You are here:

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

, , , , , Institute for Research Excellence in Learning Sciences, Taiwan ; , School of Education Science ; , King Mongkut's Institute of Technology Ladkrabang, Thailand

Computers & Education Volume 133, Number 1, ISSN 0360-1315 Publisher: Elsevier Ltd

Abstract

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.

Citation

Hong, J.C., Tsai, C.R., Hsiao, H.S., Chen, P.H., Chu, K.C., Gu, J. & Sitthiworachart, J. (2019). The effect of the “Prediction-observation-quiz-explanation” inquiry-based e-learning model on flow experience in green energy learning. Computers & Education, 133(1), 127-138. Elsevier Ltd. Retrieved September 19, 2019 from .

This record was imported from Computers & Education on March 15, 2019. Computers & Education is a publication of Elsevier.

Full text is availabe on Science Direct: http://dx.doi.org/10.1016/j.compedu.2019.01.009

Keywords

References

View References & Citations Map

These references have been extracted automatically and may have some errors. Signed in users can suggest corrections to these mistakes.

Suggest Corrections to References