Representing Time Series Data in Intelligent Training Systems

Authors

  • Shengnan Hu University of Central Florida
  • Zerong Xi University of Central Florida
  • Greg McGowin University of Central Florida
  • Gita Sukthankar University of Central Florida
  • Stephen M. Fiore University of Central Florida
  • Kevin Oden Lockheed Martin

DOI:

https://doi.org/10.32473/flairs.v34i1.128508

Keywords:

pilot training, time series data, embeddings, dynamic time warping, student modeling

Abstract

Many of the most popular intelligent training systems, including driving and flight simulators, generate user time series data. This paper presents a comparison of representation options for two different student modeling problems: 1) early failure prediction and 2) classifying student activities. Data for this analysis was gathered from pilots executing simple tasks in a virtual reality flight simulator. We demonstrate that our proposed embedding which uses a combination of dynamic time warping (DTW) and multidimensional scaling (MDS) is valuable for both student modeling tasks. However, Euclidean distance + MDS was found to be a superior embedding for predicting student failure, since DTW can obscure important agility differences between successful and unsuccessful pilots.

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Published

2021-04-18

How to Cite

Hu, S., Xi, Z., McGowin, G., Sukthankar, G., Fiore, S. M., & Oden, K. (2021). Representing Time Series Data in Intelligent Training Systems. The International FLAIRS Conference Proceedings, 34. https://doi.org/10.32473/flairs.v34i1.128508

Issue

Section

Special Track: Intelligent Learning Technologies