2024 ASEE Annual Conference & Exposition

Board 73: AI Skills-based Assessment Tool for Identifying Partial and Full-Mastery within Large Engineering Classrooms

Presented at Educational Research and Methods Division (ERM) Poster Session

In the evolving landscape of engineering education, there's a pronounced shift from traditional learning to skill-based curricula that promote active learning. Such curricula are primed to foster design thinking, which accentuates a creative and iterative problem-solving methodology. Nonetheless, for effective skill-based instruction, educators must first grasp the nuances and potential pitfalls inherent in the learning process for each student that prevent students from mastering each skill. This cognizance aids educators in refining curricular components based on student performance and providing meaningful feedback for the students. The purpose of this study is to explain the impact of different levels of cognition mistakes on the required interventions for each student to navigate them to the mastery level. So, we introduce the Partial-Mastery Cognitive Diagnosis (PMCD) model as an Artificial Intelligent-driven tool to optimize and assess skill mastery within large engineering classroom assessments. The model classifies specific cognitive errors made by students and defines new ways of identifying students who are not fully mastered but have an explainable cognition error. The results enable educators to create interventions that pinpoint and rectify these classified misconceptions, adapt curriculum based on student mastery, and provide targeted reeducation and feedback for each cognitive error.

Authors

Mr. Amirreza Mehrabi Purdue University [biography]

I am Amirreza Mehrabi, a Ph.D. student in Engineering Education at Purdue University, West Lafayette. Now I am working in computer adaptive testing (CAT) enhancement with AI and analyzing big data with machine learning (ML) under Prof. J. W. Morphew at the ENE department. My master's was in engineering education at UNESCO chair on Engineering Education at the University of Tehran. I pursue Human adaptation to technology and modeling human behavior(with machine learning and cognitive research). My background is in Industrial Engineering (B.Sc. at the Sharif University of Technology and "Gold medal" of Industrial Engineering Olympiad (Iran-2021- the highest-level prize in Iran)). Now I am working as a researcher in the Erasmus project, which is funded by European Unions (1M $_European Union & 7 Iranian Universities) which focus on TEL and students as well as professors' adoption of technology(modern Education technology). Moreover, I cooperated with Dr. Taheri to write the "R application in Engineering statistics" (an attachment of his new book "Engineering probability and statistics.")
Dr. Jason Morphew http://orcid.org/0000-0001-5971-214X Purdue University [biography]

Jason W. Morphew is an Assistant Professor in the School of Engineering Education at Purdue University. He earned a B.S. in Science Education from the University of Nebraska and spent 11 years teaching math and science at the middle school, high school, and community college level. He earned a M.A. in Educational Psychology from Wichita State and a Ph.D. from the University of Illinois Urbana-Champaign.

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