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

  1. Ana Serrano-Mamolar 1
  2. Ines Miguel-Alonso 1
  3. David Checa 1
  4. Carlos Pardo-Aguilar 1
  1. 1 Universidad de Burgos
    info

    Universidad de Burgos

    Burgos, España

    ROR https://ror.org/049da5t36

Journal:
Comunicar: Revista Científica de Comunicación y Educación

ISSN: 1134-3478

Year of publication: 2023

Issue Title: Neurotecnología en el aula: Investigación actual y futuro potencial

Issue: 76

Pages: 9-20

Type: Article

DOI: 10.3916/C76-2023-01 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

More publications in: Comunicar: Revista Científica de Comunicación y Educación

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.

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Data source: Dialnet