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Articles

The entity belief of concentration ability predicts cognitive load, failure-attribution, and flow experience when using a virtual reality device

ORCID Icon, ORCID Icon, ORCID Icon &
Pages 34-51 | Received 22 Nov 2021, Accepted 06 May 2022, Published online: 18 May 2022

References

  • Abedi, G., Rostami, F., & Nadi, A. (2015). Analyzing the dimensions of the quality of life in hepatitis B patientsusing confirmatory factor analysis. Global Journal of Health Science, 7(7). http://doi.org/10.5539/gjhs.v7n7p22
  • Adolph, K. E., & Joh, A. S. (2007). Motor development: How infants get into the act. In A. Slater, & M. Lewis (Eds.), Introduction to infant development (2nd ed., pp. 63–80). Oxford University Press.
  • Alan, A. K., Kabadayi, E. T., & Aksoy, N. C. (2022). Replaying online games for flow experience and outcome expectations: An exploratory study for the moderating role of external locus of control based on Turkish gamers’ evaluations. Entertainment Computing, 40, 100460. https://doi.org/10.1016/j.entcom.2021.100460
  • Al-Saud, L. M., Mushtaq, F., Allsop, M. J., Culmer, P. C., Mirghani, I., Yates, E., Keeling, A., Mon-Williams, M. A., & Manogue, M. (2017). Feedback and motor skill acquisition using a haptic dental simulator. European Journal of Dental Education, 21(4), 240–247. https://doi.org/10.1111/eje.12214
  • Anmarkrud, Ø, Andresen, A., & Bråten, I. (2019). Cognitive load and working memory in multimedia learning: Conceptual and measurement issues. Educational Psychologist, 54(2), 61–83. https://doi.org/10.1080/00461520.2018.1554484
  • Armougum, A., Orriols, E., Gaston-Bellegarde, A., Marle, C. J. L., & Piolino, P. (2019). Virtual reality: A new method to investigate cognitive load during navigation. Journal of Environmental Psychology, 65, 101338. https://doi.org/10.1016/j.jenvp.2019.101338
  • Awang, Z. (2015). SEM made simple, a gentle approach to learning structural equation modeling. MPWS Rich Publication Sdn. Bhd.
  • Baker, R. S., D'Mello, S. K., Rodrigo, M. M. T., & Graesser, A. C. (2010). Better to be frustrated than bored: The incidence, persistence, and impact of learners’ cognitive-affective states during interactions with three different computer-based learning environments. International Journal of Human-Computer Studies, 68(4), 223–241. https://doi.org/10.1016/j.ijhcs.2009.12.003
  • 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. https://doi.org/10.1111/j.1467-8624.2007.00995.x
  • Çakiroğlu, Ü, & Gökoğlu, S. (2019). Development of fire safety behavioral skills via virtual reality. Computers & Education, 133, 56–68. https://doi.org/10.1016/j.compedu.2019.01.014
  • 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, 218–227. https://doi.org/10.1016/j.chb.2017.01.031
  • Chen, O., Woolcott, G., & Sweller, J. (2017). Using cognitive load theory to structure computer-based learning including MOOCs. Journal of Computer Assisted Learning, 33(4), 293–305. https://doi.org/10.1111/jcal.12188
  • Choi, B., & Baek, Y. (2011). Exploring factors of media characteristic influencing flow in learning through virtual worlds. Computers & Education, 57(4), 2382–2394. https://doi.org/10.1016/j.compedu.2011.06.019
  • Choi, M., Ahn, S., & Seo, J. (2020). VR-Based investigation of forklift operator situation awareness for preventing collision accidents. Accident Analysis and Prevention, 136, 105404. https://doi.org/10.1016/j.aap.2019.105404
  • Clem, A. L., Hirvonen, R., Aunola, K., & Kiuru, N. (2021). Reciprocal relations between adolescents’ self-concepts of ability and achievement emotions in mathematics and literacy. Contemporary Educational Psychology, 65, 101964. https://doi.org/10.1016/j.cedpsych.2021.101964
  • Conard, M. A., & Marsh, R. F. (2014). Interest level improves learning but does not moderate the effects of interruptions: An experiment using simultaneous multitasking. Learning and Individual Differences, 30, 112–117. https://doi.org/10.1016/j.lindif.2013.11.004
  • Cor, M. K. (2016). Trust me, it is valid: Research validity in pharmacy education research. Currents in Pharmacy Teaching and Learning, 8(3), 391–400. http://doi.org/10.1016/j.cptl.2016.02.014
  • Csikszentmihalyi, M. (1975). Beyond boredom and anxiety. Jossey-Bass.
  • Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience. Harper Collins.
  • Csikszentmihalyi, M. (2000). Happiness, flow, and economic equality. American Psychologist, 55(10), 1163–1164. https://doi.org/10.1037/0003-066X.55.10.1163
  • DeLuca, J., & Kalmar, J. H. (2008). Information processing speed in clinical populations. Taylor & Francis.
  • Dinger, F. C., & Dickhäuser, O. (2013). Does implicit theory of intelligence cause achievement goals? Evidence from an experimental study. International Journal of Educational Research, 61, 38–47. https://doi.org/10.1016/j.ijer.2013.03.008
  • Dorigoni, A., Rajsic, J., & Bonini, N. (2022). Does cognitive reflection predict attentional control in visual tasks? Acta Psychologica, 226, 103562. https://doi.org/10.1016/j.actpsy.2022.103562
  • Dweck, C. S. (1986). Motivational processes affecting learning. American Psychologist, 41(10), 1040–1048. https://doi.org/10.1037/0003-066X.41.10.1040
  • Ehrlinger, J., Mitchum, A. L., & Dweck, C. S. (2016). Understanding overconfidence: Theories of intelligence, preferential attention, and distorted self-assessment. Journal of Experimental Social Psychology, 63, 94–100. https://doi.org/10.1016/j.jesp.2015.11.001
  • Erhel, S., & Jamet, E. (2019). Improving instructions in educational computer games: Exploring the relations between goal specificity, flow experience and learning outcomes. Computers in Human Behavior, 91, 106–114. https://doi.org/10.1016/j.chb.2018.09.020
  • Eysenck, M. W., Derakshan, N., Santos, R., & Calvo, M. G. (2007). Anxiety and cognitive performance: Attentional control theory. Emotion, 7(2), 336–353. https://doi.org/10.1037/1528-3542.7.2.336
  • Fowler, C. (2015). Virtual reality and learning: Where is the pedagogy? British Journal of Educational Technology, 46(2), 412–422. https://doi.org/10.1111/bjet.12135
  • Graham, S. (2020). An attributional theory of motivation. Contemporary Educational Psychology, 61, 101861. https://doi.org/10.1016/j.cedpsych.2020.101861
  • Green, S. B., & Salkind, N. (2004). Using SPSS for Windows and Macintosh: Analyzing and understanding data. Prentice-Hall.
  • Haimovitz, K., & Dweck, C. S. (2017). The origins of children's growth and fixed mindsets: New research and a new proposal. Child Development, 88(6), 1849–1859. https://doi.org/10.1111/cdev.12955
  • Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Pearson Prentice Hall.
  • Harley, J. M., Pekrun, R., Taxer, J. L., & Gross, J. J. (2019). Emotion regulation in achievement situations: An integrated model. Educational Psychologist, 54(2), 106–126. https://doi.org/10.1080/00461520.2019.1587297
  • Harris, D. J., Buckingham, G., Wilson, M. R., Brookes, J., Mushtaq, F., Mon-Williams, M., & Vine, S. J. (2020). The effect of a virtual reality environment on gaze behaviour and motor skill learning. Psychology of Sport & Exercise, 50, 101721. https://doi.org/10.1016/j.psychsport.2020.101721
  • Hart, S. G., & Staveland, L. E. (1988). Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research. Advances in Psychology, 52, 139–183. Human mental workload. https://doi.org/10.1016/S0166-4115(08)62386-9
  • Heider, F. (1958). The psychology of interpersonal relationships. Wiley.
