Next Generation Learner Modeling by Theory of Mind Model Induction

Klaus P. Jantke, Bernd Schmidt, Rosalie Schnappauf

2016

Abstract

Learning is a spectrum of involved processes requiring the learner’s engagement and building upon the learner’s prior knowledge and other prerequisites. Educators know how to adapt to their learners’ needs and desires. User modeling is a key technology to enable digital systems such as e-learning environments and serious games to adapt to their users’s peculiarities. There is a huge corpus of scientific research on user modeling, on implementation of user modeling and related system adaptivity, and on the impact on teaching and learning. The aim of the present contribution is to go even further. The concept of theories of mind is adopted and adapted from animal behavioral research. Theory of mind user models allow for the identification and representation of user/learner/player peculiarities beyond the limits of all other preceding approaches to user modeling. Theory of mind learner models allow for the representation of higher quality profiles describing, for instances, intention, misconceptions, or even fear. The acquisition of suchlike expressive profiles is an inductive learning process of the digital system. The inductive inference of learner profiles requires particular concepts and algorithms. An implementation serves as proof of concept.

References

  1. Angluin, D. (1980). Inductive inference of formal languages from positive data. Information and Control, 45:117-135.
  2. Arabatzis, T. and Kindi, V. (2013). The problem of conceptual change in the philosophy and history of science. In (Vosniadou, 2013b), pages 343-359.
  3. Arnold, S., Fujima, J., Jantke, K. P., Karsten, A., and Simeit, H. (2013). Game-based training for executive staff of professional disaster management: Storyboarding adaptivity of game play. In Tan, D., editor, Proceedings of the International Conference on Advanced Information and Communication Technology for Education (ICAICTE 2013), Sept. 20-22, 2013, Hainan, China, pages 68-73. Atlantis Press.
  4. Barke, H.-D., Hazari, A., and Yitbarek, S. (2009). Misconceptions in Chemistry. Addressing Perceptions in Chemical Education. Springer.
  5. Blackburn, P., de Rijke, M., and Venema, Y. (2001). Modal Logic. Cambridge, U.K.: Cambridge University Press.
  6. Bläsius, K. H. and Bürckert, H.-J., editors (1978). Deduktionssysteme. Automatisierung des logischen Denkens. München, Wien: Oldenbourg.
  7. Briggs Myers, I. and Briggs, K. (1980). Gifts Differing: Understanding Personality Types. Mountain View, CA: Davies-Black.
  8. Brown, D. E. and Hammer, D. (2013). Conceptual change in physics. In (Vosniadou, 2013b), pages 121-137.
  9. Brusilovsky, P. (2001). Adaptive hypermedia. User Modeling and User-Adapted Interaction, 11(1-2):87-110.
  10. Brusilovsky, P. and Millán, E. (2007). User models for adaptive hypermedia and adaptive educational systems. In Brusilovsky, P., Kobsa, A., and Neijdl, W., editors, The Adaptive Web. Methods and Strategies of Web Personalization, volume 4321 of LNCS, chapter 1, pages 3-53. Berlin & Heidelberg: SpringerVerlag.
  11. Brusilovsky, P., Specht, M., and Weber, G. (1995). Towards adaptive learning environments. In GI Jahrestagung 1995, pages 322-329.
  12. Call, J. and Tomasello, M. (2008). Does the chimpanzee have a theory of mind? 30 years later. Trends in Cognitive Sciences, 12(5):187-192.
  13. Carberry, S., Weibelzahl, S., Micarelli, A., and Semeraro, G., editors (2013). User Modeling, Adaptation, and Personalization. Proc. 21th International Conf., UMAP 2013, Rome, Italy, June 2013. Number 7899 in LNCS. Springer.
  14. Carey, S. (1985). Conceptual Change in Childhood. Cambridge, MA, USA: The MIT Press.
  15. Carey, S. (2000). Science education as conceptual change. J. Applied Developmental Psychology, 21:13-19.
  16. Carruthers, P. and Smith, Peter, K., editors (1996). Theories of Theories of Mind. Cambridge, U.K.: Cambridge University Press.
  17. Chi, M., Slotta, J. D., and de Leeuw, N. (1994). From things to processes: A theory of conceptual change. Learning and Instruction, 4:27-43.
