Hacia una metodología de evaluación del rendimiento del alumno en entornos de aprendizaje iVR utilizando eye-tracking y aprendizaje automático

• Ana Serrano-Mamolar ^[1] ; Ines Miguel-Alonso ^[1] ; David Checa ^[1] ; Carlos Pardo-Aguilar ^[1]
  1. [1] Universidad de Burgos

     Universidad de Burgos

     Burgos, España

     
• Localización: Comunicar: Revista Científica de Comunicación y Educación, ISSN 1134-3478, Nº 76, 2023 (Ejemplar dedicado a: Neurotecnología en el aula: Investigación actual y futuro potencial), págs. 9-20
• Idioma: español
• DOI: 10.3916/C76-2023-01
• Títulos paralelos:
  • Towards learner performance evaluation in iVR learning environments using eye-tracking and Machine-learning
• Enlaces
  • Texto completo (pdf)
• Resumen
  • español

    Actualmente, el uso de los datos del seguimiento de la mirada en entornos de aprendizaje de Realidad Virtual inmersiva (iVR) está destinado a ser una herramienta fundamental para maximizar los resultados de aprendizaje, dada la naturaleza poco intrusiva del eye-tracking y su integración en las gafas comerciales de Realidad Virtual. Pero, antes de que se pueda generalizar el uso del eye-tracking en entornos de aprendizaje, se deben identificar las tecnologías más adecuadas para el procesamiento de datos. Esta investigación propone el uso de técnicas de aprendizaje automático para este fin, evaluando sus capacidades para clasificar la calidad del entorno de aprendizaje y predecir el rendimiento de aprendizaje del usuario. Para ello, se ha desarrollado una experiencia docente en iVR para aprender el manejo de un puente-grúa. Con esta experiencia se ha evaluado el rendimiento de 63 estudiantes, tanto en condiciones óptimas de aprendizaje como en condiciones con factores estresores. El conjunto de datos final incluye 25 características, siendo la mayoría series temporales con un tamaño de conjunto de datos superior a 50 millones de puntos. Los resultados muestran que la aplicación de diferentes clasificadores como KNN, SVM o Random Forest tienen una alta precisión a la hora de predecir alteraciones en el aprendizaje, mientras que la predicción del rendimiento del aprendizaje del usuario aún está lejos de ser óptima, lo que abre una nueva línea de investigación futura. Este estudio tiene como objetivo servir como línea de base para futuras mejoras en la precisión de los modelos mediante el uso de técnicas de aprendizaje automático más complejas.

  • English

    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.

• Referencias bibliográficas
  • Añaños-Carrasco, E. (2015). Eyetracker technology in elderly people: How integrated television content is paid attention to and processed....
  • Asish, S.M., Kulshreshth, A.K., & Borst, C.W. (2022). Detecting distracted students in educational VR environments using machine learning...
  • 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
  • Checa, D., & Bustillo, A. (2020). A review of immersive virtual reality serious games to enhance learning and training. Multimedia Tools...
  • Checa, D., & Bustillo, A. (2022). Grua Rv. http://3dubu.Es/En/Cranevr/
  • Checa, D., Gatto, C., Cisternino, D., De Paolis, L.T., & Bustillo, A. (2020). A Framework for Educational and Training Immersive Virtual...
  • Christ, M., Braun, N., Neuffer, J., & Kempa-Liehr, A.W. (2018). Time series feature extraction on basis of scalable hypothesis tests (tsfresh...
  • Christ, M., Kempa-Liehr, A., & Feindt, M. (2016). Distributed and parallel time series feature extraction for industrial big data applications....
  • 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...
  • Dale, E. (1946). Audiovisual methods in teaching. Dryden Press. https://bit.ly/42aW03X
  • Dalgarno, B., & Lee, M.J.W. (2010). What are the learning affordances of 3-D virtual environments? British Journal of Educational Technology,...
  • Dede, C. (2009). Immersive interfaces for engagement and learning. Science, 323(5910), 66-69. https://doi.org/10.1126/science.1167311
  • Deng, Q., Wang, J., Hillebrand, K., Benjamin, C.R., & Soffker, D. (2020). Prediction performance of lane changing behaviors: A study of...
  • Duchowski, A.T. (2002). A breadth-first survey of eye-tracking applications. Behavior Research Methods, Instruments, & Computers, 34(4),...
  • Farran, E., Formby, S., Daniyal, F., Holmes, T., & Herwegen, J. (2016). Route-learning strategies in typical and atypical development;...
  • García-Carrasco, J., Hernández-Serrano, M.J., & Martín-García, A.V. (2015). Plasticity as a framing concept enabling transdisciplinary...
  • Gardony, A.L., Lindeman, R.W., & Brunyé, T.T. (2020). Eye-tracking for human-centered mixed reality: Promises and challenges. Proc.SPIE,...
  • Glennon, J.M., Souza, H., Mason, L., Karmiloff-Smith, A., & Thomas, M.S.C. (2020). Visuo-attentional correlates of Autism Spectrum Disorder...
  • Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. (2008). The WEKA data mining software: An update. SIGKDD...
  • Huang, H.M., Rauch, U., & Liaw, S.S. (2010). Investigating learners’ attitudes toward virtual reality learning environments: Based on...
  • Lapborisuth, P., Koorathota, S., Wang, Q., & Sajda, P. (2021). Integrating neural and ocular attention reorienting signals in virtual...
  • Ma, X., Yao, Z., Wang, Y., Pei, W., & Chen, H. (2018). Combining brain-computer interface and eye-tracking for high-speed text entry in...
  • Martinez, K., Menéndez-Menéndez, M.I., & Bustillo, A. (2021). Awareness, prevention, detection, and therapy applications for depression...
  • Mckinney, W. (2011). pandas: A foundational Python library for data analysis and statistics. Python High Performance Science Computer.
  • Patney, A., Salvi, M., Kim, J., Kaplanyan, A., Wyman, C., Benty, N., Luebke, D., & Lefohn, A. (2016). Towards foveated rendering for gaze-tracked...
  • Pritchard, A. (2017). Ways of learning: Learning theories for the classroom. Routledge. https://doi.org/10.4324/9781315460611
  • Rappa, N.A., Ledger, S., Teo, T., Wong, K.W., Power, B., & Hilliard, B. (2022). The use of eye-tracking technology to explore learning...
  • Rodero, E., & Larrea, O. (2022). Virtual reality with distractors to overcome public speaking anxiety in university students; [Realidad...
  • Shadiev, R., & Li, D. (2022). A review study on eye-tracking technology usage in immersive virtual reality learning environments. Computers...
  • Sun, Q., Patney, A., Wei, L.Y., Shapira, O., Lu, J., Asente, P., Zhu, S., Mcguire, M., Luebke, D., & Kaufman, A. (2018). Towards virtual...
  • Tanaka, Y., Kanari, K., & Sato, M. (2021). Interaction with virtual objects through eye-tracking. In International Workshop on Advanced...
  • Tavenard, R., Faouzi, J., Vandewiele, G., Divo, F., Androz, G., Holtz, C., Payne, M., Yurchak, R., Rußwurm, M., Kolar, K., & Woods, E....
  • Wilson, M. (2002). Six views of embodied cognition. Psychonomic Bulletin and Review, 9, 625-636. https://doi.org/10.3758/BF03196322
  • Wismer, P., Soares, S.A., Einarson, K.A., & Sommer, M.O.A. (2022). Laboratory performance prediction using virtual reality behaviometrics....