User Modeling and User-Adapted Interaction

, Volume 25, Issue 4, pp 371–424 | Cite as

Evaluation of topic-based adaptation and student modeling in QuizGuide

Article

Abstract

This paper presents an in-depth analysis of a nonconventional topic-based personalization approach for adaptive educational systems (AES) that we have explored for a number of years in the context of university programming courses. With this approach both student modeling and adaptation are based on coarse-grained knowledge units that we called topics. Our motivation for the topic-based personalization was to enhance AES transparency for both teachers and students by utilizing typical topic-based course structures as the foundation for designing all aspects of an AES from the domain model to the end-user interface. We illustrate the details of the topic-based personalization technology, with the help of the Web-based educational service QuizGuide—the first system to implement it. QuizGuide applies the topic-based personalization to guide students to the right learning material in the context of an undergraduate C programming course. While having a number of architectural and practical advantages, the suggested coarse-grained personalization approach deviates from the common practices toward knowledge modeling in AES. Therefore, we believe that several aspects of QuizGuide required a detailed evaluation—from modeling accuracy to the effectiveness of adaptation. The paper discusses how this new student modeling approach can be evaluated, and presents our attempts to evaluate it from multiple different prospects. The evaluation of QuizGuide across several consecutive semesters demonstrates that, although topics do not always support precise user modeling, they can provide a basis for successful personalization in AESs.

Keywords

Adaptive educational hypermedia Student modeling Adaptive navigation support Adaptive link annotation Topic-based personalization Adaptive system evaluation 

References

  1. Ahn, J.-W., Brusilovsky, P., He, D., Grady, J., Li, Q.: Personalized Web Exploration with Task Models. In: Proceedings of the 17th international conference on World Wide Web, WWW ’08, Beijing, China, April 21–25, 2008, ACM, pp. 1–10 (2008)Google Scholar
  2. Anderson, J.R., Corbett, A.T., Koedinger, K.R., Pelletier, R.: Cognitive tutors: lessons learned. J. Learn. Sci. 4(2), 167–207 (1995)CrossRefGoogle Scholar
  3. Apted, T., Kay, J., Lum, A.: Supporting metadata creation with an ontology built from an extensible dictionary. In: De Bra, P., Nejdl, W. (eds.) Proceedings of 3rd International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems (AH’2004), pp. 4–13. Springer, Berlin, 23–26 August 2004Google Scholar
  4. Aroyo, L., Dicheva, D.A.: Concept-based approach to support learning in a Web-based support environment. In: Moore, J.D., Redfield, C.L., Johnson, W.L. (eds.) Proceedings of AI-ED’2001, pp. 1–12. IOS Press, Amsterdam (2001)Google Scholar
  5. Baker, R.S., Corbett, A.T., Aleven, V.: More accurate student modeling through contextual estimation of slip and guess probabilities in Bayesian knowledge tracing. In: Woolf, B., Aïmeur, E., Nkambou, R., Lajoie, S. (eds.) Proceedings of the 9th International Conference on Intelligent Tutoring Systems, Montreal, Canada, pp. 406–415. Springer, Berlin, 23–27 June 2008Google Scholar
  6. Baker, R.S.J.d., Corbett, A.T., Gowda, S.M., Wagner, A.Z., MacLaren, B.A., Kauffman, L.R., Mitchell, A.P., Giguere, S.: Contextual slip and prediction of student performance after use of an intelligent tutor. In: De Bra, P., Kobsa, A., Chin, D. (eds.) Proceedings of 18th International Conference on User Modeling, Adaptation, and Personalization (UMAP 2009), pp. 52–63. Springer, Big Island, HI, 22–24 June 2010Google Scholar
