共變推理遊戲：遊戲自我效能與後設認知影響遊戲中的焦慮、興趣及表現之研究
作者:國立臺灣師範大學學習科學跨國頂尖研究中心洪榮昭、國立臺灣師範大學工業教育系詹瓊華
卷期：63卷第3期
日期：2018年9月
頁碼：131-162
DOI：10.6209/JORIES.201809_63(3).0005
摘要：
在日常生活中碰到問題時，常是顧此失彼，造成問題解決不能一步到位而徒花心力，為了解決這種思考不周延的現象，本研究應用一款「NG麵包遊戲」來檢測影響學生的遊戲焦慮、遊戲興趣、遊戲自我效能與後設認知間在共變推理遊戲中之相關。本研究以立意取樣方式，選取138位高一學生，每週進行20分鐘的NG麵包遊戲，連續實施六週，作為學習過程的一部分，學生必須完成線上問卷調查，包含遊戲實驗前的後設認知與遊戲自我效能等相關問卷，以及每次遊戲實驗後的遊戲焦慮、遊戲興趣等相關問卷調查，藉此以瞭解各變項間之相關。所得119份有效資料以SPSS 22與AMOS 21結構方程式進行資料分析與考驗，以瞭解高中學生情感因素間的相關性。本研究運用共變推理遊戲，以驗證各變項間之相關，研究結果顯示，後設認知、遊戲自我效能、遊戲興趣與遊戲焦慮皆呈現顯著負相關，遊戲自我效能與遊戲興趣呈現顯著正相關。結果證實，在特定的任務中提高玩家的遊戲自我效能，可減少遊戲焦慮進而支持玩家在競爭環境中的遊戲興趣。最終冀望本研究結果能提供教育工作者使用數位遊戲來訓練學生之可能性，以增強共變推理的能力。
關鍵詞：共變推理、後設認知、遊戲自我效能、遊戲興趣、遊戲焦慮
《詳全文》
參考文獻：
- 邱皓政（2011）。結構方程模式：LISREL／SIMPLIS原理與應用（二版）。臺北市：雙葉書廊。 【Chiou, H. (2011). Principles and practice of structural equation modeling with LISREL/SIMPLIS (2nd ed.). Taipei, Taiwan: Yeh Yeh Book Gallery.】
- Ackerman, R., Parush, A., Nassar, F., & Shtub, A. (2016). Metacognition and system usability: Incorporating metacognitive research paradigm into usability testing. Computers in Human Behavior, 54, 101-113. doi:10.1016/j.chb.2015.07.041
- Adkins, S. S. (2011). The worldwide market for self-paced eLearning products and services: 2010-2015 forecast and analysis. Monroe, WA: Ambient Insight Research.
- Ainley, M. (2012). Students’ interest and engagement in classroom activities. In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 283-302). Singapore: Springer. doi:10.1007/978-1-4614-2018-7_13
- Ainley, M., & Ainley, J. (2011). A cultural perspective on the structure of student interest in science. International Journal of Science Education, 33(1), 51-71. doi:10.1080/09500693.2010.518640
» 展開更多
- 邱皓政（2011）。結構方程模式：LISREL／SIMPLIS原理與應用（二版）。臺北市：雙葉書廊。 【Chiou, H. (2011). Principles and practice of structural equation modeling with LISREL/SIMPLIS (2nd ed.). Taipei, Taiwan: Yeh Yeh Book Gallery.】
- Ackerman, R., Parush, A., Nassar, F., & Shtub, A. (2016). Metacognition and system usability: Incorporating metacognitive research paradigm into usability testing. Computers in Human Behavior, 54, 101-113. doi:10.1016/j.chb.2015.07.041
- Adkins, S. S. (2011). The worldwide market for self-paced eLearning products and services: 2010-2015 forecast and analysis. Monroe, WA: Ambient Insight Research.
