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Curriculum for Accelerated Learning Through Mindfulness (CALM)

  • Anna SkinnerEmail author
  • Cali Fidopiastis
  • Sebastian Pascarelle
  • Howard Reichel
Conference paper
  • 550 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10916)

Abstract

The military training community is faced with the daunting task of providing each and every warfighter with basic, journeyman and advanced training courses – using media and methodologies that permit rapid, efficient learning and transfer of the learning to a wide range of operational tasks. There is a need for a methodology and metrics to assess the best combinations of learning techniques that can be applied across various types of military training systems and a training testbed with which to assess individual and group characteristics that can accelerate the speed of learning, increase comprehension and retention, and improve transfer of training to performance on operational tasks. Accelerated learning is comprised of two primary components: accelerating the learning pathway and accelerating the learning process. The Curriculum for Accelerated Learning through Mindfulness (CALM) theoretical model seeks to combine these two components and to close the loop between the two, providing real-time correlation of training performance to cognitive state metrics and subsequent adaptation of training content (e.g., complexity/difficulty, modality, scaffolding) in order to maintain an optimal and accelerated state of learning.

Keywords

Accelerated learning Brain sensors and measures Effects of stress & cognitive load on performance Measuring and adapting to individual differences Sensor integration to characterize operator state 

Notes

Acknowledgements

This work was funded by a Phase I Small Business Innovation Research (SBIR) contract (N00178-17-C-1133).

