Advertisement

Mobile Learning – vom Werkzeug zum Assistenten

  • Klaus P. JantkeEmail author
Chapter

Zusammenfassung

Die Digitalisierung nahezu aller Lebensbereiche ist ein unaufhaltsamer Trend; was aber Anwendern und Anwenderinnen zugemutet wird, ist häufig schlecht. Das liegt unter anderem daran, dass die Notwendigkeit einer paradigmatischen Transformation von digitalen Werkzeugen zu digitalen Assistenzsystemen nicht verstanden, nicht gewollt und/oder nicht beherrscht wird. Digitale Assistenz schließt Künstliche Intelligenz ein, insbesondere die Lernfähigkeit von Computersystemen. Ein Assistenzsystem, das Besonderheiten seiner Benutzer und Benutzerinnen – seien es Bedürfnisse, Vorlieben, Absichten, Irrtümer, Ziele, Stärken, Schwächen oder andere Eigenheiten – erlernen kann, ist auf dieser Basis in der Lage, sich an seine Benutzenden zu adaptieren. Jeder gute Lehrer und jede gute Lehrerin verhält sich den Lernenden gegenüber adaptiv. Systeme, die das menschliche Lernen unterstützen, können das auch. Im Fokus steht die Fähigkeit des digitalen Systems, Modelle seiner Benutzenden – User Models im allgemeinen, Player Models, Learner Models – induktiv zu lernen. Wenn der Ansatz lernerzentriert ist, sind derartige Modelle deklarativ, so dass Lernende sie einsehen, verstehen, ggf. korrigieren und ihr Verhalten daran orientieren können.

Schlüsselwörter

Assistenzsysteme Adaptive Systeme Künstliche Intelligenz Benutzer- Spieler- und Lernermodellierung Induktives Lernen Pervasive Games Pervasive Learning Storyboarding 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Literatur

