Abstract
The USAF has established a new approach to Specialized Undergraduate Pilot Training (SUPT) called Pilot Training Next (PTN) that integrates traditional flying sorties with VR-enabled ground-based training devices and data-driven proficiency tracking to achieve training efficiencies, improve readiness, and increase throughput. Eduworks and USC’s Institute for Creative Technologies are developing machine learning (ML) models that can measure user engagement during any computer-mediated training (simulation, courseware) and offer recommendations for restoring lapses in engagement. We are currently developing and testing this approach, called the Observational Motivation and Engagement Generalized Appliance (OMEGA) in a PTN context. Two factors motivate this work. First, one goal of PTN is for an instructor pilot (IP) to simultaneously monitor multiple simulator rides. Being alerted to distraction, attention and engagement can help an IP manage multiple students at the same time, with recommendations for restoring engagement providing further instructional support. Second, the virtual environment provides a rich source of raw data that machine learning models can use to associate user activity with user engagement. We have created a testbed for data capture in order to construct the ML models, based on theoretical foundations we developed previously. We are running pilots through multiple PTN scenarios and collecting formative data from instructors to evaluate the utility of the recommendations OMEGA generates regarding how lapsed engagement can be restored. We anticipate findings that validate the use of ML models for learning to detect engagement from the rich data sources characteristic of virtual environments. These findings will be applicable across a broad range of conventional and VR training applications.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hu, P.J.H., Hui, W.: Examining the role of learning engagement in technology-mediated learning and its effects on learning effectiveness and satisfaction. Decis. Support Syst. 53(4), 782–792 (2012)
Chi, M.T., Wylie, R.: The ICAP framework: linking cognitive engagement to active learning outcomes. Educ. Psychol. 49(4), 219–243 (2014)
Core, M.G., Georgila, K., Nye, B.D., Auerbach, D., Liu, Z.F., DiNinni, R.: Learning, adaptive support, student traits, and engagement in scenario-based learning. In: I/ITSEC, 2016 (2016)
Porter, L.W., Lawler, E.E.: Managerial attitudes and performance (1968)
Gawel, J.E.: Herzberg’s theory of motivation and Maslow’s hierarchy of needs. Pract. Assess. Res. Eval. 5(11), 3 (1997)
Pintrich, P.R.: Multiple goals, multiple pathways: the role of goal orientation in learning & achievement. J. Educ. Psychol. 92(3), 544 (2000)
D’Mello, S., Graesser, A.: Dynamics of affective states during complex learning. Learn. Instruct. 22(2), 145–157 (2012)
Baker, R.S., Corbett, A.T., Roll, I., Koedinger, K.R.: Developing a generalizable detector of when students game the system. User Model. User-Adap. Interact. 18(3), 287–314 (2008)
Bell, B., Kelsey, E., Nye, B.: Monitoring engagement and motivation across learning environments. In: Proceedings of the 2019 MODSIM World Conference, Norfolk, VA (2019)
Harrivel, A.R., et al.: Prediction of cognitive states during flight simulation using multimodal psychophysiological sensing. In: AIAA Information Systems-AIAA Infotech, p. 1135 (2017)
Wickens, C.D.: Attentional tunneling and task management. In: 2005 International Symposium on Aviation Psychology, p. 812 (2005)
Cummings, M.L., Mastracchio, C., Thornburg, K.M., Mkrtchyan, A.: Boredom and distraction in multiple unmanned vehicle supervisory control. Interact. Comput. 25(1), 34–47 (2013)
Casner, S.M., Schooler, J.W.: Vigilance impossible: diligence, distraction, and daydreaming all lead to failures in a practical monitoring task. Conscious. Cogn. 35, 33–41 (2015)
Kloft, M., Stiehler, F., Zheng, Z., Pinkwart, N.: Predicting MOOC dropout over weeks using machine learning methods. In: Proceedings of EMNLP 2014 Workshop on Analysis of Large Scale Social Interaction in MOOCs, pp. 60–65 (2014)
Al-Shabandar, R., Hussain, A.J., Liatsis, P., Keight, R.: Analyzing learners behavior in MOOCs: an examination of performance and motivation using a data-driven approach. IEEE Access 6, 73669–73685 (2018)
Piech, C., et al.: Deep knowledge tracing. In: Advances in Neural Information Processing Systems, pp. 505–513 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Bell, B., Kelsey, E., Nye, B., Bennett, W.(. (2020). Adapting Instruction by Measuring Engagement with Machine Learning in Virtual Reality Training. In: Sottilare, R.A., Schwarz, J. (eds) Adaptive Instructional Systems. HCII 2020. Lecture Notes in Computer Science(), vol 12214. Springer, Cham. https://doi.org/10.1007/978-3-030-50788-6_20
Download citation
DOI: https://doi.org/10.1007/978-3-030-50788-6_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-50787-9
Online ISBN: 978-3-030-50788-6
eBook Packages: Computer ScienceComputer Science (R0)