Treffer: Effective Educational Methods for Teaching Assistants in a First-Year Engineering MATLAB® Course.

At the University of Cincinnati, two courses were introduced during the 2012-2013 school year to provide first-year students with hands-on experience in engineering and form a link between engineering and the required mathematics and science courses. These interdisciplinary courses form a two-semester sequence in which students apply fundamental theory from algebra, trigonometry, calculus and physics to relevant engineering applications. MATLAB is introduced as a programming tool to enable students to explore engineering concepts, investigate solutions to complex problems, analyze and present data effectively, and to develop an appreciation for the power and limitations of computing tools. In an effort to improve the learning of students in the course, three sets of two teaching assistants each (TAs) tested a different educational method in the lab portion of the courses. These methods were identified by the TAs after teaching students for several weeks and analyzing the common pitfalls encountered by the students. In the first method, the TAs focus on complimenting students on their performance and encouraging them to break up the problem into smaller, more manageable steps. This fosters an engineering mindset that hopes to assist the students intimidated by programming or those that struggle with starting the problem. The next method tested involved the TAs asking questions about the coding concepts the students were using to encourage a deeper level of thought and understanding with the assignment. Lastly, the TAs questioned the students on the commands used in the lab activity and provided some initial guidance in starting the code for the assignment. The effectiveness of the different methods was assessed in two ways. In the first, qualitative data, in the form of observations and informal interviews, were collected by the TAs as they implemented the different methodologies. In the second, quantitative data, in the form of student grades on exams and lab assignments are compared between the experimental groups and a control group. The quantitative data was analyzed to support the results of the qualitative analyses. [ABSTRACT FROM AUTHOR]

Copyright of Proceedings of the ASEE Annual Conference & Exposition is the property of ASEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)