Keywords

Virtual environment, game-based learning, machinelearning, eye-tracking, feature extraction, neuroeducation

Abstract

At present, the use of eye-tracking data in immersive Virtual Reality (iVR) learning environments is set to become a powerful tool for maximizing learning outcomes, due to the low-intrusiveness of eye-tracking technology and its integration in commercial iVR Head Mounted Displays. However, the most suitable technologies for data processing should first be identified before their use in learning environments can be generalized. In this research, the use of machine-learning techniques is proposed for that purpose, evaluating their capabilities to classify the quality of the learning environment and to predict user learning performance. To do so, an iVR learning experience simulating the operation of a bridge crane was developed. Through this experience, the performance of 63 students was evaluated, both under optimum learning conditions and under stressful conditions. The final dataset included 25 features, mostly temporal series, with a dataset size of up to 50M data points. The results showed that different classifiers (KNN, SVM and Random Forest) provided the highest accuracy when predicting learning performance variations, while the accuracy of user learning performance was still far from optimized, opening a new line of future research. This study has the objective of serving as a baseline for future improvements to model accuracy using complex machine-learning techniques.

View infography

References

Añaños-Carrasco, E. (2015). Eyetracker technology in elderly people: How integrated television content is paid attention to and processed. [La tecnología del «EyeTracker» en adultos mayores: Cómo se atienden y procesan los contenidos integrados de televisión]. Comunicar, 45, 75-83. https://doi.org/10.3916/C45-2015-08

Link DOI | Link Google Scholar

Asish, S.M., Kulshreshth, A.K., & Borst, C.W. (2022). Detecting distracted students in educational VR environments using machine learning on eye gaze data. Computers & Graphics, 109, 75-87. https://doi.org/10.1016/j.cag.2022.10.007

Link DOI | Link Google Scholar

Bowman, D.A., & McMahan, R.P. (2007). Virtual reality: How much immersion is enough? Computer, 40(7), 36-43. https://doi.org/10.1109/MC.2007.257

Link DOI | Link Google Scholar

Checa, D., & Bustillo, A. (2020). A review of immersive virtual reality serious games to enhance learning and training. Multimedia Tools and Applications, 79(9-10), 5501–5527. https://doi.org/10.1007/s11042-019-08348-9

Link DOI | Link Google Scholar

Checa, D., & Bustillo, A. (2022). Grua Rv. http://3dubu.Es/En/Cranevr/

Link Google Scholar

Checa, D., Gatto, C., Cisternino, D., de Paolis, L.T., & Bustillo, A. (2020). A Framework for Educational and Training Immersive Virtual Reality Experiences. In L.T. de-Paolis & P. Bourdot (Eds.), Augmented reality, virtual reality, and computer graphics (pp. 220–228). Springer International Publishing. https://doi.org/10.1007/978-3-030-58468-9_17

Link DOI | Link Google Scholar

Christ, M., Braun, N., Neuffer, J., & Kempa-Liehr, A.W. (2018). Time series feature extraction on basis of scalable hypothesis tests (tsfresh – A Python package). Neurocomputing, 307, 72-77. https://doi.org/https://doi.org/10.1016/j.neucom.2018.03.067

Link Google Scholar

Christ, M., Kempa-Liehr, A., & Feindt, M. (2016). Distributed and parallel time series feature extraction for industrial big data applications. ArXiv, 1, https://doi.org/10.48550/arXiv.1610.07717.

Link DOI | Link Google Scholar

Cowan, A., Chen, J., Mingo, S., Reddy, S.S., Ma, R., Marshall, S., Nguyen, J.H., & Hung, A.J. (2021). virtual reality vs dry laboratory models: Comparing automated performance metrics and cognitive workload during robotic simulation training. Journal of Endourology, 35(10), 1571-1576. https://doi.org/10.1089/end.2020.1037

Link DOI | Link Google Scholar

Dale, E. (1946). Audiovisual methods in teaching. Dryden Press. https://bit.ly/42aW03X

Link Google Scholar

Dalgarno, B., & Lee, M.J.W. (2010). What are the learning affordances of 3-D virtual environments? British Journal of Educational Technology, 41(1). https://doi.org/10.1111/j.1467-8535.2009.01038.x

Link DOI | Link Google Scholar

Dede, C. (2009). Immersive interfaces for engagement and learning. Science, 323(5910), 66-69. https://doi.org/10.1126/science.1167311