- Ainley, M. (2012). Students’ interest and engagement in classroom activities. In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 283-302). Singapore: Springer. doi:10.1007/978-1-4614-2018-7_13
- Ainley, M., & Ainley, J. (2011). A cultural perspective on the structure of student interest in science. International Journal of Science Education, 33(1), 51-71. doi:10.1080/09500693.2010.518640
- Ainley, M., Hidi, S., & Berndorff, D. (2002). Interest, learning and the psychological processes that mediate their relationship. Journal of Educational Psychology, 94(3), 545-561. doi:10.1037/ 0022-0663.94.3.545
- Akca, F. (2011). The relationship between test anxiety and learned helplessness. Social Behavior and Personality: An International Journal, 39(1), 101-112. doi:10.2224/sbp.2011.39.1.101
- Arbuckle, J. (2003). AMOS 5.0 update to the AMOS user’s guide. Chicago, IL: Marketing Department, SPSS.
- Bandura, A. (1986). The explanatory and predictive scope of self-efficacy theory. Journal of Social and Clinical Psychology, 4(3), 359-373. doi:10.1521/jscp.1986.4.3.359
- Bandura, A. (1997). Self-efficacy: The exercise of control. New York, NY: W. H. Freeman.
- Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74-94. doi:10.1177/009207038801600107
- Bentler, P. M. (2006). EQS 6 structural equations program manual. Encino, CA: Multivariate Software.
- Berger, J.-L., & Karabenick, S. A. (2011). Motivation and students’ use of learning strategies: Evidence of unidirectional effects in mathematics classrooms. Learning and Instruction, 21(3), 416-428. doi:10.1016/j.learninstruc.2010.06.002
- 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), 246-263. doi:10.1111/j.1467-8624.2007.00995.x
- Boddez, Y., De Houwer, J., & Beckers, T. (2017). The inferential reasoning theory of causal learning: Towards a multi-process propositional account. In M. R. Waldmann (Ed.), The Oxford handbook of causal reasoning (pp. 53-64). Oxford, UK: Oxford University Press. doi:10.1093/ oxfordhb/9780199399550.013.7
- Bonus, J. A., Peebles, A., Mares, M.-L., & Sarmiento, I. G. (2017). Look on the bright side (of media effects): Pokémon Go as a catalyst for positive life experiences. Media Psychology, 21(2), 1-25. doi:10.1080/15213269.2017.1305280
- Byrne, B. M. (2016). Structural equation modeling with AMOS: Basic concepts, applications, and programming (3rd ed.). London, UK: Routledge.
- Connolly, T. M., Boyle, E. A., MacArthur, E., Hainey, T., & Boyle, J. M. (2012). A systematic literature review of empirical evidence on computer games and serious games. Computers & Education, 59(2), 661-686. doi:10.1016/j.compedu.2012.03.004
- Csikszentmihalyi, M., & Schneider, B. (2000). Becoming adult: How teenagers prepare for the world of work. New York, NY: Basic Books.
- Dillman, D. A., Smyth, J. D., & Christian, L. M. (2008). Internet, mail, and mixed-mode surveys: The tailored design method. Hoboken, NJ: John Wiley & Sons.
- D’Mello, S. (2013). A selective meta-analysis on the relative incidence of discrete affective states during learning with technology. Journal of Educational Psychology, 105(4), 1082-1099. doi:10. 1037/a0032674
- Doll, W. J., Xia, W., & Torkzadeh, G. (1994). A confirmatory factor analysis of the end-user computing satisfaction instrument. MIS Quarterly, 18(4), 453-461. doi:10.2307/249524
- Drigas, A. S., Ioannidou, R.-E., Kokkalia, G., & Lytras, M. D. (2014). ICTs, mobile learning and social media to enhance learning for attention difficulties. Journal of Universal Computer Science, 20(10), 1499-1510.
- Ehrman, M. (1996). An exploration of adult language learner motivation, self-efficacy and anxiety. In R. L. Oxford (Ed.), Language learning motivation: Pathways to the new century (pp. 81-103). Honolulu, HI: University of Hawaii Press.
- Elliot, A. J., McGregor, H. A., & Gable, S. (1999). Achievement goals, study strategies, and exam performance: A mediational analysis. Journal of Educational Psychology, 91(3), 549-563. doi:10.1037/0022-0663.91.3.549
- Eseryel, D., Law, V., Ifenthaler, D., Ge, X., & Miller, R. B. (2014). An investigation of the interrelationships between motivation, engagement, and complex problem solving in game-based learning. Educational Technology & Society, 17(1), 42-53.
