TEL-Seminar-2012-1


Schedule

  • Kickoff Meeting: 19.10.11 from 9:00 to 11:00 (Main building of DFKI - Reuse room):
    Introduction (slides)
  • Choose your Topic(s) using this URL: http://tiny.cc/teltopics no later than 31.10.11 (do not pick more than 3 topics)
  • Introductory Reading Discussion: 16.11.11 from 12:00 to 14:00 (Main building of DFKI - Reuse room)
  • Cluster Meetings: TBA
    • Student Modeling Cluster (Toby Dragon)
      • Overview of Student Modeling Approaches (Rau, Andreas)
      • Modeling Meta-cognitive State (Degenbaeva, Cholpon)
      • Modeling Affective State (Haupenthal, Yannic)
      • Bayesian Networks for Student Modeling (Walter, Marie-Therese)
      • Open Student Modeling (Friedmann, Alberto)
    • Adaptation Cluster (George Goguadze & Eric Andres)
      • Problem Solving Support (Schlinkmann, Christian)
      • Tutorial Dialog Systems (Mehta, Vikram)
      • Adaptive Sequencing and Course Generation (Turner, David)
      • Model-tracing Tutors (Al Nuaimi, Mahmood)
      • Constraint-based Tutors (Moin, Amir)
      • Argumentation-based Learning Systems (faiz, humayun)
    • Challenges Cluster (Sergey Sosnovsky)
      • Intelligent Learning Games (Yang, Jing Yu): 14/12/11 10:00 - 12:00 (Simon room, DFKI)
      • Ubiquitous Learning Environments and Mobile e-Learning (Tiab, John): 21/12/11 10:00 - 12:00 (Simon room, DFKI)
      • e-Learning 2.0 (Yurong, Tao): 11/01/12 10:00 - 12:00 (Simon room, DFKI)
      • Augmented and Virtual Reality for e-Learning (Sandrala, Indra Praveen): 18/01/12 10:00 - 12:00 (Simon room, DFKI)
      • Semantic Web Technologies for e-Learning (Monogios, Christos): 25/01/12 10:00 - 12:00 (Simon room, DFKI)
      • Overview: 08/02/12 10:00 - 12:00 (Simon room, DFKI)
  • Final Presenations

Instructors

Course administration

General information

  • Lecture type: Seminar CS
  • Credit points: 7 CP

Registration

Registration is open, please contact Britta Seidel to apply. The seminar can accommodate 21 students, maximum. The order of registration will be used to define the the course participants. A waiting list will be created to substitute those who might decide to drop the course.
IMPORTANT: This is only for our course-internal record of participants. In case your study programme is subject to HISPOS (e.g., computer science students), you need to register for this course also in the HISPOS system. Please consult with your department on whether you need to register in HISPOS and on the closing date for registration. You cannot obtain credits if you fail to register on time.

Course Description

This seminar aims to get students acquainted with the different aspects and components of modern computer-based education systems. You will learn about various technologies used in such systems and will have the opportunity to delve into one specific area of your interest.
Another goal of the seminar is to help students develop analytical reading, presentation and discussion skills.

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 ALL meetings of the course
  • Presentation of your topic
  • Active participation in discussions
You are required to read introductory papers on Computer Based Education Systems and core papers of the topics in your cluster and all papers specific to your topic of interest. You will have to prepare a 30 min. slide presentation on your topic based on the papers you read and discuss it with the students and the leader of your cluster. You will have to participate in the discussion of the presentations of other students in you cluster. At the end of the course you will have to prepare a joint 1 hour presentation of your cluster together with other students and participate in the discussion of presentations of other clusters.

Assignment of Course Topics and Papers

Please, read the papers assigned to your topic. The presentation should try to get input from all papers.

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

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 Sergey Sosnovsky at "sosnovsky {AT} gmail {DOT} com" 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?


Student Modeling Technologies

Leader of the cluster: Toby Dragon
  • Overview of Student Modeling Approaches
    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)
    • Paiva, A. and Self, J.: 1995, TAGUS - a user and learner modeling workbench, User Modeling and User-Adapted Interaction, 4(3), 197-228 (e-mail Sergey Sosnovsky at "sosnovsky {AT} gmail {DOT} com" to obtain this paper)

  • Modeling Meta-cognitive State
    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
    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)
    • Baker, R., D'Mello, S., Rodrigo, M., & Graesser, A. (2010). Better to be frustrated than bored: The incidence and persistence of affect during interactions with three different computer-based learning environments. International Journal of Human-Computer Studies, 68 (4), 223-241. (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
    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)

  • Open Student Modeling
    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

Leaders of the cluster: George Goguadze & Eric Andres

  • Problem Solving Support
    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)
    • 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
    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)
    • Arthur C. Graesser, Natalie K. Person. Derek Harter and The Tutoring Research Group, Teaching Tactics and Dialog in AutoTutor, International Journal of Artificial Intelligence in Education (2001) (PDF on request)
    • 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
    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).

  • Model-tracing Tutors
    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
    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)

  • Argumentation-based Learning Systems
    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:

Leader of the cluster: Sergey Sosnovsky

  • Intelligent Learning Games
    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
    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)
    • Gwo-Jen Hwang, Tzu-Chi Yang, Chin-Chung Tsai, Stephen J.H. Yang (2009). A context-aware ubiquitous learning environment for conducting complex science experiments. Computers & Education 53 (2009) 402–413. (PDF)

  • e-Learning 2.0
    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
    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
    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 eric at Jan 17, 2012 2:35 PM - Edit content - View source