Metacognition in covariation reasoning relevant to performance achievement mediated by experiential values in a simulation game

Abstract

The Chinese proverb “heal a headache by curing the head and heal foot pain by curing the feet” alludes to ineffective work resulting from a lack of covariation in reasoning. Actually, much problem solving relies on the analysis of how two or more factors vary in correlation with another related variant (i.e., covariation reasoning). To further investigate covariation reasoning and its related affective factors, we designed a website called “No Good (NG) Bread” for senior vocational high school students in Taipei who had taken baking courses for 1 year to apply their knowledge in solving baking-related problems. Data collected from 113 participants aged 16 to 17 were validated by confirmatory factor analysis using Visual PLS 1.04 to examine the interrelatedness among metacognition, experiential values, and performance achievement. The examination revealed that metacognition was positively related to hedonic and utilitarian experiential values, which were subsequently positively related to the students’ learning achievements. The results imply that websites can be developed for specialized courses, such as nursing or automobile repair, to develop students’ covariation reasoning for more effective problem solving.

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References

  1. Abernethy, B., Thomas, K. T., & Thomas, J. T. (1993). Strategies for improving understanding of motor expertise [or mistakes we have made and things we have learned]. In J. L. Starkes & F. Allard (Eds.), Cognitive issues in motor expertise (pp. 317–356). Amsterdam: Elsevier.

    Google Scholar 

  2. Anderson, J. R. (1993). Problem solving and learning. American Psychologist,48, 35–44.

    Article  Google Scholar 

  3. Ayars, A. (2016). Can model-free reinforcement learning explain deontological moral judgments? Cognition,150, 232–242.

    Article  Google Scholar 

  4. Baek, Y., & Touati, A. (2017). Exploring how individual traits influence enjoyment in a mobile learning game. Computers in Human Behavior,69, 347–357.

    Article  Google Scholar 

  5. Blanton, M., Brizuela, B. M., Gardiner, A. M., Sawrey, K., & Newman-Owens, A. (2015). A learning trajectory in 6-year-olds’ thinking about generalizing functional relationships. Journal for Research in Mathematics Education,46(5), 511–558.

    Article  Google Scholar 

  6. Cárdenas-Robledo, L. A., & Peña-Ayala, A. (2018). Ubiquitous learning: A systematic review. Telematics and Informatics,35, 1097–1132.

    Article  Google Scholar 

  7. Carlson, M. P., Jacobs, S., Coe, E., Larsen, S., & Hsu, E. (2002). Applying covariational reasoning while modeling dynamic events: A framework and a study. Journal for Research in Mathematics Education,33(5), 352–378.

    Article  Google Scholar 

  8. Catena, A., Maldonado, A., Perales, J. C., & Cándido, A. (2008). Interaction between previous beliefs and cue predictive value in covariation-based causal induction. Acta Psychologica,128(2), 339–349.

    Article  Google Scholar 

  9. Cheung, M. Y., Luo, C., Sia, C. L., & Chen, H. (2009). Credibility of electronic word-of-mouth: Informational and normative determinants of on-line consumer recommendations. International Journal of Electronic Commerce,13(4), 9–38.

    Article  Google Scholar 

  10. Chin, W. W. (1998). Issues and opinion on structural equation modeling. MIS Quarterly,22(1), 7–16.

    Google Scholar 

  11. Confrey, J., & Smith, E. (1995). Splitting, covariation, and their role in the development of exponential functions. Journal for Research in Mathematics Education,26(1), 66–86.

    Article  Google Scholar 

  12. Daniels, L. M., Stupnisky, R. H., Pekrun, R., Haynes, T. L., Perry, R. P., & Newall, N. E. (2009). A longitudinal analysis of achievement goals: From affective antecedents to emotional effects and achievement outcomes. Journal of Educational Psychology,101, 948–963.

    Article  Google Scholar 

  13. Dillman, D. A., Smyth, J. D., & Christian, L. M. (2009). Internet, mail, and mixed-mode surveys: The tailored design method. Hoboken, NJ: Wiley.

