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Community colleges play an important role in educating future scientists and engineers, especially among students from groups that are traditionally underrepresented in science, technology, engineering, and mathematics. Community college transfer programs offer lower-division courses that students can take in preparation for transfer to a four-year program. For many small community colleges, however, developing a comprehensive transfer engineering program that prepares students to be competitive for transfer can be challenging due to a lack of facilities, resources, and local expertise. As a result, engineering education becomes inaccessible to many community college students. Through a grant from the National Science Foundation Improving Undergraduate STEM Education program (NSF IUSE), three community colleges from Northern California collaborated to develop resources and teaching strategies to enable small-to-medium community college engineering programs to support a comprehensive set of lower-division engineering courses that are delivered either completely online, or with limited face-to-face interactions. This paper focuses on the development and testing of the teaching and learning resources for Engineering Graphics, which is a four-unit course (three units of lecture and one unit of lab) covering the principles of engineering drawings, computer-aided design (using both AutoCAD and SolidWorks), and the engineering design process. The paper also presents the results of the pilot implementation of the curriculum, as well as a comparison of the outcomes of the online course with those from a regular, face-to-face course. Student performance on labs and tests in the two parallel sections of the course are compared. Additionally student surveys and interviews, conducted in both the online and face-to-face course are used to document and compare students’ perceptions of their learning experience, the effectiveness of the course resources, their use of these resources, and their overall satisfaction with the course.
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Undergraduate Robotics Education with General Instructors Using a Student-Centered Personalized Learning Framework
Recent advancements in robotics, including applications like self-driving cars, unmanned systems, and medical robots, have had a significant impact on the job market. On one hand, big robotics companies offer training programs based on the job requirements. However, these training programs may not be as beneficial as general robotics programs offered by universities or community colleges. On the other hand, community colleges and universities face challenges with the required resources, especially qualified instructors, to offer students advanced robotics education. Furthermore, the diverse backgrounds of undergraduate students present additional challenges. Some students bring extensive industry experience, while others are newcomers to the field. To address these challenges, we propose a student-centered personalized learning framework for robotics. This framework allows a general instructor to teach undergraduate-level robotics courses by breaking down course topics into smaller components with well-defined topic dependencies, structured as a graph. This modular approach enables students to choose their learning path, catering to their unique preferences and pace. Moreover, our framework's flexibility allows for easy customization of teaching materials to meet the specific needs of host institutions. In addition to teaching materials, a frequently-asked-questions document would be prepared for a general instructor. If students' robotics questions cannot be answered by the instructor, the answers to these questions may be included in this document. For questions not covered in this document, we can gather and address them through collaboration with the robotics community and course content creators. Our user study results demonstrate the promise of this method in delivering undergraduate-level robotics education tailored to individual learning outcomes and preferences.
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- Award ID(s):
- 2142360
- PAR ID:
- 10595252
- Publisher / Repository:
- ASEE 2024 Conference
- Date Published:
- Format(s):
- Medium: X
- Location:
- Portland, Oregon
- Sponsoring Org:
- National Science Foundation
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