TY - JOUR
T1 - Grading OSPE Questions with Decision Learning Trees
T2 - AAAI 2021 Fall Symposium on Human Partnership with Medical AI: Design, Operationalization, and Ethics, AAAI-HUMAN 2021
AU - Bernard, Jason
AU - Wainman, Bruce
AU - Walker, O'Lencia
AU - Pitt, Courney
AU - Bayer, Ilana
AU - Mitchell, Josh
AU - Bak, Alex
AU - Saraco, Anthony
AU - Sonnadara, Ranil
N1 - Publisher Copyright:
Copyright © 2021,for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
PY - 2021
Y1 - 2021
N2 - Intelligent tutoring systems (ITSs) have been used for decades as a means for improving the quality of education for learners primarily by providing guidance to students based on a student model, e.g., predicting their knowledge level on a subject. There have been few attempts to incorporate ITSs into anatomical education. Objective structured practical examinations (OSPEs) are an important, albeit challenging, means of evaluation in anatomical education. This research aims to create an ITS for anatomical OSPEs, and as a crucial first step looks to create a machine learning-based approach for grading OSPEs. To that end, decision tree learning was evaluated with, and without, spellchecking to produce a grading tool using the answer key developed by instructional assistants. Using answers from 428 learners, the tool obtained an average accuracy of 96.8% (SD = 3.4%) across 60 questions.
AB - Intelligent tutoring systems (ITSs) have been used for decades as a means for improving the quality of education for learners primarily by providing guidance to students based on a student model, e.g., predicting their knowledge level on a subject. There have been few attempts to incorporate ITSs into anatomical education. Objective structured practical examinations (OSPEs) are an important, albeit challenging, means of evaluation in anatomical education. This research aims to create an ITS for anatomical OSPEs, and as a crucial first step looks to create a machine learning-based approach for grading OSPEs. To that end, decision tree learning was evaluated with, and without, spellchecking to produce a grading tool using the answer key developed by instructional assistants. Using answers from 428 learners, the tool obtained an average accuracy of 96.8% (SD = 3.4%) across 60 questions.
UR - https://www.scopus.com/pages/publications/85123441208
M3 - Conference article
AN - SCOPUS:85123441208
SN - 1613-0073
VL - 3068
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
Y2 - 4 November 2021 through 6 November 2021
ER -