Open AccessRepresenting Time Series Data in Intelligent Training Systems
Author(s) -
Shanshan Hu,
Zerong Xi,
Greg McGowin,
Gita Sukthankar,
Stephen M. Fiore,
Kevin Oden
Publication year - 2021
Publication title -
proceedings of the ... international florida artificial intelligence research society conference
Language(s) - English
Resource type - Journals
eISSN - 2334-0762
pISSN - 2334-0754
DOI - 10.32473/flairs.v34i1.128508
Subject(s) - dynamic time warping , computer science , embedding , simple (philosophy) , euclidean distance , time series , representation (politics) , series (stratigraphy) , artificial intelligence , machine learning , data mining , paleontology , philosophy , epistemology , politics , political science , law , biology
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.