The effect of the “Prediction-observation-quiz-explanation” inquiry-based e-learning model on flow experience in green energy learning
ARTICLE
Jon-Chao Hong, Chi-Ruei Tsai, Hsien-Sheng Hsiao, Po-Hsi Chen, Kuan-Cheng Chu, Institute for Research Excellence in Learning Sciences, Taiwan ; Jianjun Gu, School of Education Science ; Jirarat Sitthiworachart, 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 March 28, 2024 from https://www.learntechlib.org/p/208165/.
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.2019.01.009Keywords
References
View References & Citations Map- Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), pp. 191-215. Available online: http://www.ncbi.nlm.nih.gov/%20pubmed/847061.
- Bandura, A. (1981). Self-referent thought: A developmental analysis of self-efficacy. Social cognitive development frontiers and possible futures, pp. 200-239. Cambridge, England: Cambridge University Press.
- Bandura, A. (1997). Self-efficacy: The exercise of control. New York: W. H. Freeman and Company.
- Benson, P. (2007). Autonomy in language teaching and learning. Language Teaching, 40(1), pp. 21-40.
- Bhandari, A., & Duncan, J. (2014). Goal neglect and knowledge chunking in the construction of novel behaviour. Cognition, 130(1), pp. 11-30. Available online: https://doi.org/10.1016/j.%20cognition.2013.08.013.
- Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), pp. 351-370. Available online: https://doi.org/10.2307/3250921.
- Bhattacherjee, A., Perols, J., & Sanford, C. (2008). Information technology continuance: A theoretical extension and empirical test. Journal of Computer Information Systems, 49(1), pp. 17-26.
- Blackwell, L.S., Trzesniewski, K.H., & Dweck, C.S. (2007). Implicit theories of intelligence predict achievement across an adolescent transition: A longitudinal study and an intervention. Child Development, 78(1), pp. 246-263.
- Blunt, J.R., & Karpicke, J.D. (2014). Learning with retrieval-based concept mapping. Journal of Educational Psychology, 106(3), pp. 849-858.
- Bourelle, A., Bourelle, T., Knutson, A.V., & Spong, S. (2016). Sites of multimodal literacy: Comparing student learning in online and face-to-face environments. Computers and Composition, 39, pp. 55-70. Available online: https://doi.org/10.1016/%20j.compcom.2015.11.003.
- Britner, S.L., & Pajares, F. (2006). Sources of self-efficacy of middle school science students. Journal of Research in Science Teaching, 43(5), pp. 485-499. Available online: https://doi.org/10.%201002/tea.20131.
- Bybee, R., Taylor, J.A., Gardner, A., van Scotter, P., Carlson, J., & Westbrook, A. (2006). The BSCS 5E instructional model: Origins and effectiveness. Colorado Springs, CO: BSCS.
- Byrne, B.M. (2001). Structural equation modeling with AMOS: Basic concepts, applications, and programming. Hillsdale, NJ: Lawrence Erlbaum Associates.
- Chang, C.C., Liang, C., Chou, P.N., & Lin, G.Y. (2017). Is game-based learning better in flow experience and various types of cognitive load than non-game-based learning? Perspective from multimedia and media richness. Computers in Human Behavior, 71, pp. 218-227.
- Chiu, C.Y., Hong, Y.Y., & Dweck, C.S. (1997). Lay dispositionism and implicit theories of personality. Journal of Personality and Social Psychology, 73(1), p. 19.
- Christopher, M. (2000). The agile supply chain: Competing in volatile markets. Industrial Marketing Management, 29(1), pp. 37-44.
- Clark, R.C., & Mayer, R.E. (2008). E-learning and the science of instruction: Proven guidelines for consumers and designers of multimedia learning. NY: John Wiley & Sons.
- Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience. New York: Harper Perennial.
- Csikszentmihalyi, M. (1998). Finding flow: The psychology of engagement with everyday life. New York: Basic Books.