  7. Baker, R.S.J.d., Pardos, Z.A., Gowda, S.M., Nooraei, B.B., Heffernan, N.T.: Ensembling predictions of student knowledge within intelligent tutoring systems. In: Proceedings of 19th International Conference on User Modeling, Adaptation, and Personalization, UMAP 2011, Girona, Spain, pp. 13–24. Springer, 11–15 July 2011Google Scholar
  8. Beck, J., Chang, K.-M., Mostow, J., Corbett, A.: Does help help? Introducing the Bayesian evaluation and assessment methodology. In: Woolf, B., Aïmeur, E., Nkambou, R., Lajoie, S. (eds.) Proceedings of the 9th International Conference on Intelligent Tutoring Systems, Montreal, Canada, pp. 383–394. Springer, Berlin, 23–27 June 2008Google Scholar
  9. Brown, J.S., Burton, R.R.: Diagnostic models for procedural bugs in basic mathematical skills. Cogn. Sci. 2, 155–192 (1978)CrossRefGoogle Scholar
  10. Brusilovsky, P.: Adaptive hypermedia. User Model. User Adapt. Interact. 11(1/2), 87–110 (2001)MATHCrossRefGoogle Scholar
  11. Brusilovsky, P.: Developing adaptive educational hypermedia systems: from design models to authoring tools. In: Murray, T., Blessing, S., Ainsworth, S. (eds.) Authoring Tools for Advanced Technology Learning Environments: Toward cost-effective adaptive, interactive, and intelligent educational software, pp. 377–409. Kluwer, Dordrecht (2003)CrossRefGoogle Scholar
  12. Brusilovsky, P.: Knowledgetree: a distributed architecture for adaptive e-learning. In: Proceedings of 13th International World Wide Web Conference, WWW 2004, pp. 104–113. ACM Press, New York, NY, 17–22 May 2004Google Scholar
  13. Brusilovsky, P.: Adaptive navigation support. In: Brusilovsky, P., Kobsa, A., Neidl, W. (eds.) The Adaptive Web: Methods and Strategies of Web Personalization. Lecture Notes in Computer Science, vol. 4321, pp. 263–290. Springer, Berlin (2007)Google Scholar
  14. Brusilovsky, P.: Adaptive hypermedia for education and training. In: Durlach, P., Lesgold, A. (eds.) Adaptive Technologies for Training and Education, pp. 46–68. Cambridge University Press, Cambridge (2012)CrossRefGoogle Scholar
  15. Brusilovsky, P., Cooper, D.W.: Domain, Task, and user models for an adaptive hypermedia performance support system. In: Gil, Y., Leake, D.B. (eds.) Proceedings of 2002 International Conference on Intelligent User Interfaces, pp. 23–30. ACM Press, San Francisco, CA 13–16 January 2002Google Scholar
  16. Brusilovsky, P., Eklund, J.: A study of user-model based link annotation in educational hypermedia. J. Univ. Comput. Sci. 4(4), 429–448 (1998)Google Scholar
  17. Brusilovsky, P., Eklund, J., Schwarz, E.: Web-based education for all: a tool for developing adaptive courseware. In: Ashman, H., Thistewaite, P. (eds.) Proceedings of 7th International World Wide Web Conference, pp. 291–300. Elsevier Science B. V., Brisbane, Australia, 14–18 April 1998Google Scholar
  18. Brusilovsky, P., Karagiannidis, C., Sampson, D.: Layered evaluation of adaptive learning systems. Int. J. Contin. Eng. Educ. Lifelong Learn. 14(4/5), 402–421 (2004)CrossRefGoogle Scholar
  19. Brusilovsky, P., Knapp, J., Gamper, J.: Supporting teachers as content authors in intelligent educational systems. Int. J. Knowl. Learn. 2(3/4), 191–215 (2006)CrossRefGoogle Scholar
  20. Brusilovsky, P., Millán, E.: User models for adaptive hypermedia and adaptive educational systems. In: Brusilovsky, P., Kobsa, A., Neidl, W. (eds.) The Adaptive Web: Methods and Strategies of Web Personalization. Lecture Notes in Computer Science, vol. 4321, pp. 3–53. Springer, Berlin (2007)CrossRefGoogle Scholar
