A windowed time series of having multiple dimensions that measure the spatial extent of reference points, such as its orientation, scale, or position, is determined by the sampling rate and window size. The issue is that when the dimensionality increases, a larger amount of windowed time series with each combination of values is required to properly learn patterns that exemplify a class of behaviors.
Published in Chapter:
Modeling Interactive Behaviors While Learning With Digitized Objects in Virtual Reality Environments
Eric Poitras (The University of Utah, USA), Kirsten R. Butcher (The University of Utah, USA), and Matthew P. Orr (The University of Utah, USA)
Copyright: © 2020
|Pages: 20
DOI: 10.4018/978-1-7998-3250-8.ch011
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.