References

  1. Berka, C., Levendowski, D.J., Lumicao, M.N., Yau, A., Davis, G., Zivkovic, V.T., Olmstead, R.E., Tremoulet, P.D., Craven, P.L.: EEG correlates of task engagement and mental workload in vigilance, learning, and memory tasks. Aviat. Space Environ. Med. 78(5), B231–B244 (2007)Google Scholar
  2. Berta, R., Bellotti, F., De Gloria, A., Pranantha, D., Schatten, C.: Electroencephalogram and physiological signal analysis for assessing flow in games. IEEE Trans. Comput. Intell. AI Games 5(2), 164–175 (2013)CrossRefGoogle Scholar
  3. Bishop, S.R.: What do we really know about mindfulness-based stress reduction? Psychosom. Med. 64(1), 71–83 (2002)CrossRefGoogle Scholar
  4. Bishop, S.R., Lau, M., Shapiro, S., Carlson, L., Anderson, N.D., Carmody, J., Segal, Z.V., Abbey, S., Speca, M., Velting, D., Devins, G.: Mindfulness: a proposed operational definition. Clin. Psychol. Sci. Pract. 11(3), 230–241 (2004)CrossRefGoogle Scholar
  5. Csikszentmihalyi, M.: Toward a psychology of optimal experience. In: Flow and the Foundations of Positive Psychology, pp. 209–226. Springer, Dordrecht (2014).  https://doi.org/10.1007/978-94-017-9088-8_14Google Scholar
  6. Eastwood, J.D., Frischen, A., Fenske, M.J., Smilek, D.: The unengaged mind: defining boredom in terms of attention. Assoc. Psychol. Sci. 7(5), 482–495 (2012)Google Scholar
  7. Fidopiastis, C.M.: Theoretical transpositions in brain function and the underpinnings of augmented cognition. In: Schmorrow, D.D., Fidopiastis, C.M. (eds.) FAC 2011. LNCS (LNAI), vol. 6780, pp. 153–158. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-21852-1_19CrossRefGoogle Scholar
  8. Hoffman, R.R., Andrews, D., Fiore, S.M., Goldberg, S., Andre, T., Freeman, J., Fletcher, J.D., Klein, G.: Accelerated learning: prospects, issues and applications. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, Sage, CA, vol. 54, no. 4, pp. 399–402. SAGE Publications, Los Angeles (2010)CrossRefGoogle Scholar
  9. Hou, M., Fidopiastis, C.M.: Untangling operator monitoring approaches when designing intelligent adaptive systems for operational environments. In: Schmorrow, D.D., Fidopiastis, C.M. (eds.) AC 2014. LNCS (LNAI), vol. 8534, pp. 26–34. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-07527-3_3CrossRefGoogle Scholar
  10. Hou, M., Fidopiastis, C.: A generic framework of intelligent adaptive learning systems: from learning effectiveness to training transfer. Theoret. Issues Ergon. Sci. 18(2), 167–183 (2017)CrossRefGoogle Scholar
  11. Jackson, S.A., Marsh, H.W.: Development and validation of a scale to measure optimal experience: the flow state scale. J. Sport Exerc. Psychol. 18(1), 17–35 (1996)CrossRefGoogle Scholar
  12. Kabat-Zinn, J.: Full Catastrophe Living: Using the Wisdom of Your Body and Mind to Face Stress, Pain, and Illness. Delacorte, New York (1990)Google Scholar
  13. Kabat-Zinn, J.: Meditation. In: Holland, J.G. (ed.) Psychoncology, pp. 767–779. Oxford University Press, New York (1998)Google Scholar
  14. Keller, J., Bless, H., Blomann, F., Kleinböhl, D.: Physiological aspects of flow experiences: skills-demand-compatibility effects on heart rate variability and salivary cortisol. J. Exp. Soc. Psychol. 47(4), 849–852 (2011)CrossRefGoogle Scholar
  15. Langer, E.J., Moldoveanu, M.: The construct of mindfulness. J. Soc. Issues 56(1), 1–9 (2000)CrossRefGoogle Scholar
  16. Margraf, J., Zlomuzica, A.: Changing the future, not the past: a translational paradigm shift in treating anxiety. EMBO Rep. 16, 259–260 (2015)CrossRefGoogle Scholar
  17. Mauri, M., Cipresso, P., Balgera, A., Villamira, M., Riva, G.: Why is Facebook so successful? Psychophysiological measures describe a core flow state while using Facebook. Cyberpsychol. Behav. Soc. Netw. 14(12), 723–731 (2011)CrossRefGoogle Scholar
  18. Nacke, L., Lindley, C.A.: Flow and immersion in first-person shooters: measuring the player’s gameplay experience. In: Proceedings of the 2008 Conference on Future Play: Research, Play, Share, pp. 81–88. ACM (2008)Google Scholar
  19. Nicholson, D.M., Fidopiastis, C.M., Davis, L.D., Schmorrow, D.D., Stanney, K.M.: An adaptive instructional architecture for training and education. In: Schmorrow, D.D., Reeves, L.M. (eds.) FAC 2007. LNCS (LNAI), vol. 4565, pp. 380–384. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-73216-7_43CrossRefGoogle Scholar
  20. Palmer, E.D., Kobus, D.A.: The future of augmented cognition systems in education and training. In: Schmorrow, D.D., Reeves, L.M. (eds.) FAC 2007. LNCS (LNAI), vol. 4565, pp. 373–379. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-73216-7_42CrossRefGoogle Scholar
  21. Parasuraman, R.: Designing automation for human use: empirical studies and quantitative models. Ergonomics 43(7), 931–951 (2000)CrossRefGoogle Scholar
  22. Posner, M.I.: Orienting of attention. Q. J. Exp. Psychol. 32(1), 3–25 (1980)MathSciNetCrossRefGoogle Scholar
  23. Sciarini, L.W., Nicholson, D.: Assessing cognitive state with multiple physiological measures: a modular approach. In: Schmorrow, D.D., Estabrooke, I.V., Grootjen, M. (eds.) FAC 2009. LNCS (LNAI), vol. 5638, pp. 533–542. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-642-02812-0_62CrossRefGoogle Scholar
  24. Segal, Z.V., Teasdale, J.D., Williams, J.M., Gemar, M.C.: The mindfulness-based cognitive therapy adherence scale: inter-rater reliability, adherence to protocol and treatment distinctiveness. Clin. Psychol. Psychother. 9(2), 131–138 (2002)CrossRefGoogle Scholar
  25. Shapiro, S.L., Carlson, L.E., Astin, J.A., Freedman, B.: Mechanisms of mindfulness. J. Clin. Psychol. 62(3), 373–386 (2006)CrossRefGoogle Scholar
  26. Shapiro, S.L., Schwartz, G.E.: Intentional systemic mindfulness: an integrative model for self-regulation and health. Adv. Mind-Body Med. 16, 128–134 (2000)Google Scholar
  27. St. John, M., Kobus, D.A., Morrison, J.G.: DARPA augmented cognition technical integration experiment (TIE), no. TR-1905. Pacific Science and Engineering Group, Inc., San Diego (2003)Google Scholar
  28. Teasdale, J.D.: Emotional processing, three modes of mind and the prevention of relapse in depression. Behav. Res. Ther. 37, S53–S77 (1999)CrossRefGoogle Scholar
  29. Teper, R., Inzlicht, M.: Meditation, mindfulness and executive control: the importance of emotional acceptance and brain-based performance monitoring. Soc. Cogn. Affect. Neurosci. 8(1), 85–92 (2013)CrossRefGoogle Scholar
  30. Vartak, A.A., Fidopiastis, C.M., Nicholson, D.M., Mikhael, W.B., Schmorrow, D.: Cognitive state estimation for adaptive learning systems using wearable physiological sensors. In: BIOSIGNALS (2), pp. 147–152 (2008)Google Scholar
  31. Vygotsky, L.: Zone of proximal development. Mind Soc.: Dev. High. Psychol. Process. 5291, 157 (1987)Google Scholar
  32. Wickens, C.D., Hollands, J.G.: Attention, time-sharing, and workload. Eng. Psychol. Hum. Perform. 3, 439–479 (2000)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Anna Skinner
    • 1
    Email author
  • Cali Fidopiastis
    • 1
  • Sebastian Pascarelle
    • 2
  • Howard Reichel
    • 2
  1. 1.Design Interactive, Inc.OrlandoUSA
  2. 2.In-Depth Engineering Corp.FairfaxUSA

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