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.
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
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: 44016
Abstract readings: 42607
PDF downloads: 1409
Full metrics of Comunicar 76
Views: 485024
Abstract readings: 473410
PDF downloads: 11614
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
Ana Serrano-Mamolar, University of Burgos (Spain)