  • Hilchey, M. D., Ivanoff, J., Taylor, T. L., & Klein, R. M. (2011). Visualizing the temporal dynamics of spatial information processing responsible for the Simon effect and its amplification by inhibition of return. Acta Psychology, 136(2), 235–244. https://doi.org/10.1016/j.actpsy.2010.09.003
  • Hochberg, K., Kuhn, J., & Müller, A. (2018). Using smartphones as experimental tools—Effects on interest, curiosity, and learning in physics education. Journal of Science Education and Technology, 27(5), 385–403. https://doi.org/10.1007/s10956-018-9731-7
  • Hommel, B. (2011). The Simon effect as tool and heuristic. Acta Psychology, 136(2), 189–202. https://doi.org/10.1016/j.actpsy.2010.04.011
  • Hong, J. C., Hwang, M. Y., Tai, K. H., Lin, P. H., & Lin, P. C. (2020). Learning progress in a Chinese order of stroke game: The effects of intrinsic cognitive load and gameplay interest mediated by flow experience. Journal of Educational Computing Research, 58(4), 842-862. https://doi.org/10.1177/0735633119881471
  • Hong, J. C., Tai, K. H., Hwang, M. Y., Kuo, Y. C., & Chen, J. S. (2017). Internet cognitive failure relevant to users’ satisfaction with content and interface design to reflect continuance intention to use a government e-learning system. Computers in Human Behavior, 66, 353–362. https://doi.org/10.1016/j.chb.2016.08.044
  • 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, 127–138. https://doi.org/10.1016/j.compedu.2019.01.009
  • Hughes, J. S. (2015). Support for the domain specificity of implicit beliefs about persons, intelligence, and morality. Personality and Individual Differences, 86, 195–203. https://doi.org/10.1016/j.paid.2015.05.042
  • Jackson, M. (2019). Utilizing attribution theory to develop new insights into tourism experiences. Journal of Hospitality and Tourism Management, 38, 176–183. https://doi.org/10.1016/j.jhtm.2018.04.007
  • Karlen, Y., Suter, F., Hirt, C., & Merki, K. M. (2019). The role of implicit theories in students’ grit, achievement goals, intrinsic and extrinsic motivation, and achievement in the context a long-term challenging task. Learning and Individual Differences, 74, 101757. https://doi.org/10.1016/j.lindif.2019.101757
  • Kim, D., & Ko, Y. J. (2019). The impact of virtual reality (VR) technology on sport spectators’ flow experience and satisfaction. Computers in Human Behavior, 93, 346–356. https://doi.org/10.1016/j.chb.2018.12.040
  • King, R. B. (2012). How you think about your intelligence influences how adjusted you are: Implicit theories and adjustment outcomes. Personality and Individual Differences, 53(5), 705–709. https://doi.org/10.1016/j.paid.2012.05.031
  • Kline, R. B. (2004). Beyond significance testing. American Psychological Association.