  1. Arnold, O. (1996). Die Therapiesteuerungskomponente einer wissensbasierten Systemarchitektur für Aufgaben der Prozeßführung. St. Augustin.Google Scholar
  2. Arnold, O., & Jantke, K. P. (1996). Planning is learning. In W. Dilger, M. Schlosser, J. Zeidler &. A. Ittner (Eds.), Machine Learning, 1996 Annual Meeting of the Special Interest Group of Machine Learning of the German Computer Science Society (GI), Chemnitzer Informatik-Berichte CSR-96-06. Chemnitz, 12-17.Google Scholar
  3. Arnold, O., Jantke, K. P., & Spundflasch, S. (2013). Hierarchies of pervasive games by storyboarding. In Proceedings of the 5th International Games Innovation Conference (IGIC), Sept. 23-25, 2013, Vancouver, BC, Canada.  https://doi.org/10.1109/igic.2013.6659165
  4. Arnold, O., Drefahl, S., Fujima, J., & Jantke, K. P. (2017). Co-operative knowledge discovery based on meme media, natural language processing and theory of mind modeling and induction. In P. Kommers & P. Isaias (Hrsg.), Proceedings of the 15th International Conference e-Society 2017, Budapest, Hungary, 10-12 April, 2017. Sétubal, 27-38.Google Scholar
  5. Arnold, S., Fujima, J., & Jantke, K. P. (2013). Storyboarding serious games for large-scale training applications. In O. Foley, M. T. Restivo, J. Uhomoibhi & M. Helfert (Eds.), Proceedings of the 5th Internationel Conference on Computer Supported Education, CSEDU 2013. Aachen, Germany, May 6-8, 2013.  https://doi.org/10.5220/0004415606510655
  6. Arnold, S., Fujima, J., Jantke, K. P., Karsten, A., & Simeit, H. (2013). Game-based training for executive staff of professional disaster management: Storyboarding adaptivity of game play. In D. Tan (Ed.), Proceedings of the 2013 International Conference on Advanced ICT and Education. Advances in Intelligent Systems Research, Vol. 33.  https://doi.org/10.2991/icaicte.2013.14
  7. Becker, M. (2011). Entwicklung und Implementierung technologischer Verfahren zur Realisierung situationsbezogener Adaptivität bei der Bereitstellung digitaler Medien im schulischen Umfeld. Diplomarbeit. Ilmenau: Technische Universität.Google Scholar
  8. Bergin, J., Chandler, J., Eckstein, J., Manns, M. L., Marquardt, K., Sharp, H., Slipos, M., Völter, M., & Wallingford, E. (2012). Pedagogical Patterns: Advice for Educators. New York/NY.Google Scholar
  9. Blackburn, P., de Rijke, M., & Venema, Y. (2001). Modal Logic. Cambridge.Google Scholar
  10. Borchers, J. (2007). Pervasive game. Forschung & Lehre 31, 30.Google Scholar
  11. de Bra, P., Kobsa, A., & Chin, D. (Hrsg.) (2010). User Modeling, Adaptation, and Personalization. 18th International Conference, UMAP 2010, Big Island, HI, USA, June 20-24, 2010, Proceedings LNCS Vol. 6075. Berlin, Heidelberg.Google Scholar
  12. Briggs Myers, I. (1985). Manual: A Guide to the Development and Use of the Myers-Briggs Type Indicator. Palo Alto/CA.Google Scholar
  13. Brusilovsky, P., & Millán, E. (2007). User models for adaptive hypermedia and adaptive educational systems. In P. Brusilovsky, A. Kobsa & W. Nejdl (Eds.), The Adaptive Web. Methods and Strategies of Web Personalization. LNCS, Vol. 7899. Berlin, Heidelberg, 3-53.Google Scholar
  14. Carberry, S., Weibelzahl, S., Micarelli, A., & Semeraro, G. (Eds.) (2013). User Modeling, Adaptation, and Personalization. 21th International Conference, UMAP 2013, Rome, Italy, June 10-14, 2013 Proceedings. LNCS, Vol. 7899. Berlin, Heidelberg.Google Scholar
  15. Carey, S. (1985). Conceptual Change in Childhood. Cambridge/MA.Google Scholar
  16. Carruthers, P., & Smith, P. K. (1996). Theories of Theories of Mind. Cambridge/MA.Google Scholar
  17. Cennano, K. S. (1993). Learning from video: Factors influencing learners’ preconceptions and invested mental effort. Educational Technology Research and Development 41, 33-45.Google Scholar
