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Modeling Interactive Behaviors While Learning With Digitized Objects in Virtual Reality Environments

Modeling Interactive Behaviors While Learning With Digitized Objects in Virtual Reality Environments

Eric Poitras, Kirsten R. Butcher, Matthew P. Orr
Copyright: © 2022 |Pages: 20
ISBN13: 9781668463154|ISBN10: 1668463156|EISBN13: 9781668463161
DOI: 10.4018/978-1-6684-6315-4.ch024
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MLA

Poitras, Eric, et al. "Modeling Interactive Behaviors While Learning With Digitized Objects in Virtual Reality Environments." Research Anthology on Interventions in Student Behavior and Misconduct, edited by Information Resources Management Association, IGI Global, 2022, pp. 448-467. https://doi.org/10.4018/978-1-6684-6315-4.ch024

APA

Poitras, E., Butcher, K. R., & Orr, M. P. (2022). Modeling Interactive Behaviors While Learning With Digitized Objects in Virtual Reality Environments. In I. Management Association (Ed.), Research Anthology on Interventions in Student Behavior and Misconduct (pp. 448-467). IGI Global. https://doi.org/10.4018/978-1-6684-6315-4.ch024

Chicago

Poitras, Eric, Kirsten R. Butcher, and Matthew P. Orr. "Modeling Interactive Behaviors While Learning With Digitized Objects in Virtual Reality Environments." In Research Anthology on Interventions in Student Behavior and Misconduct, edited by Information Resources Management Association, 448-467. Hershey, PA: IGI Global, 2022. https://doi.org/10.4018/978-1-6684-6315-4.ch024

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Abstract

This chapter outlines a framework for automated detection of student behaviors in the context of virtual learning environments. The components of the framework establish several parameters for data acquisition, preprocessing, and processing as a means to classify different types of behaviors. The authors illustrate these steps in training and evaluating a detector that differentiates between students' observations and functional behaviors while students interact with three-dimensional (3D) virtual models of dinosaur fossils. Synthetic data were generated in controlled conditions to obtain time series data from different channels (i.e., orientation from the virtual model and remote controllers) and modalities (i.e., orientation in the form of Euler angles and quaternions). Results suggest that accurate detection of interaction behaviors with 3D virtual models requires smaller moving windows to segment the log trace data as well as features that characterize orientation of virtual models in the form of quaternions. They discuss the implications for personalized instruction in virtual learning environments.

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