A hybrid biological data analysis approach for students' learning creative characteristics recognition

Shih Yeh Chen, Chin Feng Lai, Chi Cheng Chang

Research output: Contribution to journalArticle


Regarding the effectiveness evaluation of students' creativity learning, most of the past studies have proposed teaching strategies to improve the creativity of students. For example, teachers use traditional questionnaires to evalute students' creativity learning effectiveness after implementing teaching strategies. However, most traditional questionnaires lack automated methods to provide immediate feedback to help teachers understand the current learning status of students instantly. Based on the above problem description, a hybrid biological data analysis approach is proposed that the teachers can analysis the students' learning status in the learning process through the wearable biological monitoring devices. Hybrid biological data such as the degree of course participation and creativity growth of the students is collected to be analyzed for recognizing students' learning creative characteristics. In the experiment results, we observed that the changes in brainwave values and heartbeats echoed the students' creativity status.

Original languageEnglish
Article number8831391
Pages (from-to)134411-134421
Number of pages11
JournalIEEE Access
Publication statusPublished - 2019 Jan 1



  • Biological data analysis
  • Characteristics recognition
  • Learning creative

ASJC Scopus subject areas

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

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