The effect of the “Prediction-observation-quiz-explanation” inquiry-based e-learning model on flow experience in green energy learning

Jon Chao Hong, Chi Ruei Tsai*, Hsien Sheng Hsiao, Po Hsi Chen, Kuan Cheng Chu, Jianjun Gu, Jirarat Sitthiworachart

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

44 Citations (Scopus)


There are several models of inquiry-based learning, some of which may be practiced in experimental classroom teaching, and some of which may be used in online teaching. This study proposes the “prediction-observation-quiz-explanation” (POQE) model to design a green energy generation learning program (i.e., how solar, wind, and water can produce energy) and to test how participants' cognitive-affective factors affect their interest in using this model. A total of 396 technical high school students participated in this experimental study, and 375 valid data were collected and subjected to confirmatory factor analysis with structural equation modelling. The results indicated that incremental belief of intelligence was negatively related to cognitive load, but positively related to green energy learning self-efficacy (GELSE) in practicing POQE. Cognitive load was negatively related to flow experience, while GELSE was positively related to flow experience. Finally, flow experience was positively related to the intention to continue online learning with the POQE model. The implications of this study are that e-learning designers can use this POQE model to develop more educational content for students to learn various concepts.

Original languageEnglish
Pages (from-to)127-138
Number of pages12
JournalComputers and Education
Publication statusPublished - 2019 May


  • e-learning
  • energy education
  • inquiry learning model
  • inquiry-based learning

ASJC Scopus subject areas

  • General Computer Science
  • Education


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