  21. Brusilovsky, P., Sosnovsky, S.: Engaging students to work with self-assessment questions: a study of two approaches. In: Proceedings of 10th Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE’2005, pp. 251–255. ACM Press, Monte de Caparica, Portugal, 27–29 June 2005aGoogle Scholar
  22. Brusilovsky, P., Sosnovsky, S.: Individualized exercises for self-assessment of programming knowledge: an evaluation of QuizPACK. ACM Journal on Educational Resources in Computing 5(3): Article no. 6 (2005b)Google Scholar
  23. Brusilovsky, P., Sosnovsky, S., Lee, D., Yudelson, M., Zadorozhny, V., Zhou, X.: Learning SQL programming with interactive tools: from integration to personalization. ACM Transactions on Computing Education 9 (4). Article No. 19, 1–15 (2010)Google Scholar
  24. Brusilovsky, P., Sosnovsky, S., Shcherbinina, O.: User modeling in a distributed e-learning architecture. In: Ardissono, L., Brna, P., Mitrovic, A. (eds.) Proceedings of 10th International User Modeling Conference, pp. 387–391. Springer, Berlin, 24–29 July 2005aGoogle Scholar
  25. Brusilovsky, P., Sosnovsky, S., Yudelson, M., Chavan, G.: Interactive authoring support for adaptive educational systems. In: Looi, C.-K., McCalla, G., Bredeweg, B., Breuker, J. (eds.) Proceedings of 12th International Conference on Artificial Intelligence in Education, AIED’2005, pp. 96–103. IOS Press, Amsterdam, 18–22 July 2005bGoogle Scholar
  26. Bull, S.: Supporting learning with open learner models. In: Proceedings of 4th Hellenic Conference on Information and Communication Technologies in Education, pp. 47–61. Athens, Greece, September 29–October 3, 2004Google Scholar
  27. Carbonell, J.R.: AI in CAI: an artificial intelligence approach to computer aided instruction. IEEE Trans. Man-Mach. Syst. MMS 11(4), 190–202 (1970)CrossRefGoogle Scholar
  28. Carmona, C., Bueno, D., Guzmán, E., Conejo, R.: SIGUE: making web courses adaptive. In: De Bra, P., Brusilovsky, P., Conejo, R. (eds.) Proceedings of Second International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems (AH’2002), May 29–31, 2002, Springer, Málaga, Spain, pp. 376–379 (2002)Google Scholar
  29. Chin, D.: Empirical evaluations of user models and user-adapted systems. User Model. User Adapt. Interact. 11(1–2), 181–194 (2001)MATHCrossRefGoogle Scholar
  30. Conati, C., Gertner, A., Vanlehn, K.: Using Bayesian networks to manage uncertainty in student modeling. User Model. User Adapt. Interact. 12(4), 371–417 (2002)MATHCrossRefGoogle Scholar
  31. Conejo, R., Guzman, E., Millán, E.: SIETTE: a web-based tool for adaptive teaching. Int. J. Artif. Intell. Educ. 14(1), 29–61 (2004)Google Scholar
  32. Corbett, A., McLaughlin, M., Scarpinatto, C.: Modeling student knowledge: cognitive tutors in high school and college. User Model. User Adapt. Interact. 10(2–3), 81–108 (2000)CrossRefGoogle Scholar
  33. Corbett, A.T., Anderson, J.R.: Student modeling and mastery learning in a computer-based programming tutor. In: Frasson, C., Gauthier, G., McCalla, G. (eds.) Proceedings of Second International Conference on Intelligent Tutoring Systems, ITS’92, pp. 413–420. Springer, Berlin, 10–12 June 1992Google Scholar
  34. Corbett, A.T., Anderson, J.R.: Knowledge tracing: modelling the acquisition of procedural knowledge. User Model. User Adapt. Interact. 4(4), 253–278 (1995)CrossRefGoogle Scholar
  35. Corbett, A.T., Anderson, J.R., Carver, V.H., Brancolini, S.A.: Individual differences and predictive validity in student modeling. In: Ram, A., Eiselt, K. (eds.) Proceedings of the 16th Annual Conference of the Cognitive Science Society. pp. 457–464. Lawrence Erlbaum, Edinburgh, 23–27 August 1993aGoogle Scholar