  18. Clayton, N. S., Emery, N. J., and Dickinson, A. (2006). The rationality of animal memory: Complex caching strategies of western scrub jays. In Hurley, S. and Nudds, M., editors, Rational Animals?, pages 197- 216. Oxford, UK: Oxford University Press.
  19. Clocksin, W. F. and Mellish, C. S. (1981). Programming in Prolog. Berlin, Heidelberg, New York etc.: Springer.
  20. Davis, B., Sumara, D., and Luce-Kapler, R. (2000). Engaging Minds: Learning and Teaching in a Complex World. Mahwah, NJ, USA: Lawrence Erlbaum.
  21. De Bra, P., Kobsa, A., and Chin, D., editors (2010). User Modeling, Adaptation, and Personalization. Proc. 18th International Conf., UMAP 2010, Big Island, HI, USA, June 2010. Number 6075 in LNCS. Springer.
  22. Dimitrova, V., Kuflik, T., Chin, D., Ricci, F., Dolog, P., and Houben, G.-J., editors (2014). User Modeling, Adaptation, and Personalization. Proc. 22nd International Conf., UMAP 2014, Aalborg, Denmark, July 2014. Number 8538 in LNCS. Springer.
  23. diSessa, A. A. and Sherin, B. L. (1998). What changes in conceptual change? International Journal of Science Education, 20(10):1155-1191.
  24. Doyle, A. C. (1915). The adventure of the dancing men. In The Return of Sherlock Holmes. London: Smith, Elder & Co., 3.1 edition.
  25. Egenfeldt-Nielsen, S. (2007). Educational Potential of Computer Games. Continuum Studies in Education. London, New Delhi, New York, Sydney: Bloomsbury Publishing, formerly Continuum Intl. Publ. Group.
  26. Emery, N. J. (2004). Are corvids 'feathered apes'? Cognitive evolution in crows, jays, rooks and jackdaws. In Watanabe, S., editor, Comparative Analysis of Minds, pages 181-213. Tokyo: Keio University Press.
  27. Emery, N. J. and Clayton, N. S. (2009). Comparative social cognition. Annual Review of Psychology, 60:87-113.
  28. Emery, N. J., Dally, J. M., and Clayton, N. S. (2004). Western scrub-jays (Aphelocoma californica) use cognitive strategies to protect their caches from thieving conspecifics. Animal Cognition, 7:37-43.
  29. Felder, R. M. and Silverman, L. K. (1988). Learning and teaching styles in engineering education. Engineering education, 78(7):674-681.
  30. Fujima, J. and Jantke, K. P. (2012). The potential of the direct execution paradigm: Toward the exploitation of media technologies for exploratory learning of abstract content. In Urban, B. and Müsebeck, P., editors, eLearning Baltics 2012: Proceedings of the 5th International eLBa Science Conference, pages 33-42. Fraunhofer Verlag.
  31. Gaudl, S., Jantke, K. P., and Woelfert, C. (2009). The good, the bad and the ugly: Short stories in short game play. In Iurgel, I., Zagalo, N., and Petta, P., editors, Proceedings of the 2nd International Conference on Digital Storytelling, Dec. 9-11, 2009, Erfurt, Germany, number 5915 in LNCS, pages 127-133. Springer-Verlag Berlin Heidelberg 2009.
  32. Goldman, A. I. (2006). Simulating Minds: The Philosophy, Psychology, and Neuroscience of Mindreading. New York, NY: Oxford University Press.
  33. Grieser, G. (2008). Reflective inductive inference of recursive ffnctions. Theoretical Computer Science, 397(1- 3):57-69.
  34. Grieser, G. and Jantke, K. P. (1995). Ansätze zur Reflexion in der Induktiven Inferenz. Studie der Forschungsgruppe Algorithmisches Lernen, HTWK Leipzig (FH), FB Informatik, Mathematik & Naturwissenschaften. Studie #02/95, Version 1.0.
  35. Hammer, D. (1996). Misconceptions or P-prims: How may alternative perspectives of cognitive structure influence instructional perceptions and intentions? The Journal of the Learning Sciences, 5(2):97-127.
  36. Hopcroft, J. E. and Ullman, J. D. (1979). Introduction to Automata Theory, Languages, and Computation. Boston: Addison-Wesley.
  37. Houben, Geert-Jan ANDMcCalla, G., Pianesi, F., and Zancanaro, M., editors (2009). User Modeling, Adaptation, and Personalization. Proc. 17th International Conf., UMAP 2009, Trento, Italy, June 2009. Number 5535 in LNCS. Springer.
  38. Jain, S., Osherson, D., Royer, J. S., and Sharma, A. (1999). Systems That Learn. Cambridge, MA, USA: The MIT Press.