Link DOI | Link Google Scholar

Deng, Q., Wang, J., Hillebrand, K., Benjamin, C.R., & Soffker, D. (2020). Prediction performance of lane changing behaviors: A study of combining environmental and eye-tracking data in a driving simulator. IEEE Transactions on Intelligent Transportation Systems, 21(8), 3561-3570. https://doi.org/10.1109/TITS.2019.2937287

Link DOI | Link Google Scholar

Duchowski, A. T. (2002). A breadth-first survey of eye-tracking applications. Behavior Research Methods, Instruments, & Computers, 34(4), 455-470. https://doi.org/10.3758/BF03195475

Link DOI | Link Google Scholar

Farran, E., Formby, S., Daniyal, F., Holmes, T., & Herwegen, J. (2016). Route-learning strategies in typical and atypical development; eye-tracking reveals atypical landmark selection in Williams syndrome: Route-learning and eye-tracking. Journal of Intellectual Disability Research, 60(10), 933-944. https://doi.org/10.1111/jir.12331

Link DOI | Link Google Scholar

García-Carrasco, J., Hernández-Serrano, M.J., & Martín-García, A.V. (2015). Plasticity as a framing concept enabling transdisciplinary understanding and research in neuroscience and education. Learning, Media and Technology, 40(2), 152-167. https://doi.org/10.1080/17439884.2014.908907

Link DOI | Link Google Scholar

Gardony, A.L., Lindeman, R.W., & Brunyé, T.T. (2020). Eye-tracking for human-centered mixed reality: Promises and challenges. Proc.SPIE, 11310, 113100T. https://doi.org/10.1117/12.2542699

Link DOI | Link Google Scholar

Glennon, J.M., D’Souza, H., Mason, L., Karmiloff-Smith, A., & Thomas, M.S.C. (2020). Visuo-attentional correlates of Autism Spectrum Disorder (ASD) in children with Down syndrome: A comparative study with children with idiopathic ASD. Research in Developmental Disabilities, 104, 103678. https://doi.org/10.1016/j.ridd.2020.103678

Link DOI | Link Google Scholar

Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. (2008). The WEKA data mining software: An update. SIGKDD Explor. Newsl., 11(1), 10-18. https://doi.org/10.1145/1656274.1656278

Link DOI | Link Google Scholar

Huang, H.M., Rauch, U., & Liaw, S.S. (2010). Investigating learners’ attitudes toward virtual reality learning environments: Based on a constructivist approach. Computers and Education, 55(3), 1171-1182. https://doi.org/10.1016/j.compedu.2010.05.014

Link DOI | Link Google Scholar

Lapborisuth, P., Koorathota, S., Wang, Q., & Sajda, P. (2021). Integrating neural and ocular attention reorienting signals in virtual reality. Journal of Neural Engineering, 18(6), 066052. https://doi.org/10.1088/1741-2552/ac4593

Link DOI | Link Google Scholar

Ma, X., Yao, Z., Wang, Y., Pei, W., & Chen, H. (2018). Combining brain-computer interface and eye-tracking for high-speed text entry in virtual reality. In Berkovsky & Y. Hijikata (Ed.), IUI ’18: 23rd International Conference on Intelligent User Interfaces (pp. 263-267). https://doi.org/10.1145/3172944.3172988

Link DOI | Link Google Scholar

Martinez, K., Menéndez-Menéndez, M.I., & Bustillo, A. (2021). Awareness, prevention, detection, and therapy applications for depression and anxiety in serious games for children and adolescents: Systematic review. JMIR Serious Games, 9(4), e30482. https://doi.org/10.2196/30482

Link DOI | Link Google Scholar

Mckinney, W. (2011). pandas: A foundational Python library for data analysis and statistics. Python High Performance Science Computer.

