This paper is in the following e-collection/theme issue:

Serious Games for Education (137) e-Learning and Medical Education (645) New Methods (902) Machine Learning (1373) Evaluation of Medical Education (61)

Published on in Vol 9, No 1 (2021): Jan-Mar

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/21620, first published .
Pupillary Responses for Cognitive Load Measurement to Classify Difficulty Levels in an Educational Video Game: Empirical Study

Pupillary Responses for Cognitive Load Measurement to Classify Difficulty Levels in an Educational Video Game: Empirical Study

Pupillary Responses for Cognitive Load Measurement to Classify Difficulty Levels in an Educational Video Game: Empirical Study

Authors of this article:

Hugo Mitre-Hernandez1 Author Orcid Image ;   Roberto Covarrubias Carrillo1 Author Orcid Image ;   Carlos Lara-Alvarez1, 2 Author Orcid Image
Article Authors Cited by (8) Tweetations (3) Metrics

Journals

  1. Fanourakis M, Chanel G. Attenuation of the dynamic pupil light response during screen viewing for arousal assessment. Frontiers in Virtual Reality 2022;3 View
  2. Issever D, Catalbas M, Duran F. Examining Factors Influencing Cognitive Load of Computer Programmers. Brain Sciences 2023;13(8):1132 View
  3. Cai Y, Strauch C, Van der Stigchel S, Naber M. Open-DPSM: An open-source toolkit for modeling pupil size changes to dynamic visual inputs. Behavior Research Methods 2023 View
  4. Gorin H, Patel J, Qiu Q, Merians A, Adamovich S, Fluet G. A Review of the Use of Gaze and Pupil Metrics to Assess Mental Workload in Gamified and Simulated Sensorimotor Tasks. Sensors 2024;24(6):1759 View
  5. Zhao Y, Li Y, Jiang A, Zhang H, She H, Zhan W. Effects of Visual and Auditory Instructions on Space Station Procedural Tasks. Space: Science & Technology 2024;4 View

Books/Policy Documents

  1. Knox A. Reimagining Education: Studies and Stories for Effective Learning in an Evolving Digital Environment. View