Exploration of Feature Representations for Predicting Learning and Retention Outcomes in a VR Training Scenario

Training and education of real-world tasks in Virtual Reality (VR) has seen growing use in industry. The motion-tracking data that is intrinsic to immersive VR applications is rich and can be used to improve learning beyond standard training interfaces. In this paper, we present machine learning (ML...

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Bibliographic Details
Main Authors: Alec G. Moore, Ryan P. McMahan, Nicholas Ruozzi
Format: Article
Language:English
Published: MDPI AG 2021-07-01
Series:Big Data and Cognitive Computing
Subjects:
Online Access:https://www.mdpi.com/2504-2289/5/3/29
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spelling doaj-8e8b441c7e38472ca3fdeb313f7156012021-09-25T23:45:16ZengMDPI AGBig Data and Cognitive Computing2504-22892021-07-015292910.3390/bdcc5030029Exploration of Feature Representations for Predicting Learning and Retention Outcomes in a VR Training ScenarioAlec G. Moore0Ryan P. McMahan1Nicholas Ruozzi2Department of Computer Science, College of Engineering and Computer Science, University of Central Florida, Orlando, FL 32816, USADepartment of Computer Science, College of Engineering and Computer Science, University of Central Florida, Orlando, FL 32816, USA Department of Computer Science, Erik Jonsson School of Engineering and Computer Science, The University of Texas at Dallas, Richardson, TX 75080, USATraining and education of real-world tasks in Virtual Reality (VR) has seen growing use in industry. The motion-tracking data that is intrinsic to immersive VR applications is rich and can be used to improve learning beyond standard training interfaces. In this paper, we present machine learning (ML) classifiers that predict outcomes from a VR training application. Our approach makes use of the data from the tracked head-mounted display (HMD) and handheld controllers during VR training to predict whether a user will exhibit high or low knowledge acquisition, knowledge retention, and performance retention. We evaluated six different sets of input features and found varying degrees of accuracy depending on the predicted outcome. By visualizing the tracking data, we determined that users with higher acquisition and retention outcomes made movements with more certainty and with greater velocities than users with lower outcomes. Our results demonstrate that it is feasible to develop VR training applications that dynamically adapt to a user by using commonly available tracking data to predict learning and retention outcomes.https://www.mdpi.com/2504-2289/5/3/29virtual realitymachine learningintelligent tutoring systemstraining
collection DOAJ
language English
format Article
sources DOAJ
author Alec G. Moore
Ryan P. McMahan
Nicholas Ruozzi
spellingShingle Alec G. Moore
Ryan P. McMahan
Nicholas Ruozzi
Exploration of Feature Representations for Predicting Learning and Retention Outcomes in a VR Training Scenario
Big Data and Cognitive Computing
virtual reality
machine learning
intelligent tutoring systems
training
author_facet Alec G. Moore
Ryan P. McMahan
Nicholas Ruozzi
author_sort Alec G. Moore
title Exploration of Feature Representations for Predicting Learning and Retention Outcomes in a VR Training Scenario
title_short Exploration of Feature Representations for Predicting Learning and Retention Outcomes in a VR Training Scenario
title_full Exploration of Feature Representations for Predicting Learning and Retention Outcomes in a VR Training Scenario
title_fullStr Exploration of Feature Representations for Predicting Learning and Retention Outcomes in a VR Training Scenario
title_full_unstemmed Exploration of Feature Representations for Predicting Learning and Retention Outcomes in a VR Training Scenario
title_sort exploration of feature representations for predicting learning and retention outcomes in a vr training scenario
publisher MDPI AG
series Big Data and Cognitive Computing
issn 2504-2289
publishDate 2021-07-01
description Training and education of real-world tasks in Virtual Reality (VR) has seen growing use in industry. The motion-tracking data that is intrinsic to immersive VR applications is rich and can be used to improve learning beyond standard training interfaces. In this paper, we present machine learning (ML) classifiers that predict outcomes from a VR training application. Our approach makes use of the data from the tracked head-mounted display (HMD) and handheld controllers during VR training to predict whether a user will exhibit high or low knowledge acquisition, knowledge retention, and performance retention. We evaluated six different sets of input features and found varying degrees of accuracy depending on the predicted outcome. By visualizing the tracking data, we determined that users with higher acquisition and retention outcomes made movements with more certainty and with greater velocities than users with lower outcomes. Our results demonstrate that it is feasible to develop VR training applications that dynamically adapt to a user by using commonly available tracking data to predict learning and retention outcomes.
topic virtual reality
machine learning
intelligent tutoring systems
training
url https://www.mdpi.com/2504-2289/5/3/29
work_keys_str_mv AT alecgmoore explorationoffeaturerepresentationsforpredictinglearningandretentionoutcomesinavrtrainingscenario
AT ryanpmcmahan explorationoffeaturerepresentationsforpredictinglearningandretentionoutcomesinavrtrainingscenario
AT nicholasruozzi explorationoffeaturerepresentationsforpredictinglearningandretentionoutcomesinavrtrainingscenario
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