June 15, 2019
June 15, 2019
October 19, 2019
Educational Research and Methods
Engineering students develop competencies in fundamental engineering courses (FECs) that are critical for success later in advanced courses and engineering practice. Literature on the student learning experience, however, associate these courses with challenging educational environments (e.g., large class sizes) and low student success rates. Challenging educational environments are particularly prevalent in large, research-intensive institutions. To address concerns associated with FECs, it is important to understand prevailing educational environments in these courses and identify critical points where improvement and change is needed.
The Academic Plan Model provides a systematic way to critically examine the factors that shape the educational environment. It includes paths for evaluation and adjustment, allowing educational environments to continuously improve. The Model may be applied to various levels in an institution (e.g., course, program, college), implying that a student’s entire undergraduate learning experience is the result of several enacted academic plans that are interacting with each other. Thus, understanding context-specific factors in a specific educational environment will yield valuable information affecting the undergraduate experience, including concerns related to attrition and persistence. In order to better understand why students are not succeeding in large foundational engineering courses, we developed a form to collect data on why students withdraw from certain courses. The form was included as a requirement during the withdrawal process.
In this paper, we analyzed course withdrawal data from several academic departments in charge of teaching large foundational engineering courses, and institutional transcript data for the Spring 2018 semester. The withdrawal dataset includes the final grades that students expected to receive in the course and the factors that influenced their decision to withdraw. Institutional transcript data includes demographic information (e.g., gender, major), admissions data (e.g., SAT scores, high school GPA), and institutional academic information (e.g., course grades, cumulative GPA). Results provide a better understanding of the main reasons students decide to withdraw from a course, including having unsatisfactory grades, not understanding the professor, and being overwhelmed with work. We also analyzed locus of control for the responses, finding that the majority of students withdrawing courses consider that the problem is outside of their control and comes from an external source. We provide analysis by different departments and different specific courses. Implications for administrators, practitioners, and researchers are provided.
Woods, Jr., J. C., & Chowdhury, T. M., & Murzi, H., & Soledad, M., & Knight, D. B., & Grohs, J. R., & Case, S. W., & Smith, N. (2019, June), Factors Influencing Course Withdrawal in Fundamental Engineering Courses in a Research 1 University Paper presented at 2019 ASEE Annual Conference & Exposition , Tampa, Florida. 10.18260/1-2--32834
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