  • Klupp, S., Mohring, W., Lemola, S., & Grob, A. (2021). Relations between fine motor skills and intelligence in typically developing children and children with attention deficit hyperactivity disorder. Research in Developmental Disabilities, 110, 103855. https://doi.org/10.1016/j.ridd.2021.103855
  • Le Foll, D., Rascle, O., & Higgins, N. C. (2008). Attributional feedback-induced changes in functional and dysfunctional attributions, expectations of success, hopefulness, and short-term persistence in a novel sport. Psychology of Sport and Exercise, 9(2), 77–101. https://doi.org/10.1016/j.psychsport.2007.01.004
  • Leung, L. (2019). Exploring the relationship between smartphone activities, flow experience, and boredom in free time. Computers in Human Behavior, 103, 130–139. https://doi.org/10.1016/j.chb.2019.09.030
  • Levine, S. L., Werner, K. M., Capaldi, J. S., & Milyavskaya, M. (2017). Let’s play the blame game: The distinct effects of personal standards and self-critical perfectionism on attributions of success and failure during goal pursuit. Journal of Research in Personality, 71, 57–66. https://doi.org/10.1016/j.jrp.2017.08.005
  • Liu, C.-C., Cheng, Y.-B., & Huang, C.-W. (2011). The effect of simulation games on the learning of computational problem solving. Computers & Education, 57(3), 1907–1918. http://doi.org/10.1016/j.compedu.2011.04.002
  • Mahfouz, A. Y., Joonas, K., & Opara, E. U. (2020). An overview of and factor analytic approach to flow theory in online contexts. Technology in Society, 61, 101228. https://doi.org/10.1016/j.techsoc.2020.101228
  • Makransky, G., Terkildsen, T. S., & Mayer, R. E. (2019). Adding immersive virtual reality to a science lab simulation causes more presence but less learning. Learning and Instruction, 60, 225–236. https://doi.org/10.1016/j.learninstruc.2017.12.007
  • Martınez-Plumed, F., Ferri, C., Hernández-Orallo, J., & Ramírez Quintana, M. J. (2017). A computational analysis of general intelligence tests for evaluating cognitive development. Cognitive Systems Research, 43, 100–118. https://doi.org/10.1016/j.cogsys.2017.01.006
  • Matthews, T. J., Tian, F., & Dolby, T. (2020). Interaction design for paediatric emergency VR training. Virtual Reality & Intelligent Hardware, 2(4), 330–344. https://doi.org/10.1016/j.vrih.2020.07.006
  • McGloin, R., Farrar, K. M., Krcmar, M., Park, S., & Fishlock, J. (2016). Modeling outcomes of violent video game play: Applying mental models and model matching to explain the relationship between user differences, game characteristics, enjoyment, and aggressive intentions. Computers in Human Behavior, 62, 442–451. https://doi.org/10.1016/j.chb.2016.04.018
  • Meyer, O. A., Omdahl, M. K., & Makransky, G. (2019). Investigating the effect of pre-training when learning through immersive virtual reality and video: A media and methods experiment. Computers & Education, 140, 103603. https://doi.org/10.1016/j.compedu.2019.103603
  • Molden, D. C., & Dweck, C. S. (2006). Finding “meaning” in psychology: A lay theories approach to self-regulation, social perception, and social development. American Psychologist, 61(3), 192–203. https://doi.org/10.1037/0003-066X.61.3.192
  • Moran, A. (2004). Attention and concentration training in sport. In Encyclopedia of applied psychology (pp. 209–214). https://doi.org/10.1016/B0-12-657410-3/00800-X
  • 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. https://doi.org/10.1111/j.1365-2729.2006.00170.x
  • Mullins, J. K., & Sabherwal, R. (in press). Gamification: A cognitive-emotional view. Journal of Business Research, https://doi.org/10.1016/j.jbusres.2018.09.023
  • Murray, C. D., & Gordon, M. S. (2001). Changes in bodily awareness induced by immersive virtual reality. CyberPsychology and Behavior, 4(3), 365–371. https://doi.org/10.1089/109493101300210268
