Instructors

NEW: Grade certificates (Scheine) are ready and can be picked up at secretaries office. (DFKI old building, floor -1, opening hours 9:00-14:00)

Course administration

General information

  • Lecture type: Seminar CS
  • Credit points: 7 CP
  • Time: Tuesdays 12:30-14:00
  • Location: Building E 1 3, Room SR015
  • Kickoff meeting
    • Time: 19.04.2011 12:30
    • Location: Building E 1 3, Room SR015

    Informations for non-informatics students

    Apart from computer science students, students registered in the masters on Visual Computing and in the bachelor on Computer-und Kommunikationstechnik (with background in computer science) as well as students in Lehramt Mathematik/Informatik can also register!

    Registration

    Registration is open, please contact Britta Seidel to apply.

    Course Description

    This seminar aims to get students acquainted with the different aspects of Technology Enhanced Learning that use artificial intelligence techniques. You will learn about various existing systems and you will have the opportunity to delve into one specific area of your interest.

    Prerequisites

    Knowledge of Artificial Intelligence techniques is useful but not required.

    Requirements and grading criteria

    For passing the course, the following requirements have to be satisfied.
    • participation in an introductory seminar
    • student presentation
    • participation in other presentations

    Assignment of Course Topics and Papers

    NEW Doodle link for choosing topics HERE

    StudentTopicSupervisorDate
    YASHAVANTHOverview of Student Modeling ApproachesEric Andrčs17.05.2011
    Juan GaviriaBayesian Networks for Student ModelingGeorge Goguadze24.05.2011
    George & TobyModeling Tracing Tutors and Constraint Based Tutors discussionToby & George 24.05.2011
    Han DuEducational Data MiningOliver Scheuer31.05.2011
    Ashok Kumar Nedunc Tutorial Dialog SystemsGeorge Goguadze07.06.2011
    Manjunatha Bagrey JayapEvaluation of Intelligent Tutoring SystemsGeorge Goguadze07.06.2011
    Chao Zhang Adaptive Sequencing and Course GenerationJörg Siekmann28.06.2011
    Hanna Mousa Semantic Web Technologies for e-LearningSergey Sosnovsky28.06.2011
    Ugur KIRAArgumentation-based Learning SystemsOliver Scheuer12.07.2011
    Zheng Xu Intelligent Learning GamesJörg Siekmann12.07.2011
    Ashkan Taslimi B.Modeling Affective StateToby Dragon19.07.2011
    Ralf KotzurVirtual Learning Environments and LabsRoberta Sturm19.07.2011
    Daniel MutheLearning 2.0Sergey Sosnovsky26.07.2011
    Jing NiuModeling Meta-cognitive StateSergey Sosnovsky26.07.2011

    Please read the papers assigned to your topic. The presentation should concentrate on this topic, addressed in all papers. In the question session after the presentation, questions may be asked on all papers on your topic unless they are designated as optional reading.

    Do take a look and prepare your talk based on this tutorial by Geoff Sutcliffe How to Give a Successful Talk

    NEW: Criteria for grading HERE

    Introductory Reading List

    These should be read by everybody. (Having a look at these papers before the kick-off meeting can also help you to decide whether you are interested in the course.)

    The following are the main questions that will then be discussed in a Seminar. You might need to look in more than one articles to answer them.

    • Bloom, B. S. (1984). The 2 sigma problem: The search for methods of group instruction as effective as one-to-one tutoring. Educational Researcher 13:4-16. (PDF)
      • What is the argument made in (Bloom, 1984) that’s relevant to ITSs? What is the 2-Sigma problem?
      • What does it mean to increase a performance by 2 sigma? What is a standard deviation?
      • What was the main source of support for Bloom's argument?
      • What is a formative, a parametric, and a summative evaluation?
      • What is an aptitude-achievement correlation?

    • VanLehn, K. (2006) The behavior of tutoring systems. International Journal of Artificial Intelligence in Education. 16, 3, 227-265. (e-mail George Goguadze at "george {AT} activemath {DOT} org" to obtain this paper)
      • What is the purpose of the article?