  36. Corbett, A.T., Anderson, J.R., O’Brien, A.T.: The predictive validity of student modeling in the ACT programming tutor. In: Brna, P., Ohlsson, S., Pain, H. (eds.) Proceedings of AI-ED’93, World Conference on Artificial Intelligence in Education, pp. 457–464. AACE, Charlottesville, Edinburgh, 23–27 August 1993bGoogle Scholar
  37. Cristea, A., Aroyo, L.: Adaptive authoring of adaptive educational hypermedia. In: De Bra, P., Brusilovsky, P., Conejo, R. (eds.) Proceedings of Second International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems (AH’2002), pp. 122–132. Springer, Berlin, 29–31 May 2002Google Scholar
  38. Davidovic, A., Warren, J., Trichina, E.: Learning benefits of structural example-based adaptive tutoring systems. IEEE Trans. Educ. 46(2), 241–251 (2003)CrossRefGoogle Scholar
  39. De Bra, P.: Pros and cons of adaptive hypermedia in web-based education. J. CyberPsychol. Behav. 3(1), 71–77 (2000)CrossRefGoogle Scholar
  40. De Bra, P., Aerts, A., Berden, B., de Lange, B., Rousseau, B., Santic, T., Smits, D., Stash, N.: AHA! The adaptive hypermedia architecture. In: Proceedings of the 14th ACM Conference on Hypertext and Hypermedia, pp. 81–84. Nottingham, UK, ACM (2003)Google Scholar
  41. De Bra, P., Ruiter, J.-P.: AHA! Adaptive hypermedia for all. In: Fowler, W., Hasebrook, J. (eds.) Proceedings of WebNet’2001, World Conference of the WWW and Internet, pp. 262–268. AACE, Orlando, FL, 23–27 October 2001Google Scholar
  42. De Bra, P., Smits, D., van der Sluijs, K., Cristea, A., Foss, J., Glahn, C., Steiner, C.: GRAPPLE: learning management systems meet adaptive learning environments. In: Peña-Ayala, A. (ed.) Intelligent and Adaptive Educational-Learning Systems, vol. 17, pp. 133–160. Springer, Berlin (2013)CrossRefGoogle Scholar
  43. Eliot, C., Neiman, D., Lamar, M.: Medtec: A Web-based intelligent tutor for basic anatomy. In: Lobodzinski, S., Tomek, I. (eds.) Proceedings of WebNet’97, World Conference of the WWW, Internet and Intranet, pp. 161–165. AACE, Toronto, Canada, 1–5 November 1997Google Scholar
  44. Falakmasir, M.H., Pardos, Z.A., Gordon, G.J., Brusilovsky, P.: A spectral learning approach to knowledge tracing. In: D’Mello, S.K., Calvo, R.A., Olney, A. (eds.) Proceedings of the 6th International Conference on Educational Data Mining (EDM 2010), pp. 28–34. Memphis, TN, USA, 6–9 July 2013Google Scholar
  45. Farzan, R., Brusilovsky, P.: AnnotatEd: a social navigation and annotation service for web-based educational resources. New Rev. Hypermedia Multimed 14(1), 3–32 (2008)CrossRefGoogle Scholar
  46. Fogarty, J., Baker, R., Hudson, S.: Case studies in the use of ROC curve analysis for sensor-based estimates in human computer interaction. Proc. Grap. Interface 2005, 129–136 (2005)Google Scholar
  47. Gena, C. Weibelzahl, S.: Usability engineering for the adaptive web. In: Brusilovsky, P., Kobsa, A., Neidl, W. (eds.): The Adaptive Web: Methods and Strategies of Web Personalization. Lecture Notes in Computer Science, Vol. 4321, pp. 720–762. Springer, Berlin (2007)Google Scholar
  48. Goldstein, I.P.: The genetic graph: a representation for the evolutionof procedural knowledge. Int. J. Man-Mach. Stud. 11(1), 51–77 (1979)CrossRefGoogle Scholar
  49. Goldstein, I.P., Carr, B.: The computer as coach: an athletic paradigm for intelligent education. In: Proceedings of 1977 Annual ACM Conference, Seatle, pp. 227–233. October (1977)Google Scholar