- Eysenck, M. W., Derakshan, N., Santos, R., & Calvo, M. G. (2007). Anxiety and cognitive performance: Attentional control theory. Emotion, 7(2), 336-353. doi:10.1037/1528-3542.7.2.336
- Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive-developmental inquiry. American Psychologist, 34(10), 906-911. doi:10.1037/0003-066X.34.10.906
- Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. doi:10.2307/ 3151312
- Glynn, M. A., & Webster, J. (1993). Refining the nomological net of the adult playfulness scale: Personality, motivational, and attitudinal correlates for highly intelligent adults. Psychological Reports, 72(3), 1023-1026. doi:10.2466/pr0.1993.72.3.1023
- Gopnik, A., Glymour, C., Sobel, D. M., Schulz, L. E., Kushnir, T., & Danks, D. (2004). A theory of causal learning in children: Causal maps and Bayes nets. Psychological Review, 111(1), 3-32. doi:10.1037/0033-295X.111.1.3
- Gomila, T., & Calvo, P. (2008). Directions for an embodied cognitive science: Toward an integrated approach. In T. Gomila & P. Calvo (Eds.), Handbook of cognitive science: An embodied approach (pp. 1-25). Singapore: Elsevier. doi:10.1016/B978-0-08-046616-3.00001-3
- Gonzalez, B. C. D. (2016). Learn 2 learn: A metacognition intervention for improving academic performance and motivation on middle school-aged students. Retrieved from http://digital repository.trincoll.edu/theses/597
- Green, S. B., & Salkind, N. J. (2004). Using SPSS for Windows and Macintosh: Analyzing and understanding data (4th ed.). Englewood Cliffs, NJ: Prentice-Hall.
- Hacker, D. J. (2017). The role of metacognition in learning via serious games. In R. Zheng & M. K. Gardner (Eds.), Handbook of research on serious game for educational application (pp. 19-40). Hershey, PA: Information Science Research. doi:10.4018/978-1-5225-0513-6.ch002
- Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2009). Multivariate data analysis (7th ed.). Upper Saddle River, NJ: Pearson Prentice Hall.
- Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2013). A primer on partial least squares structural equation modeling. Thousand Oaks, CA: Sage.
- 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(3), 414-433. doi:10.1007/s11747-011-0261-6
- Hamari, J., Shernoff, D. J., Rowe, E., Coller, B., Asbell-Clarke, J., & Edwards, T. (2016). Challenging games help students learn: An empirical study on engagement, flow and immersion in game-based learning. Computers in Human Behavior, 54, 170-179. doi:10.1016/j.chb.2015. 07.045
- Hancock, G. R., & Mueller, R. O. (Eds.). (2013). Structural equation modeling: A second course (2nd ed.). Charlotte, NC: Information Age.
- Hidi, S. (2001). Interest, reading, and learning: Theoretical and practical considerations. Educational Psychology Review, 13(3), 191-209. doi:10.1023/A:1016667621114
- Hidi, S., & Renninger, A. (2006). The four-phase model of interest development. Educational Psychologist, 41(2), 111-127. doi:10.1207/s15326985ep4102_4
- Hong, J.-C., & Liu, M.-C. (2003). A study on thinking strategy between experts and novices of computer games. Computers in Human Behavior, 19(2), 245-258. doi:10.1016/S0747-5632(02) 00013-4
- Hong, J.-C., Hwang, M.-Y., Hsu, T.-F., & Chen, Y.-J. (2012). The relations between students’ anxiety and interest in playing an online game. Turkish Online Journal of Educational Technology, 11(2), 255-263.