      • What do the following terms mean? Knowledge Component, Learning vs. Physical Event, Tutoring Strategy, Inner vs. Outer Loop, Minimal Feedback vs. Error-Correction Feedback vs. Hints.
      • What is the condition under which one can characterise the behaviour of ITSs as consisting of an outer and an inner loop? How do the two relate to each other, what are the responsibilities of each one, and which issues do they involve?
      • Describe briefly the following systems:
        • Algebra Cognitive Tutors
        • Andes
        • AutoTutor
        • Sherlock
        • SQL-Tutor
        • Steve
    • Corbett, K.R. Koedinger, and J.R. Anderson. Intelligent tutoring systems. In M. Helaner and T.K. Landauer, editors, Handbook of Human-Computer Interaction, Second Edition, pages 849–874. Amsterdam: Elsevier Science, 1997.(PDF)
      • How does (Corbett et al, 1997) use the argument from (Bloom, 1984)? Why?
      • What is the research goal Corbett and his colleagues suggest and how have they followed it themselves?
      • What is the ACT-R Theory?
      • How does the analysis of ITSs in (Corbett et al, 1997) compare to the analysis in (VanLehn, 2006)? What is the architecture suggested?
      • What is the instructional intervention at the:
        • Curriculum level?
        • Problem-solving support level?
      • What is the feedback like in the SHERLOCK system?

    Course Topics Offerred

    • Student Modeling Technologies:

      • Overview of Student Modeling Approaches ( supervisor: Eric Andres)
        Papers:
        • Brusilovsky, P. and Millán, E. (2007) User models for adaptive hypermedia and adaptive educational systems. In: P. Brusilovsky, A. Kobsa and W. Neidl (eds.): The Adaptive Web: Methods and Strategies of Web Personalization. Lecture Notes in Computer Science, Vol. 4321, Berlin Heidelberg New York: Springer-Verlag, pp. 3-53. (PDF)
        • Jameson, A. (1995). Numerical uncertainty management in user and student modeling: An overview of systems and issues. User Modeling and User-Adapted Interaction, 5(3-4), 193-251. (PDF)
      • Modeling Meta-cognitive State ( supervisor: Sergey Sosnovsky)
        Papers:
        • Veenman, M. V. J., Hout-Wolters, B. H. A. M., & Afflerbach, P. (2006). Metacognition and learning: conceptual and methodological considerations. Metacognition and Learning, 1(1), 3-14. Springer. (PDF)
        • Conati C, Vanlehn K. Toward computer-based support of meta-cognitive skills: A computational framework to coach self-explanation. International Journal of Artificial Intelligence in Education. 2000, 11(4), pp. 389-415. (PDF)
        • Roll, I., Aleven, V., McLaren, B. M., & Koedinger, K. R. (2007). Designing for metacognition - applying cognitive tutor principles to the tutoring of help seeking. Metacognition and Learning, 2(2), 125-140. (PDF)
      • Modeling Affective State (supervisor:Toby Dragon)
        Papers:
        • Picard, R.W., S. Papert, W. Bender, B. Blumberg, C. Breazeal, D. Cavallo, T. Machover, M. Resnick, D. Roy and C. Strohecker (2004), "Affective Learning--A Manifesto," BT Technical Journal, Volume 22, No. 4, October 2004, pp. 253-269. (PDF)
        • Arroyo, I., Cooper, D. G., Burleson, W., Woolf, B. P., Muldner, K., & Christopherson, R. (2009). Emotion Sensors Go To School. (V. Dimitrova, R. Mizoguchi, B. Du Boulay, & A. Graesser, Eds.) Artificial Intelligence in Education, 200, 17-24. IOS Press. (PDF)
        • Affect-aware Tutors: Recognizing and Responding to Student Affect. Woolf, B.P., Burleson, W., Arroyo, I., Dragon, T., Cooper, D., Picard, R. International Journal of Learning Technology, Volume 4, Number 3-4. 129-164 (2009). (PDF)
      • Bayesian Networks for Student Modeling ( supervisor: George Goguadze)
        Papers:
        • Eva Millán, Tomasz Loboda and Jose Luis Pérez-de-la-Cruz (2010). Bayesian networks for student model engineering, Computers & Education, Volume 55, Issue 4, December 2010, Pages 1663-1683. (ask your superviser for the article)
        • Conati, C., Gertner, A., & VanLehn, K. (2002). Using Bayesian networks to manage uncertainly in student modeling. User Modeling & User-Adapted Interaction, 12(4), 371-417. (PDF)
        • E.Horvitz and T. Paek, Hernessing Models of User's Goals to Mediate Clarification Dialog in Spoken Language Systems, in Proceedings of User Modelling 2001. (PDF)
        • Jim Reye (2004). Student Modelling based on Belief Networks , International Journal of Artificial Intelligence in Education 14 (2004) 1–33, IOS Press. (PDF)