  50. González-Brenes, J.P., Huang, Y., Brusilovsky, P.: General features in knowledge tracing to model multiple subskills, temporal item response theory, and expert knowledge. In: Stamper, J., Pardos, Z., Mavrikis, M., McLaren, B.M. (eds.) Proceedings of the 7th International Conference on Educational Data Mining (EDM 2014), London, UK, pp. 84–91. 4–7 July 2014Google Scholar
  51. Hatala, M., Gasevic, D., Siadaty, M., Jovanovic, J., Torniai, C.: Can educators develop ontologies using ontology extraction tools: an end-user study. In: Cress, U., Dimitrova, V., Specht, M. (eds.) Proceedings of 4th European Conference on Technology Enhanced Learning (ECTEL 2009), Nice, France, pp. 127–139. Springer, Berlin, 29 September–2 October 2009Google Scholar
  52. Heathcote, A., Brown, S., Mewhort, D.J.K.: The power law repealed: the case for an exponential law of practice. Psychon. Bull. Rev. 7(2), 185–207 (2000)CrossRefGoogle Scholar
  53. Henze, N., Naceur, K., Nejdl, W., Wolpers, M.: Adaptive hyperbooks for constructivist teaching. Künstliche Intell. 4, 26–31 (1999)Google Scholar
  54. Henze, N., Nejdl, W.: Student modeling for KBS Hyperbook system using Bayesian networks, Technical report, Report, University of Hannover (1999)Google Scholar
  55. Hothi, J., Hall, W., Sly, T.: A study comparing the use of shaded text and adaptive navigation support in adaptive hypermedia. In: Brusilovsky, P., Stock, O., Strapparava, C. (eds.) Proceedings of Adaptive Hypermedia and Adaptive Web-based systems, pp. 335–342. Springer, Berlin, 28–30 August 2000Google Scholar
  56. Hovland, C.I., Lumsdaine, A.A., Sheffield, F.D.: A baseline for measurement of percentage change. In: Hovland, C.I., Lumsdaine, A.A., Sheffield, F.D. (eds.) Experiments on Mass Communication. Wiley, New York (1949)Google Scholar
  57. Hsiao, I.-H., Sosnovsky, S., Brusilovsky, P.: Guiding students to the right questions: adaptive navigation support in an E-Learning system for Java programming. J. Comput. Assist. Learn. 26(4), 270–283 (2010)CrossRefGoogle Scholar
  58. Jameson, A.: Numerical uncertainty management in user and student modeling: an overview of systems and issues. User Model. User Adapt. Interact. 5(3–4), 193–251 (1996)CrossRefGoogle Scholar
  59. Jameson, A.: Modeling both the context and the user. Pers. Technol. 5(1), 29–33 (2001)Google Scholar
  60. Karagiannidis, C., Sampson, D.G.: Layered evaluation of adaptive applications and servers. In: Brusilovsky, P., Stock, O., Strapparava, C. (eds.) Proceedings of Adaptive Hypermedia and Adaptive Web-based systens, pp. 343–346. Springer, Berlin, 28–30 August 2000Google Scholar
  61. Käser, T., Koedinger, K.R., Gross, M.: Different parameters - same prediction: An analysis of learning curves. In: Stamper, J., Pardos, Z., Mavrikis, M., McLaren, B.M. (eds.) Proceedings of the 7th International Conference on Educational Data Mining (EDM 2014), London, UK, pp. 52–59. 4–7 July 2014Google Scholar
  62. Kavcic, A.: Fuzzy user modeling for adaptation in educational hypermedia. IEEE Trans. Syst. Man Cybern. 34(4), 439–449 (2004)CrossRefGoogle Scholar
  63. Keane, M., O’Brien, M., Smyth, B.: Are people biased in their use of search engines? Commun. ACM 51(2), 49–52 (2008)CrossRefGoogle Scholar
  64. Knutov, E., De Bra, P., Pechenizkiy, M.: AH 12 years later: a comprehensive survey of adaptive hypermedia methods and techniques. New Rev. Hypermed. Multimed. 15(1), 5–38 (2009)CrossRefGoogle Scholar
  65. Koedinger, K., Mathan, S.: Distinguishing qualitatively different kinds of learning using log files and learning curves. In: Mostow, J., Tedesco, P. (eds.) Proceedings of TS 2004 Log Analysis Workshop, pp. 39–46. Maceio, Brazi (2004)Google Scholar