  18. Chalmers, M., Bell, M., Brown, B., Hall, M., Sherwood, S., & Tennent, P. (2005). Gaming on the edge: Using seams in ubicomp games. In Proceedingsof the 2005 ACM SIGCHI International Conference on Advances in Computer Entertainment Technology, Polytechnic University of Valencia, Spain.  https://doi.org/10.1145/1178477.1178533
  19. Clocksin, W. F., & Mellish, C. S. (1991). Programming in Prolog. Berlin, Heidelberg.Google Scholar
  20. Csikszentmihály, M. (2010). Das Flow-Erlebnis. Jenseits von Angst und Langeweile: im Tun aufgehen. Stuttgart.Google Scholar
  21. Dimitrova, V., Kuflik, T., Chin, D., Ricci, F., Dolog, P., & Houben, G.-J., (Eds.) (2014). User Modeling, Adaptation, and Personalization. 22nd International Conference, UMAP 2014, Aalborg, Denmark, July 7-11, 2014. Proceedings. LNCS, Vol. 8538. Berlin, Heidelberg.Google Scholar
  22. Driver, R., Squires, A., Rushworth, P., & Wood-Robinson, V. (1994). Making Sense of Secondary Science – Research into children’s ideas. London.Google Scholar
  23. Duit, R. (1986). Wärmevorstellungen. Naturwissenschaften im Unterricht – Physik/Chemie 34, 30-33.Google Scholar
  24. Düsel, H. (2007). Konzeption und Realisierung von Methoden der formalen Verifikation von Storyboards. TU Ilmenau, Fakultät für Wirtschaftswissenschaften.Google Scholar
  25. Emery, N. J., Dally, J. M., & Clayton, N. S. (1994). Western scrub-jays (aphelocoma californica) use cognitive strategies to protect their caches from thieving conspecifics. Animal Cognition 7, 37-43.Google Scholar
  26. Fikes, R. E., Hart, P. E., & Nilsson, N. J. (1972). Learning and executing generalized robot plans. Artificial Intelligence 3, 251-288.Google Scholar
  27. Fujima, J., Jantke, K. P., & Arnold, S. (2013). Digital game playing as storyboard interpretation. In Proceedings of the 5th International Games Innovation Conference (IGIC), Sept. 23-25, 2013, Vancouver, BC, Canada.  https://doi.org/10.1109/igic.2013.6659163
  28. Grieser, G., & Lange, S. (2007). Interaction scenarios for information extraction. In R. H. Kaschek (Ed.), Intelligent Assistant Systems: Concepts, Techniques and Technologies. Hershey, London, Melbourne, Singapore, 1-14.Google Scholar
  29. Göth, C., & Schwabe, G. (2011). Mobiles Lernen. In J. Haake, G. Schwabe & M. Wessner (Hrsg.), CSCL-Kompendium. Lehr- und Handbuch zum computerunterstützten kooperativen Lernen. München, 283-293.Google Scholar
  30. Guo, Z.-S., & Tanaka, Y. (2011). A component-based 3D geographic simulation framework and its integration with a legacy GIS. In G. Kreuzberger, A. Lunzer & R. H. Kaschek (Eds.), Interdisciplinary Advances in Adaptive and Intelligent Assistant Systems: Concepts, Techniques, Applications, and Use. Hershey/PA, 63-81.Google Scholar
  31. Hawlitschek, A. (2013). Spielend lernen. Didaktisches Design digitaler Lernspiele zwischen Spielmotivation und Cognitive Load. Berlin.Google Scholar
  32. Heers, R. (2005). Being There. Untersuchungen zum Wissenserwerb in Virtuellen Umgebungen. Dissertation. Tübingen: Eberhard-Karls-Universität Tübingen.Google Scholar
  33. Houben, G.-J., McCalla, G.I., Pianesi, F., & Zancanaro, M., (Eds.) (2009). User Modeling, Adaptation, and Personalization. 17th International Conference, UMAP 2009, formerly UM and AH, Trento, Italy, June 22-26, 2009, Proceedings. LNCS, Vol. 5535. Berlin, Heidelberg.Google Scholar
  34. Hutter, D., Jantke, K. P., Mantel, H., Rock, G., & Stephan, W. (2000). Automated seasoning for system verification. In H. Kern (Hrsg.), IWK 2000, 45. Internationales Wissenschaftliches Kolloquium, 4.-6.10.2000, TU Ilmenau. Ilmenau, 787-792.Google Scholar
  35. Jain, S., Osherson, D., Royer, J. S., & Sharma, A. (1999). Systems That Learn. Cambridge/MA.Google Scholar
  36. Jantke, K. P. (2005). Informatik und Künstliche Intelligenz – Beiträge zur Adaptivität einer kommenden Generation intelligenter eLearning-Systeme. In C. Igel & R. Daugs (Hrsg.), Handbuch eLearning. Münster, 49-69.Google Scholar