      • Open Student Modeling ( supervisor: Eric Andres)
        Papers:
        • Bull, S., Brna, P. & Pain, H. (1995). Extending the Scope of the Student Model, User Modelling and User Adapted Interaction 5(1), 45-65. (PDF)
        • Mazza, R. & Dimitrova, V. (2004). Visualising Student Tracking Data to Support Instructors in Web-Based Distance Education, 13th International World Wide Web Conference - Alternate Educational Track, 154-161. (PDF)
        • Mabbott, A. & Bull, S. (2006). Student Preferences for Editing, Persuading and Negotiating the Open Learner Model, in M. Ikeda, K. Ashley & T-W. Chan (eds), Intelligent Tutoring Systems: 8th International Conference, Springer-Verlag, Berlin Heidelberg, 481-490. (PDF)

    • Adaptation Technologies for Education:

      • Problem Solving Support ( supervisor: George Goguadze)
        Papers:
        • VanLehn, K., Lynch, C., Schulze, K. Shapiro, J. A., Shelby, R., Taylor, L., Treacy, D., Weinstein, A., & Wintersgill, M. (2005). The Andes physics tutoring system: Lessons Learned. In International Journal of Artificial Intelligence and Education, 15 (3), 1-47. (PDF)
        • Heffernan, Koedinger & Razzaq (2008) Expanding the model-tracing architecture: A 3rd generation intelligent tutor for Algebra symbolization. The International Journal of Artificial Intelligence in Education. 18(2). 153-178. (PDF)
        • B. Heeren, J. Jeuring, A. van Leeuwen and A. Gerdes, Specifying Strategies for Exercises, S. Autexier et al. (Eds.): AISC/Calculemus/MKM 2008, LNAI 5144, pp. 430–445, Springer-Verlag Berlin Heidelberg, 2008 (ask George for PDF).
        • Valerie J. Shute, Focus on Formative Feedback. Review of Educational Research, 2008, Vol. 78, No. 1, pp. 153–189 (Optional reading) (PDF)
        • Gouli, E., Gogoulou, A., Papanikolaou, K., & Grigoriadou, M. (2006). An Adaptive Feedback Framework to Support Reflection, Guiding and Tutoring. In G.Magoulas and S.Chen (Eds.) Advances in Web-based Education: Personalized Learning Environmentss, 178-202 (Optional reading) (PDF)
      • Tutorial Dialog Systems (supervisor: George Goguadze)
        Papers:
        • Claus Zinn, Johanna D. Moore, and Mark G. Core, Intelligent Information Presentation for Tutoring Systems, in Multimodal Intelligent Information Presentation, 255-277, Spriner, 2005. (PDF)
        • Clancey, W. J. (1979). Dialogue management for rule-based tutorials. In B. Buchan (Ed.) /Proceedings of the Sixth International Conference on Artificial Intelligence/ (pp. 155-161). Menlo Park, CA: AAAI Press. (PDF)
        • Arthur C. Graesser, Kurt VanLehn, Carolyn P. Rosé, Pamela W. Jordan, and Derek Harter , Intelligent Tutoring Systems with Conversational Dialogue, AI Magazine Volume 22 Number 4 (2001) (© AAAI), pp. 39-52, (PDF on request).
        • V. Aleven, O. Popescu, and K.R. Koedinger, A Tutorial Dialogue System with Knowledge-Based Understanding and Classification of Student Explanations, In: Working Notes of 2nd IJCAI Workshop on Knowledge and Reasoning in Practical Dialogue Systems, 2001 (PDF)

      • Adaptive Sequencing and Course Generation (supervisor: Jörg Siekmann)
        Papers:
        • C. Ullrich and E. Melis Pedagogically Founded Courseware Generation based on HTN-Planning in ESWA (PDF)
        • Carsten Ullrich, Eric Andres, Philipp Kärger, Erica Melis, Marianne Moormann Tutorial Component Deliverable D24 for LeActiveMath project (PDF)

        • S. Fischer, Course and Exercise Sequencing Using Metadata in Adaptive Hypermedia Learning Systems, ACM Journal of Educational Resources in Computing, Vol. 1, No. 1, Spring 2001(PDF)
        • E. Sangineto, N. Capuano, M. Gaeta, A. Micarelli, Adaptive course generation through learning styles representation, Universal Access in the Information Society, Volume 7 Issue 1, March 2008, (PDF).