- 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, 110-120. doi:10.1016/j.compedu.2013.10.004
- Hong, J.-C., Hwang, M.-Y., Liu, Y.-T., Lin, P.-H., & Chen, Y.-L. (2016). The role of pre-game learning attitude in the prediction to competitive anxiety, perceived utility of pre-game learning of game, and gameplay interest. Interactive Learning Environments, 24(1), 239-251. doi:10. 1080/10494820.2013.841263
- Hong, J.-C., Hwang, M.-Y., Tai, K.-H., & Lin, P.-C. (2015). Self-efficacy relevant to competitive anxiety and gameplay interest in the one-on-one competition setting. Educational Technology Research and Development, 63(5), 791-807. doi:10.1007/s11423-015-9389-2
- 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), 1-55. doi:10.1080/10705519909540118
- Hui, E. C. M., & Bao, H. (2013). The logic behind conflicts in land acquisitions in contemporary China: A framework based upon game theory. Land Use Policy, 30(1), 373-380. doi:10.1016/ j.landusepol.2012.04.001
- Hwang, M.-Y., Hong, J.-C., Cheng, H.-Y., Peng, Y.-C., & Wu, N.-C. (2013). Gender differences in cognitive load and competition anxiety affect 6th grade students’ attitude toward playing and intention to play at a sequential or synchronous game. Computers and Education, 60(1), 254-263. doi:10.1016/j.compedu.2012.06.014
- Jansen, M., Scherer, R., & Schroeders, U. (2015). Students’ self-concept and self-efficacy in the sciences: Differential relations to antecedents and educational outcomes. Contemporary Educational Psychology, 41, 13-24. doi:10.1016/j.cedpsych.2014.11.002
- Jerusalem, M., & Schwarzer, R. (1992). Self-efficacy as a resource factor in stress appraisal processes. In R. Schwarzer (Ed.), Self-efficacy: Thought control of action (pp. 195-213). Washington, DC: Hemisphere.
- Jöreskog, K. G., & Sörbom, D. (1996). LISREL 8: user’s reference guide. Uppsala, Sweden: Scientific Software International.
- Ke, F., & Shute, V. (2015). Design of game-based stealth assessment and learning support. In C. S. Loh, Y. Sheng, & D. Ifenthaler (Eds.), Serious games analytics (pp. 301-318). New York, NY: Springer. doi:10.1007/978-3-319-05834-4_13
- Kline, R. B. (2015). Principles and practice of structural equation modeling (4th ed.). New York, NY: Guilford Press.
- Köklü, Ö., & Jakubowski, E. (2010). From interpretations to graphical representations: A case study investigation of covariational reasoning. Eurasian Journal of Educational Research, 40, 151-170.
- Koslowski, B. (2012). Scientific reasoning: Explanation, confirmation bias, and scientific practice. In G. J. Feist & M. E. Gorman (Eds.), Handbook of the psychology of science (pp. 151-192). New York, NY: Springer.
- Kurtz, B. E., & Borkowski, J. G. (1987). Development of strategic skills in impulsive and reflective children: A longitudinal study of metacognition. Journal of Experimental Child Psychology, 43(1), 129-148. doi:10.1016/0022-0965(87)90055-5
- Laffey, J. M., Espinosa, L., Moore, J., & Lodree, A. (2003). Supporting learning and behavior of at-risk young children: Computers in urban education. Journal of Research on Technology in Education, 35(4), 423-440. doi:10.1080/15391523.2003.10782394
- Lagnado, D. A., & Sloman, S. A. (2004). The advantage of timely intervention. Journal of Experimental Psychology: Learning, Memory, and Cognition, 30(4), 856-876. doi:10.1037/0278-7393.30.4.856
- Lagnado, D. A., & Sloman, S. A. (2006). Time as a guide to cause. Journal of Experimental Psychology: Learning, Memory, and Cognition, 32(3), 451-460. doi:10.1037/0278-7393.32.3.451
- Lan, Y.-J., Chen, N.-S., Li, P., & Grant, S. (2015). Embodied cognition and language learning in virtual environments. Educational Technology Research and Development, 63(5), 639-644. doi:10.1007/s11423-015-9401-x
- Law, E. L.-C., & Sun, X. (2012). Evaluating user experience of adaptive digital educational games with activity theory. International Journal of Human Computer Studies, 70(7), 478-497. doi:10. 1016/j.ijhcs.2012.01.007
- Liew, T. W., & Tan, S.-M. (2016). The effects of positive and negative mood on cognition and motivation in multimedia learning environment. Journal of Educational Technology & Society, 19(2), 104-115.