  66. Koedinger, K.R., Anderson, J.R., Hadley, W.H., Mark, M.A.: Intelligent tutoring goes to school in the big city. Int. J. Artif. Intell. Educ. 8, 30–43 (1997)Google Scholar
  67. Koedinger, K.R., McLaughlin, E.A., Stamper, J.: Automated Student Model Improvement. In: Yacef, K., Zaïane, O., Hershkovitz, H., Yudelson, M., Stamper, J. (eds.) Proceedings of the 5th International Conference on Educational Data Mining, pp. 17–24. Chania, Greece (2012)Google Scholar
  68. Lawless, S., Hederman, L., Wade, V.: Enhancing Access to Open Corpus Educational Content: Learning in the Wild. In: Proceedings of the 19th ACM Conference on Hypertext & Hypermedia, Pittsburgh, Pennsylvania, USA, pp. 167–174 (2008)Google Scholar
  69. Martin, B., Koedinger, K.R., Mitrovic, A., Mathan, S.: On using learning curves to evaluate ITS. In: Looi, C.-K., McCalla, G., Bredeweg, B., Breuker, J. (eds.) Proceedings of 12th International Conference on Artificial Intelligence in Education, AIED’2005, pp. 419–426. IOS Press, Amsterdam, July 18–22, 2005Google Scholar
  70. Martin, B., Mitrovic, A., Koedinger, K., Mathan, S.: Evaluating and improving adaptive educational systems with learning curves. User Model. User Adapt. Interact. 21(3), 249–283 (2011)CrossRefGoogle Scholar
  71. Mitrovic, A.: An intelligent SQL tutor on the web. Int. J. Artif. Intell. Educ. 13(2–4), 173–197 (2003)Google Scholar
  72. Mitrovic, A., Ohlsson, S.: Evaluation of a constraint-based tutor for a database language. Int. J. Artif. Intell. Educ. 10(304), 238–256 (1999)Google Scholar
  73. Murre, J., Chessa, A.: Power laws from individual differences in learning and forgetting: mathematical analyses. Psychon. Bull. Rev. 18(3), 592–597 (2011)CrossRefGoogle Scholar
  74. Newell, A., Rosenbloom, P.S.: Mechanisms of skill acquisition and the law of practice. In: Anderson, J.R. (ed.) Cognitive Skills and their Acquisition, pp. 1–51. Lawrence Erlbaum Associates Inc, Hillsdale, NJ (1981)Google Scholar
  75. Oberlander, J., O’Donell, M., Mellish, C., Knott, A.: Conversation in the museum: experiments in dynamic hypermedia with the intelligent labeling explorer. New Rev. Multimed. Hypermed. 4, 11–32 (1998)CrossRefGoogle Scholar
  76. Ohlsson, S.: Constraint-based student modeling. J. Artif. Intell. Educ. 3(4), 429–447 (1992)Google Scholar
  77. Olston, C., Chi, E.H.: ScentTrails: integrating browsing and searching on the Web. ACM Trans. Comput. Hum. Interact. 10(3), 177–197 (2003)CrossRefGoogle Scholar
  78. Papanikolaou, K.A., Grigoriadou, M., Kornilakis, H., Magoulas, G.D.: Personalising the interaction in a web-based educational hypermedia system: the case of inspire. User Model. User Adapt. Interact. 13(3), 213–267 (2003)CrossRefGoogle Scholar
  79. Paramythis, A., Weibelzahl, S.: A decomposition model for the layered evaluation of interactive adaptive systems. In: Ardissono, L., Brna, P., Mitrovic, A. (eds.) Proceedings of 10th International User Modeling Conference, pp. 438–442. Springer, Edinburgh, UK, 24–29 July 2005Google Scholar
  80. Pardos, Z.A., Heffernan, N.: KT-IDEM: introducing item difficulty to the knowledge tracing model. In: Konstan, J., Conejo, R., Marzo, J., Oliver, N. (eds.) Proceedings of 19th International Conference on User Modeling, Adaptation, and Personalization, UMAP 2011, pp. 243–254. Springer, Girona, Spain, 11–15 July 2011Google Scholar
  81. Pavlik Jr, P.I., Cen, H., Koedinger, K.R.: Performance factors analysis-a new alternative to knowledge tracing. In: Proceedings of 14th International Conference on Artificial Intelligence in Education (AIED 2009), pp. 531–538. Brighton, UK, 6–10 July 2009Google Scholar