      • Adaptive Educational Hypermedia (supervisor: Sergey Sosnovsky)
        Papers:
        • Brusilovsky, P. (1996) Methods and techniques of adaptive hypermedia. User Modeling and User-Adapted Interaction, 6 (2-3), pp. 87-129.(PDF)
        • Bra, P. D., & Calvi, L. (1998). AHA! An open Adaptive Hypermedia Architecture. New Review Of Hypermedia And Multimedia, 4(1), 115-139. Taylor & Francis (HTML)
        • Brusilovsky, P. and Henze, N. (2007) Open corpus adaptive educational hypermedia. In: P. Brusilovsky, A. Kobsa and W. Neidl (eds.): The Adaptive Web: Methods and Strategies of Web Personalization. Lecture Notes in Computer Science, Vol. 4321, Berlin Heidelberg New York: Springer-Verlag, pp. 671-696. (PDF)
      • Adaptive Recommender Systems for Education ( supervisor: Sergey Sosnovsky)
        Papers:
        • Manouselis, N., Drachsler, H., Vuorikari, R., Hummel, H., Koper, R., Kantor, P. B., Ricci, F., et al. (2010). Recommender Systems in Technology Enhanced Learning. Springer. (e-mail Sergey Sosnovsky at "sosnovsky {AT} gmail {DOT} com" to obtain this paper)
        • Avancini H., Straccia U.: User recommendation for collaborative and personalised digital archives, International Journal of Web Based Communities 1(2), 163–175 (2005). (PDF)
        • G. Koutrika, B. Bercovitz, H. Garcia-Molina. FlexRecs: Expressing and Combining Flexible Recommendations. ACM SIGMOD, June 29-July 2, 2009, Providence, USA. (PDF)

    • Intelligent Tutoring Systems:

      • Model-tracing Tutors ( supervisor: George Goguadze)
        Papers:
        • John R. Anderson, Albert T. Corbett, Kenneth R. Keodinger, and Ray Polletier. Cognitive tutors: Lessons learned. The Journal of Learning Sciences, 4:167–207, 1995. (PDF)
        • Koedinger, K., & Anderson, J. R. (1993). Reifying implicit planning in geometry: Guidelines for model-based intelligent tutoring system design (pp. 15-46). In S. P. Lajoie & S. J. Derry (Eds.), /Computers as Cognitive Tools/. Hillsdale, NJ: Erlbaum.(PDF)

      • Constraint-based Tutors (supervisor: Toby Dragon)
        Papers:
        • Antonija Mitrovic, Kenneth R. Koedinger, Brent Martin: A Comparative Analysis of Cognitive Tutoring and Constraint-Based Modeling. User Modeling 2003: 313-322 (doc)
        • Mitrovic, A. and Ohlsson, S. (in press, 1999). Evaluation of a constraint-based tutor for a database language. International Journal of Artificial Intelligence and Education, 10 (PDF)
        • Mitrovic, A., Ohlsson, S. Constraint-Based Knowledge Representation for Individualized Instruction Computer Science and Information Systems (COMSIS ), vol 3(1), 1-22, June 2006.(PDF)
        • Baghaei,N., Mitrovic, A., Irvin, W. Problem-Solving Support in a Constraint-based Intelligent Tutoring System for UML Technology, Instruction, Cognition and Learning, vol. 4, no 1-2, 2006.(PDF)

      • ActiveMath (supervisor: Michael Dietrich)
        Papers:
        • E. Melis, G. Goguadze, P. Libbrecht and C. Ullrich, ActiveMath - A Learning Platform With Semantic Web Features. in Ontologies and Semantic Web for e-Learning. (PDF)
        • Carsten Ullrich, Paul Libbrecht, Stefan Winterstein, Martin Mühlenbrock, A Flexible and Efficient Presentation-Architecture for Adaptive Hypermedia: Description and Technical Evaluation, in Proceedings of the 4th IEEE International Conference on Advanced Learning Technologies (ICALT 2004), Joensuu, Finland(PDF)
        • E. Melis, G. Goguadze, M. Homik, P. Libbrecht, C. Ullrich and S. Winterstein Semantic-Aware Components and Services of ActiveMath in British Journal of Educational Technology
          • (PDF on request)

