TY - GEN
T1 - Detecting Teacher Expertise in an Immersive VR Classroom
T2 - 22nd IEEE International Symposium on Mixed and Augmented Reality, ISMAR 2023
AU - Gao, Hong
AU - Bozkir, Efe
AU - Stark, Philipp
AU - Goldberg, Patricia
AU - Meixner, Gerrit
AU - Kasneci, Enkelejda
AU - Gollner, Richard
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Currently, VR technology is increasingly being used in applications to enable immersive yet controlled research settings. One such area of research is expertise assessment, where novel technological approaches to collecting process data, specifically eye tracking, in combination with explainable models, can provide insights into assessing and training novices, as well as fostering expertise development. We present a machine learning approach to predict teacher expertise by leveraging data from an off-the-shelf VR device collected in a VirATec study. By fusing eye-tracking and controller-tracking data, teachers' recognition and handling of disruptive events in the classroom are taken into account or considered. Three classification models were compared, including SVM, Random Forest, and LightGBM, with Random Forest achieving the best ROC-AUC score of 0.768 in predicting teacher expertise. The SHAP approach to model interpretation revealed informative features (e.g., fixations on identified disruptive students) for distinguishing teacher expertise. Our study serves as a pioneering effort in assessing teacher expertise using eye tracking within an interactive virtual setting, paving the way for future research and advancements in the field.
AB - Currently, VR technology is increasingly being used in applications to enable immersive yet controlled research settings. One such area of research is expertise assessment, where novel technological approaches to collecting process data, specifically eye tracking, in combination with explainable models, can provide insights into assessing and training novices, as well as fostering expertise development. We present a machine learning approach to predict teacher expertise by leveraging data from an off-the-shelf VR device collected in a VirATec study. By fusing eye-tracking and controller-tracking data, teachers' recognition and handling of disruptive events in the classroom are taken into account or considered. Three classification models were compared, including SVM, Random Forest, and LightGBM, with Random Forest achieving the best ROC-AUC score of 0.768 in predicting teacher expertise. The SHAP approach to model interpretation revealed informative features (e.g., fixations on identified disruptive students) for distinguishing teacher expertise. Our study serves as a pioneering effort in assessing teacher expertise using eye tracking within an interactive virtual setting, paving the way for future research and advancements in the field.
KW - Classification and regression trees
KW - Computing methodologies
KW - Human computer interaction (HCI)
KW - Human-centered computing
KW - Interaction paradigms
KW - Machine learning
KW - Machine learning approaches
KW - Virtual reality
UR - http://www.scopus.com/inward/record.url?scp=85180368706&partnerID=8YFLogxK
U2 - 10.1109/ISMAR59233.2023.00083
DO - 10.1109/ISMAR59233.2023.00083
M3 - Conference contribution
AN - SCOPUS:85180368706
T3 - Proceedings - 2023 IEEE International Symposium on Mixed and Augmented Reality, ISMAR 2023
SP - 683
EP - 692
BT - Proceedings - 2023 IEEE International Symposium on Mixed and Augmented Reality, ISMAR 2023
A2 - Bruder, Gerd
A2 - Olivier, Anne-Helene
A2 - Cunningham, Andrew
A2 - Peng, Evan Yifan
A2 - Grubert, Jens
A2 - Williams, Ian
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 16 October 2023 through 20 October 2023
ER -