics and Fluid Mechanics in the studied years were tested in this study for about 1470 students. Although the results show a moderate correlation between the two studied courses, a strong relationship was found within each course. This means that regardless of superiority or inferiority of the students’ performance for each course, the performance in the first course can moderately predict the performance in the proceeding course. Therefore, attributing students’ approach such as underperformance in group works or outperformance in frequent assignment tasks compared to ad-hoc exam can assist in designing the assessment task or providing special considerations for an individual student.

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Proceedings of the 26th Annual Conference of the Australasian Association for Engineering Education (AAEE 2015)

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