  37. Jantke, K. P. (2012). Theory of Mind Induction in User Modeling: An Introductory Game Case Study. Report-Series of the Children’s Media Department of the Fraunhofer Institute for Digital Media Technology IDMT.Google Scholar
  38. Jantke, K. P. (2013). Pedagogical patterns and didactic memes for memetic design by educational storyboarding. In O. Arnold, W. Spickermann, N. Spyratos & Y. Tanaka (Eds.), Webble Technology, First Webble World Summit, WWS 2013, Erfurt, Germany, June 2013. Communications in Computer and Information Science, Vol. 372. Berlin, Heidelberg, 143-154.Google Scholar
  39. Jantke, K. P. (2015). Time Travel Games – ein Konzept zur Kriminalprävention durch faszinierende Spielerlebnisse. Deutscher Präventionstag. http://www.praeventionstag.de/dokumentation/download.cms?id=2284&datei=1000-Jantke_Time-Travel_F3091-2284.pdf. Zugegriffen: 15. September 2017.
  40. Jantke, K. P., Arnold, O., & Spundflasch, S. (2013). Aliens on the bus: A family of pervasive games. In IEEE 2nd Global Conference on Consumer Electronics, Oct. 1-4, 2013, Makuhari Messe, Tokyo, Japan.  https://doi.org/10.1109/gcce.2013.6664866
  41. Jantke, K. P., Grieser, G., Lange, S., & Memmel, M. (2004). DaMiT: Data mining lehren und lernen. In A. Abecker, S. Bickel, U. Brefeld, I. Drost, N. Henze, O. Herden, M. Minor, T. Scheffer, L. Stojanovic & S. Weibelzahl (Hrsg.): LWA 2004: Lernen – Wissensentdeckung –Adaptivität, Berlin, 4.-6. Oktober 2004, Workshopwoche der GI-Fachgruppen/Arbeitskreise (1) Fachgruppe Adaptivität und Benutzermodellierung in Interaktiven Softwaresystemen (ABIS 2004), (2) Arbeitskreis Knowledge Discovery (AKKD 2004), (3) Fachgruppe Maschinelles Lernen (FGML 2004), (4) Fachgruppe Wissens- und Erfahrungsmanagement (FGWM 2004). Berlin, 171-179.Google Scholar
  42. Jantke, K. P., Igel, C., & Sturm, R. (2007). From e-learning tools to assistants by learner modelling and adaptive behavior. In R. H. Kaschek (Ed.), Intelligent Assistant Systems: Concepts, Techniques and Technologies. Hershey, London, Melbourne, Singapore, 212-231.Google Scholar
  43. Jantke, K. P., & Knauf, R. (2005). Didactic design through storyboarding: Standard concepts for standard tools. In B. R. Baltes, L. Edwards, F. Galindo, J. Hvorecky, K. P. Jantke, L. Jololian, … & J. Waldron (Eds.), Proceedings of the 4th International Symposium on Information and Communication Technologies, Cape Town, South Africa, Jan. 3rd-6th, 2005. Dublin, 20-25.Google Scholar
  44. Jantke, K. P., & Lamonova, N. (2007). Assistance and induction: The therapy planning case. In R. H. Kaschek (Ed.), Intelligent Assistant Systems: Concepts, Techniques and Technologies. Hershey, London, Melbourne, Singapore, 15-32.Google Scholar
  45. Jantke, K. P., Schmidt, B., & Schnappauf, R. (2016). Next generation learner modeling by theory of mind model induction. In J. O. Umohoibhi, G. Costagliola, S. Zvacek & B. M. McLaren (Eds.), 8th International Conference on Computer Supported Education, CSEDU 2016, Rome, Italy, April 21-23, 2016, Vol. 1. Sétubal: 499-506.Google Scholar
  46. Jantke, K. P., & Spundflasch, S. (2013). Understanding pervasive games for purposes of learning. In O. Foley, M. T. Restivo, J. Uhomoibh & M. Helfert (Eds.), Proceedings of the 5th International. Conference. on Computer Supported Education (CSEDU), May 6-8, 2013, Aachen, Germany.  https://doi.org/10.5220/0004413006960701
  47. Jeschke, S., & Richter, T. (2007). Mathematics in virtual knowledge spaces: User adaptation by intelligent assistants. In R. H. Kaschek (Ed.), Intelligent Assistant Systems: Concepts, Techniques and Technologies. Hershey, London, Melbourne, Singapore: 232-263.Google Scholar
  48. Jones, N. D., Sestoft, P., & Gomard, C. K. (1993). Partial Evaluation and Automatic Program Generation. New York/NY.Google Scholar
  49. Jung, C. G. (1921). Psychologische Typen. Zürich.Google Scholar
  50. Kaschek, R. H. (Ed.). (2007). Intelligent Assistant Systems: Concepts, Techniques and Technologies. Hershey, London, Melbourne, Singapore.Google Scholar
