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Optimizing the Learning Experience: Examining Interactions Between the Individual Learner and the Learning Context

Conference paper
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Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1211)

Abstract

The modern educational environment extends beyond the lecture-based classroom and now involves virtual, simulated, and applied learning contexts. Due to innate individual differences, no learning environment is ideal for all individual learners. Each learner exhibits individual difference factors that can impact one’s involvement, achievement, and satisfaction in learning across different learning contexts. This paper discusses the unique dynamics between individual difference variables and modern learning environments including online classrooms, simulation-based, and applied learning contexts. Recommendations to better support the full range of individual learners are discussed and presented.

Keywords

Individual differences Modern learning environments Learner engagement 

References

  1. 1.
    Schatz, S.: Twenty-five emerging trends in learning and their implications for military partners: an international study. Presented at I/ITSEC 2019, Orlando, FL (2019)Google Scholar
  2. 2.
    Carroll, M., Lindsey, S., Chaparro, M.: Integrating engagement inducing interventions into traditional, virtual and embedded learning environments. In: Sottilare, R., Schwarz, J. (eds.) Adaptive Instructional Systems, HCII 2019. LNCS, vol. 11597. Springer, Cham (2019)Google Scholar
  3. 3.
    Squire, K.D., Jan, M.: Mad city mystery: developing scientific argumentation skills with a place-based augmented reality game on handheld computers. J. Sci. Educ. Technol. 16, 5–29 (2007)CrossRefGoogle Scholar
  4. 4.
    Winsett, C., Foster, C., Dearing, J., Burch, G.: The impact of group experiential learning on student engagement. Acad. Bus. Res. J. 3, 7–17 (2016)Google Scholar
  5. 5.
    Colquitt, J.A., LePine, J.A., Noe, R.A.: Toward an integrative theory of training motivation: a meta-analytic path analysis of 20 years of research. J. Appl. Psychol. 85, 678–707 (2000)CrossRefGoogle Scholar
  6. 6.
    Gully, S., Chen, G.: Individual differences, attribute-treatment interactions, and training outcomes. Learn. Train. Dev. Organ. 3–64 (2010)Google Scholar
  7. 7.
    Noe, R.A., Tews, M.J., Dachner, A.M.: Leaner engagement: a new perspective for enhancing our understanding of learner motivation and workplace learning. Acad. Manag. Ann. 4, 279–315 (2010)CrossRefGoogle Scholar
  8. 8.
    Carroll, M., Lindsey, S., Chaparro, M., Winslow, B.: An applied model of learner engagement and strategies for increasing learner engagement in the modern educational environment. Interact. Learn. Environ. 1–15 (2019)Google Scholar
  9. 9.
    Carroll, M., Rebensky, S., Chaparro, M., Bennett, W., Winslow, B.: The Influence of Individual Factors on Learner Engagement for Simulation-based Learning (under review)Google Scholar
  10. 10.
    Landhauber, A., Keller, J.: Flow and its affective, cognitive, and performance-related consequences. In: Engeser, S. (ed.) Advances in Flow Research, pp. 65–86. Springer, New York (2012)CrossRefGoogle Scholar
  11. 11.
    Hamilton, J.A., Haier, R.J., Buchsbaum, M.S.: Intrinsic enjoyment and boredom coping scales: validation with personality, evoked potential and attention measures. Pers. Individ. Differ. 5, 183–193 (1984)CrossRefGoogle Scholar
  12. 12.
    Hanus, M.D., Fox, J.: Assessing the effects of gamification in the classroom: a logitudinal study on intrinsic motivation, social comparison, satisfaction, effort, and academic performance. Comput. Educ. 80, 152–161 (2015)CrossRefGoogle Scholar
  13. 13.
    Chamorro-Premuzic, T., Furnham, A.: Personality predicts academic performance: evidence from two longitudinal university samples. J. Res. Pers. 37, 319–338 (2003)CrossRefGoogle Scholar
  14. 14.
    Kappe, R., Flier, H.: Predicting academic success in higher education: what’s more important than being smart? Eur. J. Psychol. Educ. 27, 605–619 (2012)CrossRefGoogle Scholar
  15. 15.
    Shih, H., Chen, S.E., Chen, S., Wey, S.: The relationship among tertiary level EFL students’ personality, online learning motivation and online learning satisfaction. Procedia Soc. Behav. Sci. 103(26), 1152–1160 (2013)CrossRefGoogle Scholar
  16. 16.
    Barnett, T., Pearson, A.W., Pearson, R., Kellermanns, F.W.: Five-factor model personality traits as predictors of perceived and actual usage of technology. Eur. J. Inf. Syst. 24(4), 374–390 (2011)CrossRefGoogle Scholar
  17. 17.