  • Nakagawa, S., & Cuthill, I. C. (2007). Effect size, confidence interval and statistical significance: A practical guide for biologists. Biological Reviews, 82(4), 591–605. https://doi.org/10.1111/j.1469-185X.2007.00027.x
  • Neuman, B., & Gray, R. (2013). A direct comparison of the effects of imagery and action observation on hitting performance. Movement & Sport Sciences-Science & Motricite, 79, 11–21. https://doi.org/10.1051/sm/2012034
  • Ommundsen, Y. (2001). Self-handicapping strategies in physical education classes: The influence of implicit theories of the nature of ability and achievement goal orientations. Psychology of Sport and Exercise, 2(3), 139–156. https://doi.org/10.1016/S1469-0292(00)00019-4
  • Pan, F., Ou, Y., Sun, H., & Qian, Y. (2020). Integration of conflict resolution and positive emotions: Electrophysiological evidence. Neuropsychologia, 149, 107661. https://doi.org/10.1016/j.neuropsychologia.2020.107661
  • Passing, D., David, T., & Eshel-Kedmi, G. (2016). Improving children's cognitive modifiability by dynamic assessment in 3D immersive virtual reality environments. Computers & Education, 95, 296–308. https://doi.org/10.1016/j.compedu.2016.01.009
  • Pearce, J. M., Ainley, M., & Howard, S. (2005). The ebb and flow of online learning. Computers in Human Behavior, 21(5), 745–771. https://doi.org/10.1016/S0747-5632(04)00036-6
  • Pekrun, R. (2006). The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational Psychology Review, 18(4), 315–341. https://doi.org/10.1007/s10648-006-9029-9
  • Pekrun, R., Lichtenfeld, S., Marsh, H. W., Murayama, K., & Goetz, T. (2017). Achievement emotions and academic performance: Longitudinal models of reciprocal effects. Child Development, 88(5), 1653–1670. https://doi.org/10.1111/cdev.12704
  • Rascle, O., Le Foll, D., Charrier, M., Higgins, N. C., Rees, T., & Coffee, P. (2015). Durability and generalization of attribution-based feedback following failure: Effects on expectations and behavioral persistence. Psychology of Sport and Exercise, 18, 68–74. https://doi.org/10.1016/j.psychsport.2015.01.003
  • Sankaranarayanan, G., Odlozil, C. A., Wells, K. O., Leeds, S. G., Chauhan, S., Fleshman, J. W., Jones, D. B., & De, S. (in press). Training with cognitive load improves performance under similar conditions in a real surgical task. The American Journal of Surgery, https://doi.org/10.1016/j.amjsurg.2020.02.002
  • Satchell, L., Hoskins, S., Corr, P., & Moore, R. (2017). Ruminating on the nature of intelligence: Personality predicts implicit theories and educational persistence. Personality and Individual Differences, 113, 109–114. https://doi.org/10.1016/j.paid.2017.03.025
  • Schrader, C., & Bastiaens, T. J. (2012). The influence of virtual presence: Effects on experienced cognitive load and learning outcomes in educational computer games. Computers in Human Behavior, 28(2), 648–658. https://doi.org/10.1016/j.chb.2011.11.011
  • Schrader, C., & Grassinger, R. (2021). Tell me that I can do it better. The effect of attributional feedback from a learning technology on achievement emotions and performance and the moderating role of individual adaptive reactions to errors. Computers & Education, 161, 104028. https://doi.org/10.1016/j.compedu.2020.104028
  • Selzer, M. N., Gazcon, N. F., & Larrea, M. L. (2019). Effects of virtual presence and learning outcome using low-end virtual reality systems. Displays, 59, 9–15. https://doi.org/10.1016/j.displa.2019.04.002
  • Shi, Y., Du, J., Ahn, C. R., & Ragan, E. (2019). Impact assessment of reinforced learning methods on construction workers’ fall risk behavior using virtual reality. Automation in Construction, 104, 197–214. https://doi.org/10.1016/j.autcon.2019.04.015
  • Simon, J. R. (2011). The “Simon effect”: A potent behavioral mechanism. Acta Psychology, 136(2), 181. https://doi.org/10.1016/j.actpsy.2010.04.007
