Cognitive and affective brain-computer interfaces for improving learning strategies and enhancing student capabilities: A systematic literature review

Nuraini Jamil, Abdelkader Nasreddine Belkacem, Sofia Ouhbi, Christoph Guger

Research output: Contribution to journalReview articlepeer-review

5 Citations (Scopus)

Abstract

Brain-computer interface (BCI) technology has the potential to positively contribute to the educational learning environment, which faces many challenges and shortcomings. Cognitive and affective BCIs can offer a deep understanding of brain mechanisms, which may improve learning strategies and increase brain-based skills. They can offer a better empirical foundation for teaching-learning methodologies, including adjusting learning content based on brain workload, measuring student interest of a topic, or even helping students focus on specific tasks. The latest findings from emerging BCI technology, neuroscience, cognitive sciences, and psychology could be used in learning and teaching strategies to improve student abilities in education. This study investigates and analyzes the research on BCI patterns and its implementation for enhancing cognitive capabilities of students. The results showed that there is insufficient literature on BCI that addresses students with disabilities in the learning process. Further, our analysis revealed a bias toward the significance of cognitive process factors compared with other influential factors, such as the learning environment and emotions that influence learning. Finally, we concluded that BCI technology could improve students' learning and cognitive skills - when consistently associated with the different pedagogical teaching-learning strategies - for better academic achievement.

Original languageEnglish
Pages (from-to)134122-134147
Number of pages26
JournalIEEE Access
Volume9
DOIs
Publication statusPublished - 2021

Keywords

  • 21st century abilities
  • Applications in subject areas
  • Brain computer interface
  • Improving classroom teaching
  • Neurofeedback
  • Teaching/learning strategies

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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