    Watjatrakul, B.: Online learning adoption: effects of neuroticism, openness to experience, and perceived values. Interact. Smart Tech. 13(3), 229–243 (2016)Google Scholar
  18. 18.
    Wang, L., Tian, Y., Lei, Y., Zhou, Z.: The influence of different personality traits on learning achievement in three learning situations. LNCS, pp. 475–488 (2017)Google Scholar
  19. 19.
    Keller, H., Karau, S.J.: The importance of personality in students’ perceptions of the online learning experience. Comput. Hum. Behav. 29, 2494–2500 (2013)CrossRefGoogle Scholar
  20. 20.
    Cohen, A., Baruth, O.: Personality, learning, and satisfaction in fully online academic courses. Comput. Hum. Behav. 72, 1–12 (2017)CrossRefGoogle Scholar
  21. 21.
    Pawlowska, D.K.: Student personality, classroom environment, and Student Outcomes: a person-environment fit analysis. Ph.D. thesis (2011)Google Scholar
  22. 22.
    Pawlowska, D.K., Westerman, J.W., Bergman, S.M., Huelsman, T.J.: Student personality, classroom environment, and student outcomes: a person-environment fit analysis. Learn. Individ. Diff. 36, 180–193 (2014)CrossRefGoogle Scholar
  23. 23.
    Codish, D., Ravid, G.: Personality based gamification-educational gamification for extroverts and introverts. In: Proceedings of the 9th CHAIS Conference for the Study of Innovation and Learning Technologies: Learning in the Technological Era, pp. 36–44 (2014)Google Scholar
  24. 24.
    Dunn, K.: Why wait? The influence of academic self-regulation, intrinsic motivation, and statistics anxiety on procrastination in online statistics. Innov. High. Educ. 39(1), 33–44 (2013)CrossRefGoogle Scholar
  25. 25.
    Conrad, D.L.: Engagement, excitement, anxiety, and fear: learner’s experiences of starting an online course. Am. J. Distance Learn. 16, 205–226 (2002)CrossRefGoogle Scholar
  26. 26.
    Rovai, A.P., Ponton, M.K., Wighting, M.J., Baker, J.D.: A comparative analysis of student motivation in traditional classroom and e-learning courses. Int. J. E-Learn. 6, 413–432 (2007)Google Scholar
  27. 27.
    Ong, D., Chan, Y., Cho, W., Koh, T.: Motivation of learning: an assessment of the practicality and effectiveness of gamification with a tertiary education system in Malaysia. In: World Academy of Researchers, Educators, and Scholars in Business, Social Sciences, Humanities, and Education Conference. (2013)Google Scholar
  28. 28.
    Jia, Y., Xu, B., Karanam, Y., Voida, S.: Personality-targets gamification: a survey study on personality traits and motivational affordances. In: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, pp. 2001–2013 (2016)Google Scholar
  29. 29.
    Star, K.: Gamification, Interdependence, and the moderating effect of personality on performance. Doctoral dissertation, Coventry University (2015)Google Scholar
  30. 30.
    Tabak, F., Nguyen, N.T.: Technology acceptance and performance in online learning environments: impact of self-regulation. J. Online Learn. Teach. 9, 116–130 (2013)Google Scholar
  31. 31.
    Denden, M., Tlili, A., Essalmi, F., Jenmi, M.: Educational gamification based on personality. In: IEEE/ACS 14th International Conference on Computer Systems and Applications, pp. 1399–1405 (2017)Google Scholar
  32. 32.
    Komarraju, M., Karau, S.J.: The relationship between the big five personality traits and academic motivation. Pers. Individ. Differ. 39, 557–567 (2005)CrossRefGoogle Scholar
  33. 33.
    Ullén, F., et al.: Proneness for psychological flow in everyday life: associations with personality and intelligence. Personality Individ. Diff. 52, 167–172 (2012)CrossRefGoogle Scholar
  34. 34.
    Buckley, P., Doyle, E.: Individualizing gamification: an investigation of the impact of learning styles and personality traits on the efficacy of gamification using a prediction market. Comput. Educ. 106, 43–55 (2017)CrossRefGoogle Scholar
  35. 35.
    Ludwig, P.M., Nagel, J.K., Lewis, E.J.: Student learning outcomes from a pilot medical innovations course with nursing, engineering, and biology undergraduate students. Int. J. STEM Educ. 4(1), 33 (2017)CrossRefGoogle Scholar
  36. 36.
    LePine, J.A., Dyne, L.V.: Voice and cooperative behavior as contrasting forms of contextual performance: evidence of differential relationships with big five personality characteristics and cognitive ability. J. Appl. Psychol. 86, 326–336 (2001)CrossRefGoogle Scholar
  37. 37.
    Moses, L., et al.: Are math readiness and personality predictive of first-year retention in engineering? J. Psychol. Interdiscip. Appl. 145, 229–245 (2011)CrossRefGoogle Scholar

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Florida Institute of TechnologyMelbourneUSA

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