Mehrabi, A., & Morphew, J. (2024, June), Board 73: AI Skills-based Assessment Tool for Identifying Partial and Full-Mastery within Large Engineering Classrooms  Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. 10.18260/1-2--48372

\bibitem{asee_peer_48372}
  Mehrabi, A., \& Morphew, J. (2024, June), \emph{Board 73: AI Skills-based Assessment Tool for Identifying Partial and Full-Mastery within Large Engineering Classrooms}
  Paper presented at 2024 ASEE Annual Conference \& Exposition, Portland, Oregon.
  10.18260/1-2--48372

Amirreza Mehrabi and Jason Morphew.  "Board 73: AI Skills-based Assessment Tool for Identifying Partial and Full-Mastery within Large Engineering Classrooms".  2024 ASEE Annual Conference & Exposition, Portland, Oregon, 2024, June.  ASEE Conferences, 2024.  https://peer.asee.org/48372 Internet.  14 Dec, 2024

\bibitem{asee_peer_48372}
  Amirreza Mehrabi and Jason Morphew.
  "Board 73: AI Skills-based Assessment Tool for Identifying Partial and Full-Mastery within Large Engineering Classrooms".
  \emph{2024 ASEE Annual Conference \& Exposition, Portland, Oregon, 2024, June}.
  ASEE Conferences, 2024.
  https://peer.asee.org/48372 Internet.  14 Dec, 2024

@INPROCEEDINGS{asee_peer_48372
author = "Amirreza Mehrabi and Jason Morphew"
title = "Board 73: AI Skills-based Assessment Tool for Identifying Partial and Full-Mastery within Large Engineering Classrooms"
booktitle = "2024 ASEE Annual Conference \& Exposition"
year = "2024"
month = "June"
address = "Portland, Oregon"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/48372}
number = {10.18260/1-2--48372}
}

TY  - CPAPER
AB  - 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.
AU  - Amirreza Mehrabi
AU  - Jason Morphew
CY  - Portland, Oregon
DA  - 2024/06/23
PB  - ASEE Conferences
TI  - Board 73: AI Skills-based Assessment Tool for Identifying Partial and Full-Mastery within Large Engineering Classrooms
UR  - https://peer.asee.org/48372
DO  - 10.18260/1-2--48372
ER  -