    Google Scholar 

  14. Dunlosky, J., & Metcalfe, J. (2009). Metacognition. Thousand Oaks, CA: Sage.

    Google Scholar 

  15. Eccles, J. S., Wigfield, A., & Schiefele, U. (1997). Motivation to succeed. In W. Damon & N. Eisenberg (Eds.), Handbook of child psychology (5th ed., pp. 1017–1095). New York: Wiley.

    Google Scholar 

  16. Efklides, A. (2011). Interactions of metacognition with motivation and affect in self-regulated learning: The MASRL model. Educational Psychologist,46, 6–25.

    Article  Google Scholar 

  17. Etemad-Sajadi, R., & Ghachem, L. (2015). The impact of hedonic and utilitarian value of online avatars on e-service quality. Computers in Human Behavior,52, 81–86.

    Article  Google Scholar 

  18. Fang, Y. H., & Chiu, C. M. (2010). In justice we trust: Exploring knowledge-sharing continuance intentions in virtual communities of practice. Computers in Human Behavior,26, 235–246.

    Article  Google Scholar 

  19. Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive-developmental inquiry. American Psychologist,34(10), 906–911.

    Article  Google Scholar 

  20. Gardner, A. K., Jabbour, I. J., Williams, B. H., & Huerta, S. (2016). Different goals, different pathways: The role of metacognition and task engagement in surgical skill acquisition. Journal of Surgical Education,73(1), 61–65.

    Article  Google Scholar 

  21. Garrett, J. J. (2010). The elements of user experience: User-centered design for the Web and beyond (2nd ed.). Indianapolis, IN: New Riders.

    Google Scholar 

  22. Garrison, D. R., & Akyol, Z. (2013). Toward the development of a metacognition construct for communities of inquiry. Internet and Higher Education,17, 84–89.

    Article  Google Scholar 

  23. Gauthier, A., & Jenkinson, J. (2018). Designing productively negative experiences with serious game mechanics: Qualitative analysis of game-play and game design in a randomized trial. Computers & Education,127, 66–89.

    Article  Google Scholar 

  24. Geurten, M., Meulemans, T., & Lemaire, P. (2018). From domain-specific to domain-general? The developmental path of metacognition for strategy selection. Cognitive Development,48, 62–81.

    Article  Google Scholar 

  25. Green, S. B., & Salkind, N. (2004). Using SPSS for windows and Macintosh: Analyzing and understanding data (4th ed.). Englewood Cliffs, NJ: Prentice-Hall.

    Google Scholar 

  26. Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science,40, 414–433.

    Article  Google Scholar 

  27. Hancock, G. R., & Mueller, R. O. (2013). Structural equation modeling: A second course (2nd ed.). Charlotte, NC: Information Age Publishing Inc.

    Google Scholar 

  28. Hertzog, C., & Nesselroade, J. R. (1987). Beyond autoregressive models: Some implications of the trait-state distinction for the structural modeling of developmental change. Child Development,58, 93–109.

    Article  Google Scholar 

  29. Hirschman, E. C., & Holbrook, M. B. (1982). Hedonic consumption: Emerging concepts, methods, and propositions. Journal of Marketing,46, 92–101.

    Article  Google Scholar 

  30. Holbrook, M. B. (2006). Consumption experience, customer value, and subjective personal introspection: An illustrative photographic essay. Journal of Business Research,59, 714–725.

    Article  Google Scholar 

  31. Hou, L., Chi, H. L., Tarng, W., Chai, J., Panuwatwanich, K., & Wang, X. (2017). A framework of innovative learning for skill development in complex operational tasks. Automation in Construction,83, 29–40.

    Article  Google Scholar 

  32. Hung, S. W., & Cheng, M. J. (2013). Are you ready for knowledge sharing? An empirical study of virtual communities. Computers & Education,62, 8–17.

    Article  Google Scholar 

  33. Kalyuga, S., Ayres, P., Chandler, P., & Sweller, J. (2003). The expertise reversal effect. Educational Psychologist,38, 23–31.

    Article  Google Scholar 

  34. Karle, J. W., Watter, S., & Shedden, J. M. (2010). Task switching in video game players: Benefits of selective attention but not resistance to proactive interference. Acta Psychologica,134, 70–78.