  39. Jantke, K. P. (1994). Towards reflecting inductive inference machines. GOSLER Report 24/93, HTWK Leipzig (FH), FB Informatik, Mathematik & Naturwissenschaften.
  40. Jantke, K. P. (1995). Reflecting and self-confident inductive inference machines. In Jantke, K. P., Shinohara, T., and Zeugmann, T., editors, Proc. 6th International Workshop on Algorithmic Learning Theory (ALT'95), October 18-20, 1995, Fukuoka, Japan, volume 997 of LNAI, pages 282-297. Springer-Verlag.
  41. Jantke, K. P. (2010). The Gorge approach. Digital gamecontrol and play for playfully developing technology competence. In Cordeiro, J., Shishkov, B., Verbraeck, A., and Helfert, M., editors, CSEDU 2010. 2nd International Conference on computer Supported Education, Proc., Vol. 1, Valencia, Spain, April 7-10, 2010, pages 411-414. INSTICC.
  42. Jantke, K. P., Beick, H.-R., Brovko, Y., and Drefahl, S. (2013). Refinement of adaptivity by reflection. In Yetongnon, K., Dipanda, A., and Chbeir, R., editors, 9th International Conference on Signal Image Technology & Internet-based Systems, Dec. 2-5, 2013, Kyoto, Japan, pages 309-316. IEEE.
  43. Jantke, K. P., Grieser, G., Lange, S., and Memmel, M. (2004). DaMiT: Data Mining lernen und lehren. In Lernen, Wissensentdeckung und Adaptivität (LWA-2004), Fachgruppentreffen Maschinelles Lernen (FGML), 4.-6. Oktober 2004, Berlin.
  44. Jantke, K. P., Hoppe, I., Lengyel, D., and Neumann, A. (2010). Time to play Gorge - Time to learn AI: A qualitative study. In Hambach, S., Martens, A., Tavangarian, D., and Urban, U., editors, eLearning Baltics 2010, Proc. 3rd Intl. eLBa Science Conference, pages 99-110. Fraunhofer Verlag.
  45. Jantke, K. P. and Hume, T. (2015). Effective learning through meaning construction in digital role playing games. In IEEE International Conference on Con-
  46. Jantke, K. P. and Knauf, R. (2005). Didactic design through storyboarding: Standard concepts for standard tools. In Proc. 4th Intl. Symp. on Information and Communication Technologies, Cape Town, South Africa, January 3-6, 2005, pages 20-25. Computer Science Press, Trinity College Dublin, Ireland.
  47. Jantke, K. P., Memmel, M., Rostanin, O., Thalheim, B., and Tschiedel, B. (2003). Decision support by learningon-demand. In CAiSE Workshop 2003, Klagenfurt, Osterreich.
  48. Jantke, K. P. and Schulz, A. (2011). Adaptivity in moodle beyond the limits of adaptivity in moodle. In Verbraeck, A., Helfert, M., Cordeiro, J., and Shishkov, B., editors, 3rd International Conference on Computer Supported Education, CSEDU 2011, May 6-8, 2011, Noordwijkerhout, The Netherlands, pages 418- 421. INSTICC.
  49. Jung, C. G. (1921). Psychologische Typen. Zürich: Rascher Verlag.
  50. Kassak, O., Kompan, M., and Bielikova, M. (2015). User preference modeling by global and individual weights for personalized recommendation. Acta Polytechnica Hungarica, 12(8):27-41.
  51. Kayoko, I. and Hatano, G. (2013). Conceptual change in nïve biology. In (Vosniadou, 2013b), pages 195-219.
  52. Knauf, R., Sakurai, Y., Tsuruta, S., and Jantke, K. P. (2010). Modeling didactic knowledge by storyboarding. Journal of Educational Computing Research, 42(4):355- 383.
  53. Kolb, D. and Fry, R. E. (1975). Towards an applied theory of experiential learning. In Cooper, C. L., editor, Theories of Group Processes, pages 33-58.
  54. Kolb, D. A. (1984). Experiential Learning: Experience as the Source of Learning and Development. Upper Saddle River, New Jersey: Prentice Hall.
  55. Konstan, J. A., Conejo, R., Marzo, J. L., and Oliver, N., editors (2011). User Modeling, Adaptation, and Personalization. Proc. 19th International Conf., UMAP 2011, Girona, Spain, July 2011. Number 6787 in LNCS. Springer.
  56. Koster, R. (2005). A Theory of Fun for Game Design. Scottsdale, AZ, USA: Paraglyph Press.
  57. Krebs, J. and Jantke, K. P. (2014). Methods and technologies for wrapping educational theory into serious games. In Zvacek, S., Restivo, M. T., Uhomoibhi, J., and Helfert, M., editors, Proceedings of the 6th International Conference on Computer Supported Education, CSEDU 2014, Barcelona, Spain, May 1-3, 2014, pages 497-502. SCITEPRESS.
  58. Leinhardt, G. and Ravi, A. (2013). Changing historical conception of history. In (Vosniadou, 2013b), pages 343- 359.
