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Intelligent Tutoring Gets Physical: Coaching the Physical Learner by Modeling the Physical World

  • Benjamin GoldbergEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9744)

Abstract

Extending the application of intelligent tutoring beyond the desktop and into the physical world is a sought after capability. If implemented correctly, Artificial Intelligence (AI) tools and methods can be applied to support personalized and adaptive on-the-job training experiences as well as assist in the development of knowledge, skills and abilities (KSAs) across athletics and psychomotor domain spaces. While intelligent tutoring in a physical world is not a traditional application of such technologies, it still operates in much the same fashion as all Intelligent Tutoring Systems (ITS) in existence. It takes raw system interaction data and applies modeling techniques to infer performance and competency while a learner executes tasks within a scenario or defined problem set. While a traditional ITS observes learner interaction and performance to infer cognitive understanding of a concept and procedure, a physical ITS will observe interaction and performance to infer additional components of behavioral understanding and technique. A question the authors address in this paper is how physical interactions can be captured in an ITS friendly format and what technologies currently exist to monitor learner physiological signals and free-form behaviors? Answering the question involves a breakdown of the current state-of-the-art across technologies spanning wearable sensors, computer vision, and motion tracking that can be applied to model physical world components. The breakdown will include the pros and cons of each technology, an example of a domain model the data provided can inform, and the implications the derived models have on pedagogical decisions for coaching and reflection.

Keywords

Intelligent tutoring systems Physical modeling Psychomotor Wearable sensors 

References

  1. 1.
    Kulik, J.A., Fletcher, J.: Effectiveness of intelligent tutoring systems: a meta-analytic review. Rev. Educ. Res. 86(1), 42–78 (2016)CrossRefGoogle Scholar
  2. 2.
    Mohidin, F.: Blooms Taxonomy – The Psychomotor Domain and Mind Mapping (No Date). http://www.mindmaptutor.com/blooms-taxonomy-the-psychomotor-domain-and-mind-mapping. Accessed 8 Jan 2016
  3. 3.
    Ericsson, K.A.: The influence of experience and deliberate practice on the development of superior expert performance. In: Cambridge Handbook of Expertise and Expert Performance, pp. 683–703 (2006)Google Scholar
  4. 4.
    Ericsson, K.A., Krampe, R.T., Tesch-Romer, C.: The role of deliberate practice in the acquisition of expert performance. Psychol. Rev. 100(3), 363–406 (1993)CrossRefGoogle Scholar
  5. 5.
    Fitts, P.M., Posner, M.I.: Human Performance. Brooks/Cole Publishing, Belmont (1967)Google Scholar
  6. 6.
    Goldberg, B., Amburn, C.: The application of GIFT in a psychomotor domain of instruction: a marksmanship use case. In: Proceedings of 3rd Annual GIFT Users Symposium, Orlando, FL (2015)Google Scholar
  7. 7.
    Goldberg, B., Sottilare, R., Brawner, K., Sinatra, A., Ososky, S.: Developing a generalized intelligent framework for tutoring (GIFT): informing design through a community of practice. In: Workshop at the 2015 International Conference on Artificial Intelligence in Education (AIED). Madrid, Spain (2015)Google Scholar
  8. 8.
    Sottlare, R., LaViola, J.: Extending intelligent tutoring beyond the desktop to the psychomotor domain. In: Proceedings of the Interservice/Industry Training Simulation and Education Conference (I/ITSEC), Orlando, FL (2015)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.U.S. Army Research Laboratory-Human Research and Engineering DirectorateOrlandoUSA

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