      • Evaluation of Intelligent Tutoring Systems(supervisor: George Goguadze)
        Papers:
        • Regian, J. W. & Shute, V. J. (1994). Evaluating intelligent tutoring systems. In H. O'Neil and E. L. Baker (Eds.), Technology assessment (pp. 79-96). Hillsdale, NJ: Lawrence Erlbaum Associates. (PDF).
        • K. R. Koedinger, J.R. Anderson, Intelligent Tutoring Goes To School in the Big City, International Journal of Artificial Intelligence in Education (1997), 8,30-43 (PDF)
        • C.R. Beal, I.M. Arroyo, P.R. Cohen, B.P. Woolf, Evaluation of Animal Watch: An Intelligent tutoring system for arithmetic and fractions, Journal of Interactive Online Learning, Vol.9, N.1, Spring 2010, ISSN: 1541-4914. (PDF)
        • Shelby, R., Schulze, K., Treacy, D., Wintersgill, M., VanLehn, K., & Weinstein, A. (2001). An assessment of the Andes tutor. In Proceedings of the Physics Education Research Conference. Rochester, NY. (PDF)
        • Alex Sneyderman (2001). Evaluation of the Cognitive Tutor Algebra I Program, Miami-Dade County Public Schools Office of Evaluation and Research. (PDF on request by George)

      • Educational Data Mining ( supervisor: Oliver Scheuer)
        Papers:
        • Baker, R.S.J.d., Yacef, K. (2009) The State of Educational Data Mining in 2009: A Review and Future Visions. Journal of Educational Data Mining, 1 (1), 3-17. (PDF)
        • Baker, R.S.J.d., Corbett, A.T., Roll, I., Koedinger, K.R. (2008) Developing a Generalizable Detector of When Students Game the System. User Modeling and User-Adapted Interaction, 18, 3, 287-314. (PDF)
        • Yudelson, M. V., Medvedeva, O., and Crowley, R. S. (2008) A multifactor approach to student model evaluation. User Modeling and User-Adapted Interaction, 18(4), 349-382. (PDF)
      • Adaptive Testing & Item Response Theory ( supervisor: Eric Andres).
        Papers:
        • Baker, Frank (2001). The Basics of Item Response Theory. ERIC Clearinghouse on Assessment and Evaluation, University of Maryland, College Park, MD. (PDF)
        • Conejo, R., Guzmán, E., Millán, E., Trella, M., & Pérez-De, J. L. (2004). SIETTE: A Web–Based Tool for Adaptive Testing, 14(1), 29-61. IOS Press. (PDF)

      • Computer-supported Collaborative Learning:

        • Collaborative and Group Learning (supervisor: Oliver Scheuer)
          Papers:
          • Stahl, G., Koschmann, T., & Suthers, D. (2006). Computer-supported collaborative learning: An historical perspective. In R. K. Sawyer (Ed.), Cambridge handbook of the learning sciences (pp. 409-426). Cambridge, UK: Cambridge University Press. (PDF)
          • Rummel, N. & Spada, H. Learning to Collaborate: An Instructional Approach to Promoting Collaborative Problem Solving in Computer-Mediated Settings. Journal of the Learning Sciences, 2005, 14(2), 201-241. (ask your superviser for the article)
          • Amy Soller, Alejandra Martínez Monés, Patrick Jermann, and Martin Muehlenbrock. 2005. From Mirroring to Guiding: A Review of State of the Art Technology for Supporting Collaborative Learning. Int. J. Artif. Intell. Ed. 15, 4 (December 2005), 261-290. (PDF)
          • Maria de los Angeles Constantino-Gonzalez, Daniel D. Suthers, and José G. Escamilla de los Santos. 2003. Coaching Web-based Collaborative Learning based on Problem Solution Differences and Participation. Int. J. Artif. Intell. Ed. 13, 2-4 (April 2003), 263-299. (PDF)

        • Argumentation-based Learning Systems (supervisor: Oliver Scheuer).
          Papers:
          • Scheuer, O., Loll, F., Pinkwart, N, & McLaren, B. M. (2010). Computer-Supported Argumentation: A Review of the State-of-the-Art. In: International Journal of Computer-Supported Collaborative Learning, 5(1): 43-102. (PDF)
            • (selected parts)
          • Suthers, D.; Connelly, J.; Lesgold, A.; Paolucci, M.; Toth, E.; Toth, J. & Weiner, A. Representational and Advisory Guidance for Students Learning Scientific Inquiry. In: Forbus, K. D. & Feltovich, P. J. (ed.) Smart machines in education: The coming revolution in educational technology, AAAI/MIT Press, 2001, 7-35. (PDF)
          • Suthers, D. Representational Guidance for Collaborative Inquiry. In: Andriessen, J.; Baker, M. & Suthers, D. (ed.) Arguing to Learn: Confronting Cognitions in Computer-Supported Collaborative Learning Environments, Kluwer Academic Publishers, 2003, 27-46. (PDF)

      • Challenges:

        • Intelligent Learning Games (supervisor: Jörg Siekmann )
          Papers:
          • Garris, R., Ahlers, R., & Driskell, J. E. (2002). Games, Motivation, and Learning: A Research and Practice Model. Simulation & Gaming, 33(4), 441-467. ISAGA. (PDF)
          • Squire, K., & Jenkins, H. (2003). Harnessing the power of games in education. Insight, 3(1), 5-33. (PDF)
          • Conati C. and Manske M. (2009). Evaluating Adaptive Feedback in an Educational Computer Game. Proceedings of IVA 2009, 9th International Conference on Intelligent Virtual Agents, Lecture Notes in Artificial Intelligence 5773. Springer Verlag, p. 146-158. (PDF)
          • Peirce, N., Conlan, O., & Wade, V. (2008). Adaptive Educational Games: Providing Non-invasive Personalised Learning Experiences. Second IEEE International Conference on Digital Games and Intelligent Toys Based Education, 2008, p. 28-35. (PDF)
        • Ubiquitous Learning Environments and Mobile e-Learning (supervisor: Roberta Sturm)
          Papers:
          • Cui, Y., & Bull, S. (2005). Context and learner modelling for the mobile foreign language learner. System, 33(2), 353-367. Elsevier. (PDF)
          • Sharples, M. (2000). The design of personal mobile technologies for lifelong learning. Computers & Education, 34(3-4), 177-193. (PDF)
          • Yang, S. J. H. (2006). Context Aware Ubiquitous Learning Environments for Peer-to-Peer Collaborative Learning. Educational Technology & Society, 9(1), 188-201. (PDF)
          • Ogata, H., & Yano, Y. (2004). Context-aware support for computer-supported ubiquitous learning. Proceedings The 2nd IEEE International Workshop on Wireless and Mobile Technologies in Education'2004. (PDF)
        • e-Learning 2.0 (supervisor: Sergey Sosnovsky)
          Papers:
          • Ullrich, C., Borau, K., Luo, H., Tan, X., Shen, L., & Shen, R. (2008). Why web 2.0 is good for learning and for research: principles and prototypes. WWW 08 Proceeding of the 17th international conference on World Wide Web (p. 705-714). ACM. (PDF)
          • Vassileva J., Sun L. (2007) Using Community Visualization to Stimulate Participation in Online Communities.e-Service Journal, 6 (1), 3-40. (extended version of Vassileva and Sun, Proc. CRIWG'2006, Medina del Campo, Span). (PDF)
          • Dron, J. (2007). Designing the undesignable: Social software and control. Educational Technology & Society, 10(3), 60-71. (PDF)
          • Farzan, R. and Brusilovsky, P. (2008) AnnotatEd: A social navigation and annotation service for web-based educational resources. New Review in Hypermedia and Multimedia 14 (1), 3-32. (PDF)
        • Virtual Learning Environments and Labs (supervisor: Roberta Sturm)
          Papers:
          • Johnson, A., Roussos, M., Leigh, J., Vasilakis, C., Barnes, C., & Moher, T. (1998). The NICE project: Learning together in a virtual world. Proceedings of the Virtual Reality Annual (p. 176-183). IEEE. (PDF)
          • Serious use of a serious game for language learning W. Lewis Johnson In R. Luckin et al. (Eds.), Artificial Intelligence in Education, Amsterdam: IOS Press, 2007. (PDF)
          • Jacobson, J. (2008) Ancient Architecture in Virtual Reality; Does Visual Immersion Really Aid Learning? Dissertation, School of Information Sciences, University of Pittsburgh.
            Chapters 2.1 - 2.3, 2.4 - 2.6
            (PDF)
        • Semantic Web Technologies for e-Learning (supervisor: Sergey Sosnovsky)
          Papers:
          • Mizoguchi, R., & Bourdeau, J. (2000). Using Ontological Engineering to Overcome AI-ED Problems. International Journal of Artificial Intelligence in Education, 11(2), 107-121. (PDF)
          • Sosnovsky, S., & Dicheva, D. (2010). Ontological technologies for user modelling. International Journal of Metadata, Semantics and Ontologies, 5(1), 32-71.(PDF)
          • Dolog, P., & Nejdl, W. (2007). Semantic Web Technologies for the Adaptive Web. In P. Brusilovsky, A. Kobsa, & W. Nejdl (Eds.), (pp. 697-719). Heidelberg, Germany: Springer Verlag.(PDF)

Last edited by george at Sep 29, 2011 10:48 AM - Edit content