  51. Kassak, O., Kompan, M., & Bielikova, M. (2015). User preference modeling by global and individual weights for personalized recommendation. Acta Polytechnica Hungaria 12, 27-41.Google Scholar
  52. Kirschner, P. A., & Kirschner, F. (2012). Mental Effort. In N. M. Scheel (Ed.), Encyclopedia of the Sciences of Learning. Berlin, Heidelberg, New York/NY, 2182-2184.Google Scholar
  53. Knauf, R., Sakurai, Y., Takada, K., & Tsuruta, S. (2012). A case study on using personalized data mining for university curricula. In 2012 IEEE International Conference. on Systems, Man, and Cybernetics (SMC).  https://doi.org/10.1109/icsmc.2012.6378259
  54. Konstan, J. A., Conejo, R., Marzo, J. L., & Oliver, N., (Eds.) (2011). User Modeling, Adaptation, and Personalization. 19th International Conference, UMAP 2011, Girona, Spain, July 11-15, 2011. LNCS, Vol. 6787. Berlin, Heidelberg.Google Scholar
  55. Kreutzberger, G., Lunzer, A., & Kaschek, R. H. (Eds.). (2011). Interdisciplinary Advances in Adaptive and Intelligent Assistant Systems: Concepts, Techniques, Applications, and Use. Hershey/PA.Google Scholar
  56. Krotz, F. (2007). Mediatisierung. Fallstudien zum Wandel von Kommunikation. Wiesbaden.Google Scholar
  57. Krumpholz, A. (2007). Building a virtual trainer for an immersive haptic virtual reality environment. In R. H. Kaschek (Ed.), Intelligent Assistant Systems: Concepts, Techniques and Technologies. Hershey, London, Melbourne, Singapore, 264-279.Google Scholar
  58. Lehman, M. M., & Ramil, J. F. (2002). Software evolution and software evolution processes. Annals of Software Engineering 14, 275-309.Google Scholar
  59. Lenerz, C. (2009). Layered languages of ludology – Eine Fallstudie. In A. Beyer & G. Kreuzberger (Hrsg.), Digitale Spiele – Herausforderung und Chance. Boitzenburg, 39-52.Google Scholar
  60. Lindström, P. (1969). On extensions of elementary logic. Theoria 35, 1-11.Google Scholar
  61. Masthoff, J., Mobasher, B., Desmarais, M. C., & Nkambou, R., (Eds.) (2012). User Modeling, Adaptation, and Personalization. 20th International Conference, UMAP 2012, Montreal, Canada, July 16-20, 2012 Proceedings. LNCS, Vol. 7379. Berlin, Heidelberg.Google Scholar
  62. Memmel, M., Ras, E., Jantke, K. P., & Yacci, M. (2007). Approaches to learning object oriented instructional design. In A. Koohang & K. Harman (Eds.), Learning Objects and Instructional Design. Santa Rosa/CA, 281-326.Google Scholar
  63. Montola, M., & Stenros, J. (2009). The beast. In M. Montola, J. Stenros & A. Wærn (Eds.), Pervasive Games: Theory and Design. Burlington/MA, 25-30.Google Scholar
  64. Montola, M., Stenros, J., & Wærn, A. (2009). Pervasive Games: Theory and Design. Burlington/MA.Google Scholar
  65. Motelet, O., & Baloinan, N. (2004). Introducing learning management systems standards in the classroom. In Proceedings of the IEEE International Conference on Advanced Learning Technologies, ICALT’04, Aug. 30 – Sept. 1, 2004.  https://doi.org/10.1109/icalt.2004.1357641
  66. Müller, V. C., & Bostrom, N. (2016). Future progress in Artificial Intelligence: A survey of expert opinion. In V. C. Müller (Ed.), Fundamental Issues of Artificial Intelligence. Synthese Library 376. Cham (CH), 555-572.Google Scholar
  67. Paas, F. (1992). Training strategies for attaining transfer of problem-solving skill in statistics. A cognitive load approach. Journal of Educational Psychology 84, 429-434.Google Scholar
  68. Prensky, M. (2001). Digital Game-Based Learning. New York/NY.Google Scholar
  69. Ricci, F., Bontcheva, K., Conlan, O., & Lawless, S., (Eds.) (2015). User Modeling, Adaptation, and Personalization. 23rd International Conference, UMAP 2015, Dublin, Ireland, June 29 – July 3, 2015. Proceedings. LNCS, Vol. 9146. Berlin, Heidelberg.Google Scholar
  70. Richter, M. M. (1978). Logikkalküle. Stuttgart.Google Scholar