- Lin, L., Atkinson, R. K., Savenye, W. C., & Nelson, B. C. (2016). Effects of visual cues and self-explanation prompts: Empirical evidence in a multimedia environment. Interactive Learning Environments, 24(4), 799-813. doi:10.1080/10494820.2014.924531
- MacCallum, R. C., & Hong, S. (1997). Power analysis in covariance structure modeling using GFI and AGFI. Multivariate Behavioral Research, 32(2), 193-210. doi:10.1207/s15327906mbr3202_5
- Madison, B. L., Carlson, M., Oehrtman, M., & Tallman, M. (2015). Conceptual pre-calculus: Strengthening students’ quantitative and covariational reasoning. Mathematics Teacher, 109(1), 54-59. doi:10.5951/mathteacher.109.1.0054
- Masicampo, E. J., & Baumeister, R. F. (2011). Unfulfilled goals interfere with tasks that require executive functions. Journal of Experimental Social Psychology, 47(2), 300-311. doi:10.1016j. jesp.2010.10.011
- Martens, R., Burton, D., Vealey, R. S., Bump, L. A., & Smith, D. E. (1990). Development and validation of the competitive state anxiety inventory-2. In R. Martens, R. S. Vealey, & D. Burton (Eds.), Competitive anxiety in sport (pp. 117-190). Champaign, IL: Human Kinetics.
- Martens, R., Vealey, R. S., & Burton, D. (1990). Competitive anxiety in sport. Champaign, IL: Human Kinetics.
- McMorris, T., Sproule, J., Turner, A., & Hale, B. J. (2011). Acute, intermediate intensity exercise, and speed and accuracy in working memory tasks: A meta-analytical comparison of effects. Physiology and Behavior, 102(3-4), 421-428. doi:10.1016/j.physbeh.2010.12.007
- Meluso, A., Zheng, M., Spires, H. A., & Lester, J. (2012). Enhancing 5th graders’ science content knowledge and self-efficacy through game-based learning. Computers & Education, 59(2), 497-504. doi:10.1016/j.compedu.2011.12.019
- 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. doi:10. 1111/j.1365-2729.2006.00170.x
- Morony, S., Kleitman, S., Lee, Y. P., & Stankov, L. (2013). Predicting achievement: Confidence vs. self-efficacy, anxiety, and self-concept in Confucian and European countries. International Journal of Educational Research, 58, 79-96. doi:10.1016/j.ijer.2012.11.002
- Morris, L. W., Davis, M. A., & Hutchings, C. H. (1981). Cognitive and emotional components of anxiety: Literature review and a revised worry-emotionality scale. Journal of Educational Psychology, 73(4), 541-555. doi:10.1037/0022-0663.73.4.541
- Nicaise, M. (1995). Treating test anxiety: A review of three approaches. Teacher Education and Practice, 11(1), 65-81.
- Nichols, B. S. (2012). The development, validation, and implications of a measure of consumer competitive arousal (CCAr). Journal of Economic Psychology, 33(1), 192-205. doi:10.1016/ j.joep.2011.10.002
- Nie, Y., Lau, S., & Liau, A. K. (2011). Role of academic self-efficacy in moderating the relation between task importance and test anxiety. Learning and Individual Differences, 21(6), 736-741. doi:10.1016/j.lindif.2011.09.005
- Pajares, F. (1996). Self-efficacy beliefs in academic settings. Review of Educational Research, 66(4), 543-578. doi:10.3102/00346543066004543
- Parker, P. D., Marsh, H. W., Ciarrochi, J., Marshall, S., & Abduljabbar, A. S. (2014). Juxtaposing math self-efficacy and self-concept as predictors of long-term achievement outcomes. Educational Psychology, 34(1), 29-48. doi:10.1080/01443410.2013.797339
- Perales, J. C., Catena, A., Cándido, A., & Maldonado, A. (2017). Rules of causal judgment: Mapping statistical information onto causal beliefs. In M. R. Waldmann (Ed.), The Oxford handbook of causal reasoning (pp. 29-51). Oxford, UK: Oxford University Press. doi:10.1093/oxfordhb/ 9780199399550.013.6
- Potosky, D. (2002). A field study of computer efficacy beliefs as an outcome of training: The role of computer playfulness, computer knowledge, and performance during training. Computers in Human Behavior, 18(3), 241-255. doi:10.1016/S0747-5632(01)00050-4
- Ree, M. J., French, D., MacLeod, C., & Locke, V. (2008). Distinguishing cognitive and somatic dimensions of state and trait anxiety: Development and validation of the State-Trait Inventory for Cognitive and Somatic Anxiety (STICSA). Behavioural and Cognitive Psychotherapy, 36(3), 313-332. doi:10.1017/S1352465808004232
- Renninger, K. A. (2010). Working with and cultivating the development of interest, self-efficacy, and self-regulation. In D. D. Preiss & R. J. Sternberg (Eds.), Innovations in educational psychology: Perspectives on learning, teaching and human development (pp. 107-138). New York, NY: Springer.
