How attention level and cognitive style affect learning in a MOOC environment? Based on the perspective of brainwave analysis

Jung Jung Chang, Wen Shen Lin, Hong Ren Chen

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)

Abstract

The massive open online courses (MOOCs) enable learners to learn over distances. These courses also attract attentions in higher education. However, classroom virtualization generates problems, creates distraction for students. MOOCs have provided an individualized learning opportunity in the sense that the cognitive style affects learning. This study aims at exploring the differences of students’ attention levels and learning effectiveness perceived affected by different cognitive styles within a MOOC learning environment. Brainwave detection equipment was used to carry out a teaching experiment among junior university students who were enrolled in a course about the Internet of Things. Results revealed that: (1) learners who have independent verbal, independent imagery, and dependent imagery types of cognitive styles, learning effectiveness are significantly better in the MOOC learning environment compared to traditional PowerPoint-based teaching; (2) in the MOOC learning environment, learners who have dependent verbal type of cognitive style would have significantly better attentional levels than those who have the dependent imagery type of cognitive style; (3) attention levels among learners who have the independent verbal type of cognitive style were highly positively correlated with learning effectiveness. It also implies that learners who have the independent verbal type of cognitive style benefit greatly to learn in the MOOC environment. For those learners who have the dependent verbal type of cognitive style are the last to benefit to learn in the MOOC teaching environment.

Original languageEnglish
Pages (from-to)209-217
Number of pages9
JournalComputers in Human Behavior
Volume100
DOIs
Publication statusPublished - 2019 Nov

All Science Journal Classification (ASJC) codes

  • Arts and Humanities (miscellaneous)
  • Human-Computer Interaction
  • Psychology(all)

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