TY - CONF
T1 - Modeling the effects of students’ interactions with immersive simulations using Markov switching systems
AU - Hoernle, Nicholas
AU - Gal, Kobi
AU - Grosz, Barbara
AU - Protopapas, Pavlos
AU - Rubin, Andee
N1 - Funding Information:
This paper is based on work supported by the National Science Foundation under grant IIS-1623124. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. Thank you to Prof. Leilah Lyons (NYSci; University of Illinois-Chicago) and Aditi Mallavarapu (University of Illinois-Chicago) for their input on this work; and NYSci for assisting with the data collection.
Funding Information:
This paper is based on work supported by the National Science Foundation under grant IIS-1623124. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
Publisher Copyright:
© 2018 International Educational Data Mining Society. All rights reserved.
PY - 2018/1/1
Y1 - 2018/1/1
N2 - Simulations that combine real world components with interactive digital media provide a rich setting for students with the potential to assist knowledge building and understanding of complex physical processes. This paper addresses the problem of modeling the effects of multiple students’ simultaneous interactions on the complex and exploratory environments such simulations provide. We work towards assisting educators with the difficult task of interpreting student exploration. We represent the system dynamics that result from student actions with a complex time series and use switch based models to decompose the time series into individual periods that target interpretability for teachers. The model learns the transition points between successive periods in the time series as well as the internal dynamics that govern each period. This model differs from other switch based models in that it decomposes the time series in a way that is human interpretable. This approach was applied to data that was obtained from an existing multi-person simulation with pedagogical goals of teaching sustainability and systems thinking. A visualization of the model was designed to validate the interpretability of the generated text-based descriptions when compared to a movie representation of the system dynamics. A pilot study using this visualization indicates that the switch based model finds relevant boundaries between salient periods of student work.
AB - Simulations that combine real world components with interactive digital media provide a rich setting for students with the potential to assist knowledge building and understanding of complex physical processes. This paper addresses the problem of modeling the effects of multiple students’ simultaneous interactions on the complex and exploratory environments such simulations provide. We work towards assisting educators with the difficult task of interpreting student exploration. We represent the system dynamics that result from student actions with a complex time series and use switch based models to decompose the time series into individual periods that target interpretability for teachers. The model learns the transition points between successive periods in the time series as well as the internal dynamics that govern each period. This model differs from other switch based models in that it decomposes the time series in a way that is human interpretable. This approach was applied to data that was obtained from an existing multi-person simulation with pedagogical goals of teaching sustainability and systems thinking. A visualization of the model was designed to validate the interpretability of the generated text-based descriptions when compared to a movie representation of the system dynamics. A pilot study using this visualization indicates that the switch based model finds relevant boundaries between salient periods of student work.
KW - Bayesian inference
KW - Exploratory learning environment
KW - Interpretability
KW - Markov chain Monte Carlo
KW - Switching state space models
UR - http://www.scopus.com/inward/record.url?scp=85084011397&partnerID=8YFLogxK
M3 - Paper
AN - SCOPUS:85084011397
T2 - 11th International Conference on Educational Data Mining, EDM 2018
Y2 - 15 July 2018 through 18 July 2018
ER -