- Csikszentmihalyi, M. (2014). Applications of flow in human development and education: The collected work of Mihaly Csikszentmihalyi. Dordrecht: Springer.
- Dai, T., & Cromley, J.G. (2014). Changes in implicit theories of ability in biology and dropout from STEM majors: A latent growth curve approach. Contemporary Educational Psychology, 39(3), pp. 233-247.
- Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), pp. 319-340. Available online: https://doi.org/%2010.2307/249008.
- Dağhan, G., & Akkoyunlu, B. (2016). Modeling the continuance usage intention of online learning environments. Computers in Human Behavior, 60, pp. 198-211.
- Deci, E.L. (1975). Intrinsic motivation. New York: Plenum.
- Deci, E.L., & Ryan, R.M. (1985). Intrinsic motivation and self-determination in human behavior. New York: Plenum.
- Deci, E.L., & Ryan, R.M. (2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11, pp. 227-268.
- Deci, E.L., & Ryan, R.M. (2008). Facilitating optimal motivation and psychological well-being across life's domains. Canadian Psychology, 49, pp. 14-23.
- Doll, W.J., Xia, W., & Torkzadeh, G. (1994). A confirmatory factor analysis of the user computing satisfaction instrument. MIS Quarterly, 18(4), pp. 453-461.
- Duncan, J., Parr, A., Woolgar, A., Thompson, R., Bright, P., & Cox, S. (2008). Goal neglect and Spearman's g: Competing parts of a complex task. Journal of Experimental Psychology: General, 137(1), pp. 131-148.
- Dweck, C.S. (1986). Motivational processes affecting learning. American Psychologist, 41, pp. 1040-1048.
- Dweck, C.S. (2000). Self-theories: Their role in motivation, personality and development. Philadelphia, PA: Psychology Press.
- Dweck, C.S. (2012). Mindset: How you can fulfill your potential.
- Dweck, C.S., & Bempechat, J. (1983). Children's theories of intelligence: Consequences for learning. Learning and motivation in the classroom Hillsdale, NJ: Erlbaum.
- Dweck, C.S., & Leggett, E.L. (1988). A social-cognitive approach to motivation and personality. Psychological Review, 95(2), pp. 256-273.
- Dweck, C.S., & Molden, D.C. (2005). Self-theories: Their impact on competence motivation and acquisition. Handbook of competence and motivation, pp. 122-140. New York City: Guilford Press.
- Fay, B. (1996). Contemporary philosophy of social science: A multicultural approach. Contemporary philosophy of social science: A multicultural approach, Vol. 1 New York: Cambridge University Press.
- Feltz, D.L. (2007). Self-confidence and sport performance. Essential readings in sport and exercise psychology Champaign, IL: Human Kinetics.
- Feltz, D.L., Short, S., & Sullivan, P. (2008). Self-efficacy in sport: Research and strategies for working with athletes, teams, and coaches. Champaign, IL: Human Kinetics.
- Fornell, C., & Larcker, D.F. (1981). Structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), pp. 39-50.
- Garcia-Sanjuan, F., Jurdi, S., Jaen, J., & Nacher, V. (2018). Evaluating a tactile and a tangible multi-tablet gamified quiz system for collaborative learning in primary education. Computers & Education, 123, pp. 65-84.
- Guo, Z., Xiao, L., Van Toorn, C., Lai, Y., & Seo, C. (2016). Promoting online learners' continuance intention: An integrated flow framework. Information & Management, 53, pp. 279-295.
- Hair, J.F., Black, W.C., Babin, B.J., & Anderson, R.E. (2010). Multivariate data analysis. Upper Saddle River, NJ: Prentice Hall.
- Hew, K.F., & Cheung, W.S. (2014). Students' and instructors' use of massive open online courses (MOOCs): Motivations and challenges. Educational Research Review, 12, pp. 45-58. Available online: https://doi.org/10.1016/j.edurev.2014.05.001.
- Hoffman, D., & Novak, T.P. (2009). Flow online: Lessons learned and future prospects. Journal of Interactive Marketing, 23(1), pp. 23-34.
