Treffer: Engaging Multidisciplinary Engineers in an Introduction to Programming Laboratory.

Engineering students outside of computer science are required to take an introductory course in computer programming in one of several languages (MATLAB, C++, VB.net), including a laboratory component. This provides a unique challenge in engaging a group of multidisciplinary students with different programming backgrounds, especially since the lab is required by some engineering majors but optional for others. The lab had essentially turned into a recitation session with additional lecturing and reviews of homework solutions. Over the last several semesters the college has reevaluated how the lab can be useful to all disciplines, and this paper outlines the curriculum redesign to problem-based learning in a collaborative classroom. Students now work in a space designed for active learning for two periods each week, grouped in teams of six. Their goal is to solve programming challenges that range from programming fundamentals to image processing and manipulating experimental data, which stimulates the interest of all engineering disciplines. Example labs include solving programming interview questions, using image kernels to sharpen digital images, and developing a simple Microsoft Paint application. These challenges correspond to the latest lecture material, forcing students to actively work through the current learning objectives and keep pace with the course. Each lab session has the support of a faculty member and teaching assistants to guide discussions and provide just-in-time teaching. Student feedback and grades have shown students are meeting the desired learning objectives while also enjoying the challenging nature of the problems. Students with no prior programming experience have especially benefited from the new lab format with strong improvements in critical thinking, creativity, and problem solving skills. [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.)