  82. Pirolli, P., Kairam, S.: A knowledge-tracing model of learning from a social tagging system. User Model. User Adapt. Interact. 23(2–3), 139–168 (2013)CrossRefGoogle Scholar
  83. Prentzas, J., Hatzilygeroudis, I., Garofalakis, J.: A Web-based intelligent tutoring systems using hybrid rules as its representation basis. In: Cerri, S.A., Gouardères, G., Paraguaçu, F. (eds.) Proceedings of 6th International Conference on Intelligent Tutoring Systems (ITS’2002), pp. 119–128. Springer, Berlin, 2–7 June 2002Google Scholar
  84. Ritter, S.: PAT Online: A Model-tracing tutor on the World-wide Web. In: P. Brusilovsky, K. Nakabayashi and S. Ritter (eds.) Proceedings of Workshop ”Intelligent Educational Systems on the World Wide Web” at AI-ED’97, 8th World Conference on Artificial Intelligence in Education, Kobe, Japan, 18 August 1997, ISIR, pp. 11–17, also available at http://www.contrib.andrew.cmu.edu/~plb/AIED97_workshop/Ritter/Ritter.html (1997)
  85. Schneider-Hufschmidt, M., Kühme, T., Malinowski, U. (eds.): Adaptive user Interfaces: Principles and Practice. Human factors in information technology. North-Holland, Amsterdam (1993)Google Scholar
  86. Schultz, S., Arroyo, I.: Tracing Knowledge and Engagement in Parallel in an Intelligent Tutoring System. In: Stamper, J., Pardos, Z., Mavrikis, M., McLaren, B.M. (eds.) Proceedings of the 7th International Conference on Educational Data Mining (EDM 2014), pp. 312–315. London, UK, 4–7 July 2014Google Scholar
  87. Self, J.: Student models in computer-aided instruction. Int. J. Man-Mach. Stud. 6, 261–276 (1974)CrossRefGoogle Scholar
  88. Self, J.: Bypassing the intractable problem of student modelling. In: Frasson, C., Gauthier, G. (eds.) Intelligent Tutoring Systems: At the Crossroads of Artificial Intelligence and Education, pp. 107–123. Ablex Publishing, Norwood (1990)Google Scholar
  89. Shute, V.J.: SMART: Student modeling approach for responce tutoring. User Model. User Adapt. Interact. 5(1), 1–44 (1995)CrossRefGoogle Scholar
  90. Smith, A.S.G., Blandford, A.: MLTutor: an application of machine learning algorithms for an adaptive Web-based information system. Int. J. Artif. Intell. Educ. 13(2–4), 235–261 (2003)Google Scholar
  91. Sosnovsky, S., Brusilovsky, P.: Layered evaluation of topic-based adaptation to student knowledge. In: Proceedings of Fourth Workshop on the Evaluation of Adaptive Systems at 10th International User Modeling Conference, UM 2005, pp. 47–56. 26 July 2005Google Scholar
  92. Sosnovsky, S., Brusilovsky, P., Hsiao, I.-H.: Adaptation ”in the Wild”: Ontology-based personalization of open-corpus learning material. In: Proceedings of 7th European Conference on Technology Enhanced Learning (EC-TEL 2012), pp. 425–431. Saarbrücken, Germany (2012)Google Scholar
  93. Sosnovsky, S., Brusilovsky, P., Lee, D.H., Zadorozhny, V., Zhou, X.: Re-assessing the value of adaptive navigation support in e-learning. In: Nejdl, W., Kay, J., Pu, P., Herder, E. (eds.) Proceedings of 5th International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems (AH’2008), pp. 193–203. Springer, Hannover, Germany, 29 July–1 August, 2008Google Scholar
  94. Sosnovsky, S., Brusilovsky, P., Yudelson, M.: Supporting adaptive hypermedia authors with automated content indexing. In: Proceedings of Second International Workshop on Authoring of Adaptive and Adaptable Educational Hypermedia at the Third International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems (AH’2004), Eindhoven, the Netherlands (2004)Google Scholar