- Renninger, K. A., & Hidi, S. E. (2015). The power of interest for motivation and engagement. New York, NY: Routledge. doi:10.4324/9781315771045
- Renninger, A., Hidi, S. E., & Krapp, A. (Eds.). (2014). The role of interest in learning and development. Hove, UK: Psychology Press.
- Richardson, M., Abraham, C., & Bond, R. (2012). Psychological correlates of university students’ academic performance: A systematic review and meta-analysis. Psychological Bulletin, 138(2), 353-387. doi:10.1037/a0026838
- Riggs, M. L., & Knight, P. A. (1994). The impact of perceived group success-failure on motivational beliefs and attitudes: A causal model. Journal of Applied Psychology, 79(5), 755-766. doi:10. 1037/0021-9010.79.5.755
- Ritchie, L., & Williamon, A. (2013). Measuring musical self-regulation: Linking processes, skills, and beliefs. Journal of Education and Training Studies, 1(1), 106-117. doi:10.11114/jets.v1i1.81
- Roeser, R. W., & Peck, S. C. (2009). An education in awareness: Self, motivation, and self-regulated learning in contemplative perspective. Educational Psychologist, 44(2), 119-136. doi:10.1080/ 00461520902832376
- Rottman, B. M., & Keil, F. C. (2012). Causal structure learning over time: Observations and interventions. Cognitive Psychology, 64(1-2), 93-125. doi:10.1016/j.cogpsych.2011.10.003
- Sansone, C., Smith, J. L., Thoman, D. B., & MacNamara, A. (2012). Regulating interest when learning online: Potential motivation and performance trade-offs. The Internet and Higher Education, 15(3), 141-149. doi:10.1016/j.iheduc.2011.10.004
- Schaie, K. W., Dutta, R., & Willis, S. L. (1991). Relationship between rigidity-flexibility and cognitive abilities in adulthood. Psychology and Aging, 6(3), 371-383. doi:10.1037/0882-7974. 6.3.371
- Schönfeld, P., Brailovskaia, J., Bieda, A., Zhang, X. C., & Margraf, J. (2016). The effects of daily stress on positive and negative mental health: Mediation through self-efficacy. International Journal of Clinical and Health Psychology, 16(1), 1-10. doi:10.1016/j.ijchp.2015.08.005
- Schraw, G., & Dennison, R. S. (1994). Assessing metacognitive awareness. Contemporary Educational Psychology, 19(4), 460-475. doi:10.1006/ceps.1994.1033
- Schumacker, R. E., & Lomax, R. G. (2016). A beginner’s guide to structural equation modeling (4th ed.). New York, NY: Routledge.
- Schunk, D. H. (2012). Learning theories: An educational perspective (6th ed.). Boston, MA: Pearson.
- Selfe, C. L., Hawisher, G. E., Van Ittersum, D., & Gee, J. P. (2016). Gaming lives in the twenty- first century. New York, NY: Palgrave Macmillan. doi:10.1057/9780230601765
- Sloman, S. A., & Lagnado, D. (2015). Causality in thought. Annual Review of Psychology, 66, 223-247. doi:10.1146/annurev-psych-010814-015135
- Snow, C. E., Burns, M. S., & Griffin, P. (1998). Preventing reading difficulties in young children. Committee on the Prevention of Reading Difficulties in Young Children, National Research Council. Washington, DC: National Academy Press. doi:10.17226/6023
- Stankov, L., Lee, J., Luo, W., & Hogan, D. J. (2012). Confidence: A better predictor of academic achievement than self-efficacy, self-concept and anxiety? Learning and Individual Differences, 22(6), 747-758. doi:10.1016/j.lindif.2012.05.013
- Steinkuehler, C., Squire, K., & Barab, S. (Eds.). (2012). Games, learning, and society: Learning and meaning in the digital age. Cambridge, UK: Cambridge University Press. doi:10.1017/CBO97 81139031127.006
- Stewart, O., & Tei, E. (1983). Some implications of metacognition for reading instruction. Journal of Reading, 27(1), 36-43.