    Article  Google Scholar 

  35. Liaw, S. S., Huang, H. M., & Chen, G. D. (2007). An activity-theoretical approach to investigate learners’ factors toward e-learning systems. Computers in Human Behavior,23(4), 1906–1920.

    Article  Google Scholar 

  36. Liu, I. F., Chen, M. C., Sun, Y. S., Wibli, D., & Kuo, C. H. (2010). Extending the TAM model to explore the factors that affect intention to use an online learning community. Computers & Education,54, 600–610.

    Article  Google Scholar 

  37. López, I., & Ruiz, S. (2011). Explaining website effectiveness: The hedonic-utilitarian dual mediation hypothesis. Electronic Commerce Research and Applications,10(1), 49–58.

    Article  Google Scholar 

  38. Lu, Y., & Yang, D. (2011). Information exchange in virtual communities under extreme disaster conditions. Decision Support System,50, 529–538.

    Article  Google Scholar 

  39. Mayer, R. E. (2014). The Cambridge handbook of multimedia learning (2nd ed.). Cambridge, MA: Cambridge University Press.

    Google Scholar 

  40. Moore, K. C., Paoletti, T., & Musgrave, S. (2013). Covariational reasoning and invariance among coordinate systems. The Journal of Mathematical Behavior,32(3), 461–473.

    Article  Google Scholar 

  41. 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(3), 149–158.

    Article  Google Scholar 

  42. Norman, D. A., & Bobrow, D. G. (1975). On data-limited and resource limited processes. Cognitive Psychology,7, 44–64.

    Article  Google Scholar 

  43. Norman, E., & Furnes, B. (2016). The relationship between metacognitive experiences and learning: Is there a difference between digital and non-digital study media? Computers in Human Behavior,54, 301–309.

    Article  Google Scholar 

  44. Paoletti, T., & Moore, K. C. (2017). The parametric nature of two students’ covariational reasoning. Journal of Mathematical Behavior,48, 137–151.

    Article  Google Scholar 

  45. Pekrun, R., Elliot, A. J., & Maier, M. A. (2009). Achievement goals and achievement emotions: Testing a model of their joint relations with academic performance. Journal of Educational Psychology,101, 115–135.

    Article  Google Scholar 

  46. Pintrich, P. R., Wolters, C. A., & Baxter, G. P. (2000). Assessing metacognition and selfregulated learning. In G. Schraw & J. C. Impara (Eds.), Issues in the measurement of metacognition. Lincoln, NE: University of Nebraska-Lincoln.

    Google Scholar 

  47. Prins, F. J., Veenman, M. V. J., & Elshout, J. J. (2006). The impact of intellectual ability and metacognition on learning: New support for the threshold of problematicity theory. Learning and Instruction,16(4), 374–387.

    Article  Google Scholar 

  48. Proctor, R. W., & Capaldi, E. J. (2006). Why science matters: Understanding the methods of psychological research. Malden, MA: Blackwell Publishing.

    Google Scholar 

  49. Pu, Y. H., Wu, T. T., Chiu, P. S., & Huang, Y. M. (2016). The design and implementation of authentic learning with mobile technology in vocational nursing practice course. British Journal of Educational Technology,47(3), 494–509.

    Article  Google Scholar 

  50. Raes, A., Schellens, T., De Wever, B., & Vanderhoven, E. (2012). Scaffolding information problem solving in web-based collaborative inquiry learning. Computers & Education,59(1), 82–94.

    Article  Google Scholar 

  51. Rigby, K., & Slee, P. T. (1993). Psychoticism and attitude toward authority among pre-adolescent boys. Personality and Individual Differences,14(6), 845–847.

    Article  Google Scholar 

  52. Roebers, C. M. (2017). Executive function and metacognition: Towards a unifying framework of cognitive self-regulation. Developmental Review,45, 31–51.

    Article  Google Scholar 

  53. Schaie, K. W., Dutta, R., & Willis, S. L. (1991). Relationship between rigidity-flexibility and cognitive abilities in adulthood. Psychology and Aging,6, 371–383.