- Hong, J.C., Hwang, M.Y., Liu, M.C., Ho, H.Y., & Chen, Y.L. (2014). Using a "prediction-observation- explanation" inquiry model to enhance student interest and intention to continue science learning predicted by their internet cognitive failure. Computers & Education, 72, pp. 110-120.
- Hranstinski, S. (2009). A theory of online learning as online participation. Computers & Education, 52(1), pp. 78-82. Available online: https://doi.org/10.1016/j.compedu.2008.06.009.
- Hu, L., & Bentler, P.M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), pp. 1-55.
- Justice, C., Warry, W., Cuneo, C.L., Inglis, S., Miller, S., & Rice, J. (2002). A grammar for inquiry: Linking goals and methods in a collaboratively taught social sciences inquiry course. The Alan Blizzard Award paper: The award winning papers Windsor: Special Publication of the Society for Teaching and Learning in Higher Education and McGraw-Hill Ryerson.
- Kalyuga, S. (2011). Cognitive load theory: How many types of load does it really need?. Educational Psychology Review, 23, pp. 1-19. Available online: https://doi.org/10.1007/s10648-010-%209150-7.
- Kalyuga, S., Chandler, P., & Sweller, J. (2000). Incorporating learner experience into the design of multimedia instruction. Journal of Educational Psychology, 92(1), p. 126. Available online: https://doi.org/10.1037//0022-0663.92.1.126.
- Kiili, K. (2005). Digital game-based learning: Towards an experiential gaming model. Internet and Higher Education, 8(1), pp. 13-24.
- Kiili, K., de Freitas, S.D., Arnab, S., & Lainema, T. (2012). The design principles for flow experience in educational games. Procedia Computer Science, 15, pp. 78-91.
- Kim, M.K. (2015). Models of learning progress in solving complex problems: Expertise development in teaching and learning. Contemporary Educational Psychology, 42, pp. 1-16.
- Kim, B., & Kim, J. (2005). Path analysis of flow states variables effect in educational computer games on learning. The Journal of Educational Information and Media, 11, pp. 89-114.
- Komarraju, M., & Nadler, D. (2013). Self-efficacy and academic achievement: Why do implicit beliefs, goals, and effort regulation matter?. Learning and Individual Differences, 25, pp. 67-72. Available online: https://doi.org/10.1016/j.lindif.2013.01.005.
- Kuhnle, C., & Sinclair, M. (2011). Decision mode as an antecedent of flow, motivational interference, and regret. Learning and Individual Differences, 21, pp. 239-243.
- Land, S.M., Zimmerman, H.T., Murray, O.T., Hooper, S., Yeh, K.C., & Sharma, P. (2011). Mobile computing: Perspectives on design, learning, and development. Jacksonville, FL: 2011 AECT International Convention.
- Lauren, N., Fielding, K.,S., Smith, L., & Louis, W.R. (2016). You did, so you can and you will: Self-efficacy as a mediator of spillover from easy to more difficult pro-environmental behavior. Journal of Environmental Psychology, 48, pp. 191-199.
- Liao, L.-F. (2006). A flow theory perspective on learner motivation and behavior in distance education. Distance Education, 27, pp. 45-62.
- Lin, T.J., Liang, J.C., & Tsai, C.C. (2015). Identifying Taiwanese university students' physics learning profiles and their role in physics learning self-efficacy. Research in Science Education, 45(4), pp. 605-624. Available online: https://doi.org/10.1007/s11165-014-9440-z.
- Lin, T.C., Liang, J.C., & Tsai, C.C. (2015). Conceptions of memorizing and understanding in learning, and self-efficacy held by university biology majors. International Journal of Science Education, 37(3), pp. 446-468. Available online: https://doi.org/10.1080/09500693.2014.99205.