  59. MacBeth, D. (2000). On an apparatus for conceptual change. Science Education, 84(2):228-264.
  60. Masthoff, J., Mobasher, B., Desmarais, M. C., and Nkambou, R., editors (2012). User Modeling, Adaptation, and Personalization. Proc. 20th International Conf.,
  61. UMAP 2012, Montreal, Canada, July 2012. Number
  62. Mauer, M. C. (2012). Das Konstrukt der Theory of Mind bei Erwachsenen. PhD thesis, LMU München, Fak. Psychologie und Pädagogik.
  63. Phillips, W. (1991). Earth science misconceptions. The Science Teacher, 58(2):21.
  64. Popper, K. (1934). Logik der Forschung. Tübingen.
  65. Ricci, F., Bontcheva, K., Conlan, O., and Lawless, S., editors (2015). User Modeling, Adaptation, and Personalization. Proc. 23rd International Conf., UMAP 2015, Dublin, Ireland, June/July 2015. Number 9146 in LNCS. Springer.
  66. Richter, M. M. (1978). Logikkalküle. Stuttgart: Teubner.
  67. Rogers jr., H. (1967). Theory of Recursive Functions and Effective Computability. Hoboken, NJ, USA: McGraw-Hill.
  68. Schewe, K.-D., Thalheim, B., and Tretiakov, A. (2007). Formalisation of user preferences, obligations and rights. In Kaschek, R. H., editor, Intelligent Assistent Systems.Concepts, Techniques and Technologies, chapter VI, pages 114-143. Hershey, London, Melbourne, Singapore: Idea Group Publ.
  69. Schmidt, B. (2014). Theory of Mind Player Modeling. Konzeptentwicklung, Implementierung und Erprobung mit logischer Programmierung. Bachelor thesis, FH Erfurt - University of Applied Sciences, Angewandte Informatik.
  70. Sipser, M. (1997). Introduction to the Theory of Computation. Boston, MA, USA: PWS Publ. Co.
  71. Smith, A., Min, W., Mott, B. W., and Lester, J. C. (2015). Diagrammatic student models: Modeling student drawing performance with deep learning. In Ricci, F., Bontcheva, K., Conlan, O., and Lawless, S., editors, User Modeling, Adaptation, and Personalization. Proc. 23rd International Conf., UMAP 2015, Dublin, Ireland, June/July 2015, pages 216-227.
  72. Specht, M. and Weber, G. (1997). Kognitive Lernermodellierung. Kognitionswissenschaft, 6(4):165-176.
  73. Sterling, L. and Shapiro, E. (1986). The Art of Prolog. Cambridge, MA, USA: The MIT Press.
  74. Suddendorf, T. (2007). The evolution of foresight: What is mental time travel and is it unique to humans? Behavioral and Brain Sciences, 30:299-313.
  75. Thagard, P. (2012). The Cognitive Science of Science: Explanation, Discovery, and Cognitive Change. Cambridge, MA, USA: The MIT Press.
  76. Thagard, P. (2013). Conceptual change in the history of science: Life, mind, and disease. In (Vosniadou, 2013b), pages 360-374.
  77. Vosniadou, S. (2013a). Conceptual change in learning and instruction: The framework theory approach. In (Vosniadou, 2013b), pages 1-30.
  78. Vosniadou, S., editor (2013b). International Handbook of Research on Conceptual Change. Second Edition. New York, Milton Park: Routledge.
  79. Wisniak, J. (2004). Phlogistion: The rise and fall of a theory. Indian Journal of Chemical Technology, 11:732- 743.
Download


Paper Citation


in Harvard Style

Jantke K., Schmidt B. and Schnappauf R. (2016). Next Generation Learner Modeling by Theory of Mind Model Induction . In Proceedings of the 8th International Conference on Computer Supported Education - Volume 1: CSEDU, ISBN 978-989-758-179-3, pages 499-506. DOI: 10.5220/0005903804990506


in Bibtex Style

@conference{csedu16,
author={Klaus P. Jantke and Bernd Schmidt and Rosalie Schnappauf},
title={Next Generation Learner Modeling by Theory of Mind Model Induction},
booktitle={Proceedings of the 8th International Conference on Computer Supported Education - Volume 1: CSEDU,},
year={2016},
pages={499-506},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005903804990506},
isbn={978-989-758-179-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 8th International Conference on Computer Supported Education - Volume 1: CSEDU,
TI - Next Generation Learner Modeling by Theory of Mind Model Induction
SN - 978-989-758-179-3
AU - Jantke K.
AU - Schmidt B.
AU - Schnappauf R.
PY - 2016
SP - 499
EP - 506
DO - 10.5220/0005903804990506