  95. Specht, M., Kravcik, M., Pesin, L., Klemke, R.: Learner’s Lounge: Information Brokering for the Adaptive Learning Environment. In: Driscoll, M., Reeves, T.C. (eds.) Proceedings of World Conference on E-Learning, E-Learn 2002, pp. 2510–2512. AACE, Montreal, Canada, 15–19 October 2002Google Scholar
  96. Specht, M., Oppermann, R.: ACE—adaptive courseware environment. New Rev. Hypermed. Multimed. 4, 141–161 (1998)CrossRefGoogle Scholar
  97. Specht, M., Weber, G., Heitmeyer, S., Schöch, V.: AST: Adaptive WWW-Courseware for statistics. In: Brusilovsky, P., Fink, J., Kay, J. (eds.) Proceedings of Workshop ”Adaptive Systems and User Modeling on the World Wide Web” at 6th International Conference on User Modeling, UM97, Chia Laguna, Sardinia, Italy, June 2, 1997, pp. 91–95, also available at http://www.contrib.andrew.cmu.edu/~plb/UM97_workshop/Specht.html (1997)
  98. Srivastava, J., Cooley, R., Deshpande, M., Tan, P.: Web usage mining: discovery and applications of usage patterns from Web data. SIGKDD Explor. Newsl. 1(2), 12–23 (2000)CrossRefGoogle Scholar
  99. Stern, M.K., Woolf, B.P.: Curriculum sequencing in a Web-based tutor. In: Goettl, B.P., Halff, H.M., Redfield, C.L., Shute, V.J. (eds.) Proceedings of 4th International Conference, ITS-98, pp. 574–583. Springer, Berlin, 16–19 August 1998Google Scholar
  100. Triantafillou, E., Pomportis, A., Demetriadis, S., Georgiadou, E.: The value of adaptivity based on cognitive style: an empirical study. Br. J. Educ. Technol. 35(1), 95–106 (2004)CrossRefGoogle Scholar
  101. VanLehn, K.: Student models. In: Polson, M.C., Richardson, J.J. (eds.) Foundations of Intelligent Tutoring Systems, pp. 55–78. Lawrence Erlbaum Associates, Hillsdale (1988)Google Scholar
  102. Vassileva, J.: DCG + GTE: dynamic courseware generation with teaching expertise. Instr. Sci. 26(3/4), 317–332 (1998)CrossRefGoogle Scholar
  103. Virvou, M., Moundridou, M.: Student and instructor models: Two kinds of user models and their interaction in an ITS authoring tools. In: Bauer, M., Gmytrasiewicz, P.J., Vassileva, J. (eds.) Proceedings of 8th International Conference on User Modeling, UM 2001, pp. 158–167. Springer, Berlin, 13–17 July 2001Google Scholar
  104. Wang, J.Z., Taylor, W.: Concept forest: a new ontology-assisted text document similarity measurement method. In: Lin, T.Y. et al. (eds.) Proceedings of the 2007 international conference on Web Intelligence, WI ’07, pp. 395–401. IEEE, Silicon Valey, CA, USA, 2–5 November 2007Google Scholar
  105. Weber, G., Kuhl, H.-C., Weibelzahl, S.: Developing adaptive internet based courses with the authoring system NetCoach. In: Reich, S., Tzagarakis, M.M., De Bra, P.M.E. (eds.) Hypermedia: Openness, Structural Awareness, and aptivity, pp. 226–238. Springer, Berlin (2002)CrossRefGoogle Scholar
  106. Yudelson, M.: Providing service-based personalization in an adaptive hypermedia University of Pittsburgh (2010)Google Scholar
  107. Yudelson, M., Fancsali, S., Ritter, S., Berman, S., Nixon, T., Joshi, A.: Better data beats big data. In: Stamper, J., Pardos, Z., Mavrikis, M., McLaren, B.M. (eds.) Proceedings of the 7th International Conference on Educational Data Mining (EDM 2014), pp. 205–208. London, UK, 4–7 July 2014Google Scholar
  108. Yudelson, M., Koedinger, K., Gordon, G.: Individualized Bayesian knowledge tracing models. In: Lane, H.C., Yacef, K., Mostow, J., Pavlik, P. (eds.) Proceedings of Artificial Intelligence in Education 2013, pp. 171–180. Springer, Berlin/Heidelberg, Germany (2013)Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.German Research Center for Artificial Intelligence (DFKI)SaarbrückenGermany
  2. 2.School of Information SciencesUniversity of PittsburghPittsburghUSA

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