- Lin, T.-J., & Tsai, C.-C. (2013). A multi-dimensional instrument for evaluating Taiwanese high school students' science learning self-efficacy in relation to their approaches to learning science. International Journal of Science and Mathematics Education, 11(6), pp. 1275-1301. Available online: https://doi.org/10.1007/s10763-012-9376-6.
- MacCallum, R.C., & Hong, S. (1997). Power analysis in covariance structure modeling using GFI and AGFI. Multivariate Behavioral Research, 32(2), pp. 193-210.
- Mahdi Hosseini, S., & Fattahi, R. (2014). Databases' interface interactivity and user self-efficacy: Two mediators for flow experience and scientific behavior improvement. Computers in Human Behavior, 36, pp. 316-322.
- Mandernach, B.J., Gonzales, R.M., & Garnett, A.L. (2006). An examination of online instructor presence via threaded discussion participation. Journal of Online Learning and Teaching, 2(4), pp. 248-260. Available online: http://jolt.merlot.org/%20index.htm.
- Martı´nez-Plumed, F., Ferri, C., Herna´ndez-Orallo, J., & Ramı´rez-Quintana, M.J. (2017). A computational analysis of general intelligence tests for evaluating cognitive development. Cognitive Systems Research, 43, pp. 100-118.
- Masters, K., & Oberprieler, G. (2004). Encouraging equitable online participation through curriculum articulation. Computers & Education, 42(4), pp. 319-332. Available online: https://doi.org/10.1016/j.compedu.2003.09.001.
- Mayer, R.E. (2005). Cognitive theory of multimedia learning. The Cambridge handbook of multimedia learning, pp. 31-48. New York: Cambridge University Press.
- Mayer, R.E. (2009). Multimedia learning. New York: Cambridge University Press.
- Mazur, E. (2009). Education farewell, lecture?. Science, 323(5910), pp. 50-51.
- Moneta, G.B. (2004). The flow experience across cultures. Journal of Happiness Studies, 5, pp. 115-121.
- Moreno, R. (2006). Does the modality principle hold for different media? A test of the method-affects-learning hypothesis. Journal of Computer Assisted Learning, 22, pp. 149-158. Available online: https://doi.org/10.1111/j.1365-2729.2006.00170.x.
- Moreno, R., & Mayer, R. (2007). Interactive multimodal learning environments: Special issue on interactive learning environments: Contemporary issues and trends. Educational Psychology Review, 19(3), pp. 309-326.
- Mulaik, S.A., James, L.R., Van Alstine, J., Bennet, N., Lind, S., & Stilwell, C.D. (1989). Evaluation of goodness-of-fit indices for structural equation models. Psychological Bulletin, 105(3), pp. 430-445.
- Nunnally, J. (1978). Psychometric theory. New York: McGraw-Hill.
- Paas, F.G. (1992). Training strategies for attaining transfer of problem-solving skill in statistics: A cognitive-load approach. Journal of Educational Psychology, 84(4), p. 429. Available online: https://doi.org/10.1037/0022-0663.84.4.429.
- Paas, F.G.W.C., & van Merriënboer, J.J.G. (1993). The efficiency of instructional conditions: An approach to combine mental effort and performance measures. Human Factors: The Journal of the Human Factors and Ergonomics Society, pp. 737-743. Available online: https://doi.org/10.1177/001872089303500412.
- Palmer, S. (2000). Enquiry-based learning can maximize a student's potential. Psychology Learning and Teaching, 2(2), pp. 82-86.
- Park, B., Plass, J.L., & Brünken, R. (2014). Cognitive and affective processes in multimedia learning. Learning and Instruction, 29, pp. 125-127.
- Pedaste, M., Mäeots, M., Siiman, L.A., de Jong, T., van Riesen, S.A.N., & Kamp, E.T. (2015). Phases of inquiry-based learning: Definitions and the inquiry cycle. Educational Research Review, 14, pp. 47-61.
- Peleta, J.É., Ettisb, S., & Cowart, K. (2017). Optimal experience of flow enhanced by telepresence: Evidence from social media use. Information & Management, 54, pp. 115-128.
