2024 ASEE Annual Conference & Exposition

Investigating and predicting the Cognitive Fatigue Threshold as a Factor of Performance Reduction in Assessment

Presented at DSA Technical Session 8

Equity in engineering education hinges on the ability to fairly evaluate students. A critical issue in assessment is whether cognitive fatigue, which is marked by decreased performance during prolonged cognitive tasks, is a component of student proficiency or should be considered a measurement error. This raises crucial concerns about assessment fairness and the potential risk of inequitable attrition rates in engineering. Cognitive fatigue is often related to individual fatigue thresholds. Therefore, an important question is how instructors can arrange challenging questions in an exam and select the optimal number of questions for assessments. Item Response Theory (IRT) elucidates the relationship between students' latent traits and item features. At the same time, Machine Learning (ML) predicts the impact of features like item difficulty, discrimination, item order, and response time on each student's response pattern. By combining IRT and ML models, this study shows the effect of cognitive fatigue on student responses and provides an AI tool to predict the cognitive fatigue threshold during the exams for each student. The result of this cognitive fatigue threshold prediction not only helps instructors reduce the impact of cognitive fatigue on student performance by making better decisions on their exam duration and distribution of difficult items but can also reduce cheating on exams. This study underscores the significance of fostering awareness among students and professors regarding cognitive states during assessments and the provision of constructive feedback on student performance.

Authors

Mr. Amirreza Mehrabi Purdue Engineering Education [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, West Lafayette [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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