  • Škola, F., & Liarokapis, F. (2018). Embodied VR environment facilitates motor imagery brain-computer interface training. Computers & Graphics, 75, 59–71. https://doi.org/10.1016/j.cag.2018.05.024
  • Sullivan, G. M., & Feinn, R. (2012). Using effect size-or why the p value is not enough. Journal of Graduate Medical Education, 4(3), 279–282. https://doi.org/10.4300/JGME-D-12-00156.1
  • Sun, C., Hu, W., & Xu, D. (2019). Navigation modes, operation methods, observation scales and background options in UI design for high learning performance in VR-based architectural applications. Journal of Computational Design and Engineering, 6(2), 189–196. https://doi.org/10.1016/j.jcde.2018.05.006
  • Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1207/s15516709cog1202_4
  • Sweller, J. (2010). Element interactivity and intrinsic, extraneous, and germane cognitive load. Educational Psychology Review, 22(2), 123–138. https://doi.org/10.1007/s10648-010-9128-5
  • Sweller, J., & Chandler, P. (1994). Why some material is difficult to learn. Cognition and Instruction, 12(3), 185–233. https://doi.org/10.1207/s1532690xci1203_1
  • Tabbakh, T., & Freeland-Graves, J. (2016). Development and validation of the multidimensional home environment scale (MHES) for adolescents and their mothers. Eating Behaviors, 22, 76–82. https://doi.org/10.1016/j.eatbeh.2016.03.031
  • Tai, K. H., Hong, J. C., Tsai, C. R., Lin, C. J., & Hwang, M. Y. (2022). Virtual reality for car-detailing skill development: Learning outcomes of procedural accuracy and performance quality predicted by VR self-efficacy, VR using anxiety, VR learning interest and flow experience. Computers & Education, 182, 104458. https://doi.org/10.1016/j.compedu.2022.104458
  • Thompson, C. B., & Panacek, E. A. (2006). Research study designs: Experimental and quasi-experimental. Air Medical Journal, 25(6), 242–246. https://doi.org/10.1016/j.amj.2006.09.001
  • Tu, L., Hao, T., Bi, C., & Xing, G. (2020). Breathcoach: A smart in-home breathing training system with bio-feedback via VR game. Smart Health, 16, 100090. https://doi.org/10.1016/j.smhl.2019.100090
  • Weiner, B. (1976). Attribution theory, achievement motivation, and the educational process. Review of Educational Research, 42(2), 203–215. https://doi.org/10.3102/00346543042002203
  • Williams, H. (2010). Implicit attribution. Journal of Pragmatics, 42(3), 617–636. https://doi.org/10.1016/j.pragma.2009.07.013
  • Williams, R. B. (2018). Conceptual models and mental models in operation: Frustration, performance and flow with two different video game controllers. Entertainment Computing, 28, 2–10. https://doi.org/10.1016/j.entcom.2018.07.004
  • Wolfe, B., Sawyer, B. D., Kosovicheva, A., Reimer, B., & Rosenholtz, R. (2019). Detection of brake lights while distracted: Separating peripheral vision from cognitive load. Attention, Perception, & Psychophysics, 81(8), 2798–2813. https://doi.org/10.3758/s13414-019-01795-4
  • Wright, D. J., Wood, G., Franklin, Z. C., Marshall, B., Riach, M., & Holmes, P. S. (2018). Directing visual attention during action observation modulates corticospinal excitability. Plos One, 13(1), e0190165. https://doi.org/10.1371/journal.pone.0190165
  • Yeager, D. S., & Dweck, C. S. (2012). Mindsets that promote resilience: When students believe that personal characteristics can be developed. Educational Psychologist, 47(4), 302–314. https://doi.org/10.1080/00461520.2012.722805
  • Yeh, Y. C., Lai, S. C., & Lin, C. W. (2016). The dynamic influence of emotions on game-based creativity: An integrated analysis of emotional valence, activation strength, and regulation focus. Computers in Human Behavior, 55, 817–825. https://doi.org/10.1016/j.chb.2015.10.037
  • Yoo, J.-H., & Kim, Y.-J. (2018). Factors influencing nursing students’ flow experience during simulation-based learning. Clinical Simulation in Nursing, 24(C), 1–8. https://doi.org/10.1016/j.ecns.2018.09.001

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