Link Google Scholar

Patney, A., Salvi, M., Kim, J., Kaplanyan, A., Wyman, C., Benty, N., Luebke, D., & Lefohn, A. (2016). Towards foveated rendering for gaze-tracked virtual reality. ACM Trans. Graph., 35(6), 1-12. https://doi.org/10.1145/2980179.2980246

Link DOI | Link Google Scholar

Pritchard, A. (2017). Ways of learning: Learning theories for the classroom. Routledge. https://doi.org/10.4324/9781315460611

Link DOI | Link Google Scholar

Rappa, N.A., Ledger, S., Teo, T., Wai Wong, K., Power, B., & Hilliard, B. (2022). The use of eye-tracking technology to explore learning and performance within virtual reality and mixed reality settings: A scoping review. Interactive Learning Environments, 30(7), 1338-1350. https://doi.org/10.1080/10494820.2019.1702560

Link DOI | Link Google Scholar

Rodero, E., & Larrea, O. (2022). Virtual reality with distractors to overcome public speaking anxiety in university students; [Realidad virtual con distractores para superar el miedo a hablar en público en universitarios]. Comunicar, 30(72). https://doi.org/10.3916/C72-2022-07

Link DOI | Link Google Scholar

Shadiev, R., & Li, D. (2022). A review study on eye-tracking technology usage in immersive virtual reality learning environments. Computers & Education, 104681. https://doi.org/10.1016/j.compedu.2022.104681

Link DOI | Link Google Scholar

Sun, Q., Patney, A., Wei, L.Y., Shapira, O., Lu, J., Asente, P., Zhu, S., McGuire, M., Luebke, D., & Kaufman, A. (2018). Towards virtual reality infinite walking: Dynamic saccadic redirection. ACM Transactions on Graphics, 37(4), 1-13. https://doi.org/10.1145/3197517.3201294

Link DOI | Link Google Scholar

Tanaka, Y., Kanari, K., & Sato, M. (2021). Interaction with virtual objects through eye-tracking. In M. Nakajima, J.G. Kim, W.N. Lie, & Q. Kemao (Eds.), International Workshop on Advanced Imaging Technology (IWAIT) 2021 (p. 1176624). SPIE. https://doi.org/10.1117/12.2590989

Link DOI | Link Google Scholar

Tavenard, R., Faouzi, J., Vandewiele, G., Divo, F., Androz, G., Holtz, C., Payne, M., Yurchak, R., Rußwurm, M., Kolar, K., & Woods, E. (2020). Tslearn, A Machine-learning Toolkit for Time Series Data. J. Mach. Learn. Res., 21, 118, 1-6.

Link Google Scholar

Wilson, M. (2002). Six views of embodied cognition. Psychonomic Bulletin and Review, 9, 625-636. https://doi.org/10.3758/BF03196322

Link DOI | Link Google Scholar

Wismer, P., Soares, S.A., Einarson, K.A., & Sommer, M.O.A. (2022). Laboratory performance prediction using virtual reality behaviometrics. PloS One, 17(12), e0279320. https://doi.org/10.1371/journal.pone.0279320

Link DOI | Link Google Scholar

Crossmark

Technical information

Received: 26-12-2022

Revised: 25-01-2023

Accepted: 23-02-2023

OnlineFirst: 30-05-2023

Publication date: 01-07-2023

Article revision time: 30 days | Average time revision issue 76: -6 days

Article acceptance time: 59 days | Average time of acceptance issue 76: 72 days

Preprint editing time: 142 days | Average editing time preprint issue 76: 155 days

Article editing time: 187 days | Average editing time issue 76: 200 days

Metrics

Metrics of this article

Views: 42357

Abstract readings: 41001

PDF downloads: 1356

Full metrics of Comunicar 76

Views: 470526

Abstract readings: 459497

PDF downloads: 11029

Cited by

Cites in Web of Science

Currently there are no citations to this document

Cites in Scopus

Currently there are no citations to this document

Cites in Google Scholar

Detection of Stress Stimuli in Learning Contexts of iVR Environments JM Ramírez-Sanz, HM Peña-Alonso… - … on Extended Reality, 2023 - Springer

https://link.springer.com/chapter/10.1007/978-3-031-43404-4_29

Download

Alternative metrics

How to cite

Serrano-Mamolar, A., Miguel-Alonso, I., Checa, D., & Pardo-Aguilar, C. (2023). Towards learner performance evaluation in iVR learning environments using eye-tracking and Machine-learning. [Hacia una metodología de evaluación del rendimiento del alumno en entornos de aprendizaje iVR utilizando eye-tracking y aprendizaje automático]. Comunicar, 76, 9-20. https://doi.org/10.3916/C76-2023-01

Share

           

Oxbridge Publishing House

4 White House Way

B91 1SE Sollihul United Kingdom

Administration

Editorial office

Creative Commons

This website uses cookies to obtain statistical data on the navigation of its users. If you continue to browse we consider that you accept its use. +info X