- Refsgaard, J.C., & Henriksen, H.J. (2004). Modelling guidelines––terminology and guiding principles. Advances in Water Resources, 27(1), pp. 71-82.
- Reychav, I., & Wu, D. (2016). The interplay between cognitive task complexity and user interaction in mobile collaborative training. Computers in Human Behavior, 62, pp. 333-345.
- Rogers, Y., Connelly, K., Hazlewood, W., & Tedesco, L. (2010). Enhancing learning: A study of how mobile devices can facilitate sense-making. Personal and Ubiquitous Computing, 14, pp. 111-124.
- Schmeck, A., Opfermann, M., van Gog, T., Paas, F., & Leutner, D. (2015). Measuring cognitive load with subjective rating scales during problem solving: Differences between immediate and delayed ratings. Instructional Science, 43(1), pp. 93-114. Available online: https://doi.org/10.1007/s11251-014-9328-3.
- Schmidt, A., & Braun, S. (2006). Context-aware workplace learning support: Concept, experiences, and remaining challenges. Innovative approaches for learning and knowledge sharing, pp. 518-524. Berlin/Heidelberg: Springer.
- Sheldon, K.M., & Filak, V. (2008). Manipulating autonomy, competence, and relatedness in a game-learning context: New evidence that all three needs matter. British Journal of Social Psychology, 47, pp. 267-283.
- Shively, R.L., & Ryan, C.S. (2013). Longitudinal changes in college math students' implicit theories of intelligence. Social Psychology of Education, 16(2), pp. 241-256.
- Siemens, G. (2013). Massive open online courses: Innovation in education. Open educational resources: Innovation, research and practice, pp. 5-15. Vancouver, Canada: Commonwealth of learning.
- Silver, W.S., Mitchell, T.T., & Gist, M.E. (1995). Responses to successful and unsuccessful performance: The moderating effect of self-efficacy on the relationship between performance and attributions. Organizational Behavior and Human Decision Processes, 62, pp. 286-299. Available online: https://doi.org/10.1006/%20obhd.1995.1051.
- Skuballa, I.T., Dammert, A., & Renkl, A. (2018). Two kinds of meaningful multimedia learning: Is cognitive activity alone as good as combined behavioral and cognitive activity?. Learning and Instruction, 54, pp. 35-46.
- Suáreza, Á., Spechta, M., Prinsenb, F., Kalza, M., & Ternie, S. (2018). A review of the types of mobile activities in mobile inquiry-based learning. Computers & Education, 118, pp. 38-55.
- Sung, Y.T., Chang, K.E., & Liu, T.C. (2016). The effects of integrating mobile devices with teaching and learning on students learning performance: A meta-analysis and research synthesis. Computers & Education, 94, pp. 252-275.
- Sweller, J. (2005). Implications of cognitive load theory for multimedia learning. The Cambridge handbook of multimedia learning, pp. 19-29. New York, NY: Cambridge University Press.
- Sweller, J. (2015). In academe, what is learned and how is it learned?. Current Directions in Psychological Science, 24, pp. 190-194.
- Sweller, J. (2016). Cognitive load theory, evolutionary educational psychology, and instructional design. Evolutionary perspectives on child development and education, p. 291e306. Switzerland: Springer.
- Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive load theory. New York: Springe.
- Tobert, S., & Moneta, G.B. (2013). Flow as a function of affect and coping in the workplace. Individual Differences Research, 11, pp. 102-113.
- Tsai, C.C., Ho, H.N.J., Liang, J.C., & Lin, H.M. (2011). Scientific epistemic beliefs, conceptions of learning science and self-efficacy of learning science among high school students. Learning and Instruction, 21, pp. 757-769.
- Tsai, Y.H., Lin, C.H., Hong, J.C., & Tai, K.H. (2018). The effects of metacognition on online learning interest and continuance to learn with MOOCs. Computers & Education Available online: https://doi.org/10.1016/j.compedu.2018.02.011.
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