- Stern, C., & West, T. V. (2014). Circumventing anxiety during interpersonal encounters to promote interest in contact: An implementation intention approach. Journal of Experimental Social Psychology, 50, 82-93. doi:10.1016/j.jesp.2013.09.008
- Suliman, W. A., & Halabi, J. (2007). Critical thinking, self-esteem, and state anxiety of nursing students. Nurse Education Today, 27(2), 162-168. doi:10.1016/j.nedt.2006.04.008
- Thompson, N., & McGill, T. J. (2014). The application of affective computing technology to e-Learning. In M. Thomas (Ed.), Pedagogical considerations and opportunities for teaching and learning on the Web (pp. 109-128). Hershey, PA: IGI Global. doi:10.4018/978-1-4666- 4611-7.ch007
- Vogel, J. J., Vogel, D. S., Cannon-Bowers, J., Bowers, C. A., Muse, K., & Wright, M. (2006). Computer gaming and interactive simulations for learning: A meta-analysis. Journal of Educational Computing Research, 34(3), 229-243. doi:10.2190/FLHV-K4WA-WPVQ-H0YM
- Vos, N., van der Meijden, H., & Denessen, E. (2011). Effects of constructing versus playing an educational game on student motivation and deep learning strategy use. Computers & Education, 56(1), 127-137. doi:10.1016/j.compedu.2010.08.013
- Webster, J., Trevino, L. K., & Ryan, L. (1993). The dimensionality and correlates of flow in human-computer interactions. Computers in Human Behavior, 9(4), 411-426. doi:10.1016/ 0747-5632(93)90032-N
- Weiner, B. (2013). Human motivation. Hove, UK: Psychology Press.
- Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 13-39). San Diego, CA: Academic Press. doi:10.1016/B978-012109890-2/50031-7
Journal directory listing - Volume 63 (2018) - Journal of Research in Education Sciences【63(3)】september
Game Performance in Covariation Reasoning: The Correlates Between Gameplay Self-Efficacy, and Metacognition Reflected Gameplay Anxiety and Gameplay Interest
Author: Jon-Chao Hong (The Advanced Center for the Study of Learning Sciences, National Taiwan Normal University), Chiung-Hua Chan (Department of Industrial Education, National Taiwan Normal University)
Vol.&No.：Vol. 63, No.3
Date：September 2018
Pages：131-162
DOI：10.6209/JORIES.201809_63(3).0005
Abstract：
For studying cause-effect relationships, a monoapproach is preferred. However, to account for the complex nature of reality, practicing covariation thinking is necessary. For exploring how cognitive-affective factors play a crucial role in the ability to practice covariation reasoning, this study collected data from senior high school students aged 16-17 years and 138 students were invited to practice that game 20 minutes for 6 times. As part of their studying process, the students were required to complete online questionnaires. The questionnaire related to metacognition and gameplay self-efficacy were delivered before this experiment, questionnaires related to gameplay anxiety and gameplay interest were given after each trial of game playing. Path analysis of data from 119 effective responses was performed using SPSS (version 22) and structural equation modeling-AMOS (version 21). The results demonstrated that gameplay self-efficacy, gameplay interest, and metacognition in learning “NG Bread” was negatively correlated to gameplay anxiety, but gameplay self-efficacy was positively related to gameplay interest. Furthermore, the result indicated that enhancing gameplay self-efficacy in a specific task may reduce players’ anxiety and encourage interest in playing the game in a competitive setting. The implication of this study may encourage educators to use this digital game for improving covariation reasoning of their students.
Keywords：covariation reasoning, gameplay anxiety, gameplay interest, gameplay self-efficacy, metacognition