  71. Salomon, G. (1983). The differential investment of mental effort in learning from different sources. Educational Psychologist 18, 42-50.Google Scholar
  72. Schäfer, K. (1984). Von der Alltagserfahrung zur Newtonschen Mechanik. physica didactica 11, 197-203.Google Scholar
  73. Schneider, J., & Kortuem, G. (2001). How to Host a Pervasive Game – Supporting Face-to-Face Interactions in Live-Action Roleplaying. In Position paper at the Designing Ubiquitous Computing Games Workshop at UbiComp. http://www.academia.edu/2814099/How_to_host_a_pervasive_game. Zugegriffen: 21.September 2017.
  74. Smith, A., Min, W., Mott, B. W., & Lester, J. C. (2015). Diagrammatic student models: Modeling student drawing performance with deep learning. In F. Ricci, K. Bontcheva, O. Conlan & S. Lawless (Eds.), User Modeling, Adaptation, and Personalization. 23rd International Conference, UMAP 2015, Dublin, Ireland, June 29 – July 3, 2015. Proceedings. LNCS, Vol. 9146. Berlin, Heidelberg, 216-227.Google Scholar
  75. Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science 12, 257-285.Google Scholar
  76. Takada, K., Miyazawa, Y., Yamamoto, Y., Imada, Y., Tsuruta, S., & Knauf, R. (2013). Curriculum optimization by correlation analysis and its validation. In A. Holzinger & G. Pasi (Eds.), Human-Computer Interaction and Knowledge Discovery in Complex, Unstructured, Big Data. Special Session on Smart Learning Environments. LNCS 7947, Berlin and Heidelberg, 311-318.Google Scholar
  77. Thagard, P. (2012). The Cognitive Science of Science: Explanation, Discovery, and Conceptual Change. Cambridge/MA.Google Scholar
  78. Tsuruta, S., Knauf, R., Dohi, S., Kawabe, T., & Sakurai, Y. (2013). An intelligent system for modeling and supporting academic educational processes. In A. P. Alaya (Ed.), Intelligent and Adaptive Educational-Learning Systems: Achievements and Trends. Berlin, Heidelberg, 469-496.Google Scholar
  79. Vassileva, J., Blustein, J., Aroyo, L., & D’Mello, S., (Eds.) (2016). User Modeling, Adaptation, and Personalization. Proceedings of the 24th International Conference UMAP 2016, Halifax, Canada. New York/NY.Google Scholar
  80. Vosniadou, S. (Ed.) (2008). International Handbook of Research on Conceptual Change. London.Google Scholar
  81. Wallace, M. (2006). In celebration of the inner rouge. the escapist 30, 4-6.Google Scholar
  82. Wanke, E. (1994). On the decidability of certain integer subgraph problems on context-free graph languages. Information and Computation 113, 26-49.Google Scholar
  83. Whitton, N. (2009). Learning with digital games: A practical guide to engaging students in higher education. London.Google Scholar
  84. Winter, J. (2016). Konzeption eines Time Travel Prevention Games für die Einbruchsprävention. Masterarbeit, Hochschule Furtwangen, Fakultät Digitale Medien.Google Scholar
  85. Winter, J., & Jantke, K. P. (2014). Formal Concepts and Methods Fostering Creative Thinking in Digital Games Design. In 3rd IEEE Global Conference on Consumer Electronics (GCCE 2014), Oct. 7-10, 2014, Makuhari Messe, Tokyo, Japan.  https://doi.org/10.1109/gcce.2014.7031153
  86. Winter, M., & Pemberton, L. (2010). Unearthing invisible buildings: Device focus and device sharing in a collaborative mobile learning activity. In D. Parsons (Ed.), Innovations in Mobile Educational Technologies and Applications. Hershey/PA, 77-95.Google Scholar
  87. Yamamoto, Y, Knauf, R., Miyazawa, Y., & Tsuruta, S. (2015). Increasing the sensitivity of a personalized educational data mining method for curriculum composition. In G. Chen, V. Kumar, Kinshuk, R. Huang & S. C. Kong (Eds.), Emerging Issues in Smart Learning. Lecture Notes in Educational Technology. Berlin, Heidelberg, 189-196.Google Scholar

Copyright information

© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2018

Authors and Affiliations

  1. 1.WeimarDeutschland

Personalised recommendations