    Article  Google Scholar 

  54. Sheth, J. N., Newman, B. I., & Gross, B. L. (1991). Why we buy what we buy: A theory of consumption values. Journal of Business Research,22, 159–170.

    Article  Google Scholar 

  55. Spence, I., & Feng, J. (2010). Video games and spatial cognition. Review of General Psychology,14, 92–104.

    Article  Google Scholar 

  56. Suh, T., Bae, M., Zhao, H., Kim, S. H., & Arnold, M. J. (2010). A multi-level investigation of international marketing projects: The roles of experiential knowledge and creativity on performance. Industrial Marketing Management,39(2), 211–220.

    Article  Google Scholar 

  57. Szeto, E. (2015). Community of inquiry as an instructional approach: What effects of teaching, social and cognitive presences are there in blended synchronous learning and teaching? Computers & Education,81, 191–201.

    Article  Google Scholar 

  58. Thompson, P. W., & Carlson, M. P. (2017). Variation, covariation, and functions: Foundational ways of thinking mathematically. In J. Cai (Ed.), Compendium for research in mathematics education. Reston, VA: National Council of Teachers of Mathematics.

    Google Scholar 

  59. Tsai, Y. S., 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,121, 18–29.

    Article  Google Scholar 

  60. VanDeventer, S. S., & White, J. A. (2002). Expert behavior in children’s video game play. Simulation and Gaming,33, 28–48.

    Article  Google Scholar 

  61. Venkatesh, V., & Brown, S. A. (2001). A longitudinal investigation of personal computers in homes: Adoption determinants and emerging challenges. MIS Quarterly,25(1), 71–102.

    Article  Google Scholar 

  62. Videras, J., Owen, A. L., Conover, E., & Wu, S. (2012). The influence of social relationships on pro-environment behaviors. Journal of Environmental Economics and Management,63(1), 35–50.

    Article  Google Scholar 

  63. Voet, M., & De Wever, B. (2016). Towards a differentiated and domain-specific view of educational technology: An exploratory study of history teachers’ technology use. British Journal of Educational Technology,48(6), 1402–1413.

    Article  Google Scholar 

  64. Voss, K. E., Spangenberg, E. R., & Grohman, B. (2003). Measuring the hedonic and utilitarian dimensions of consumer attitude. Journal of Marketing Research,40, 310–320.

    Article  Google Scholar 

  65. Vroom, V. (1964). Work and motivation. New York, NY: Wiley.

    Google Scholar 

  66. Wells, A. (2000). Emotional disorders and metacognition: Innovative cognitive therapy. Chichester: Wiley.

    Google Scholar 

  67. Welsh, M. B., Delfabbro, P. H., Burns, N. R., & Begg, S. H. (2014). Individual differences in anchoring: Traits and experience. Learning and Individual Differences,29, 131–140.

    Article  Google Scholar 

  68. Wigfield, A., & Eccles, J. S. (2000). Expectancy-value theory of achievement motivation. Contemporary Educational Psychology,25, 68–81.

    Article  Google Scholar 

  69. Xu, D., Huang, W. W., Wang, H., & Heales, J. (2014). Enhancing e-learning effectiveness using an intelligent agent-supported personalized virtual learning environment: An empirical investigation. Information & Management,51(4), 430–440.

    Article  Google Scholar 

  70. Zhang, Y., & Er, M. J. (2016). Sequential active learning using meta-cognitive extreme learning machine. Neurocomputing,173(3), 835–844.

    Article  Google Scholar 

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Acknowledgement

This work was financially supported by the “Institute for Research Excellence in Learning Sciences” of National Taiwan Normal University (NTNU) from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan.

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Correspondence to Ming-Yueh Hwang.

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Hong, J., Hwang, M., Liu, M. et al. Metacognition in covariation reasoning relevant to performance achievement mediated by experiential values in a simulation game. Education Tech Research Dev 68, 929–948 (2020). https://doi.org/10.1007/s11423-019-09711-1

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Keywords

  • Experiential value
  • Human–computer interface
  • Improving classroom teaching
  • Interactive learning environments
  • Metacognition