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Computer-Based Educational Technologies (Winter 2014-2015)


Basic Info

Schedule

  1. (04/11/14) Kickoff Meeting (Slides)
  2. (11/11/14) Introductory Reading Discussion
  3. (25/11/14)
    Modeling Knowledge: Klos, Stefan
    • Corbett, A. T., & Anderson, J. R. (1994). Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction, 4(4), 253�278. (PDF)
    • Brusilovsky, P. and Mill�n, 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): RECOMMENDED TO EVERYONE
    • Paiva, A. and Self, J.: 1995, TAGUS - a user and learner modeling workbench, User Modeling and User-Adapted Interaction, 4(3), 197-228 (for a PDF, ask the teacher)
    Modeling Metacognitive State: Gerbracht, Richard
    • 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): RECOMMENDED TO EVERYONE
    • 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)
  4. (26/11/14)
    Modeling Affective State: Goyal, Mayank
    • 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): RECOMMENDED TO EVERYONE
    • 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)
    Open Student Modeling: Manojkumar, Kuncham
    • 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): RECOMMENDED TO EVERYONE
  5. (16/12/14)
    Managing Uncertainty in Student Modelling: Davit Hovhannisyan
    • 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): RECOMMENDED TO EVERYONE
    • 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)
    Model-tracing Tutors: Srikanth Bommaraveni
    • 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): RECOMMENDED TO EVERYONE
    • 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)
  6. (17/12/14)
    Constraint-based Tutors: Kleio Mavridou
    • Mitrovic, A. (2011). Fifteen years of constraint-based tutors: what we have achieved and where we are going. User Modeling and User-Adapted Interaction, 22(1-2), 39�72. doi:10.1007/s11257-011-9105-9. (for a PDF, ask the teacher): RECOMMENDED TO EVERYONE
    • Mitrovic, A. and Ohlsson, S. (1999). Evaluation of a constraint-based tutor for a database language. International Journal of Artificial Intelligence and Education, 10. (PDF)
    • Mitrovic, A., Ohlsson, S. (2006). Constraint-Based Knowledge Representation for Individualized Instruction Computer Science and Information Systems (COMSIS), vol 3(1), 1-22, June 2006. (PDF)
    Feedback, Scaffolding and Problem Solving Support: Tim Steuer
    • 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 (PDF): RECOMMENDED TO EVERYONE
    • Murray, T., & Arroyo, I. (2002). Toward measuring and maintaining the zone of proximal development in adaptive instructional systems (pp. 749�758). Presented at the Intelligent Tutoring Systems, Springer. (PDF)
  7. (13/01/15)
    Modelling Affective State: Stefanie Lehmann
    • 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) RECOMMENDED TO EVERYONE
    • 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)
    Adaptive Educational Hypermedia: Nilufar Oripova
    • Brusilovsky, P. (1996) Methods and techniques of adaptive hypermedia. User Modeling and User-Adapted Interaction, 6 (2-3), pp. 87-129. (PDF)
    • Brusilovsky, P. (2001) Adaptive hypermedia. User Modeling and User Adapted Interaction, Ten Year Anniversary Issue (Alfred Kobsa, ed.) 11 (1/2), 87-110. (PDF)
    • Brusilovsky, P. (2003) Adaptive navigation support in educational hypermedia: The role of student knowledge level and the case for meta-adaptation. British Journal of Educational Technology, 34 (4), 487-497. (PDF) RECOMMENDED TO EVERYONE
  8. (14/01/15)
    Educational Recommender Systems: Revathy Santhanam
    • Manouselis, N.; Drachsler, H.; Vuorikari, R.; Hummel, H.; Koper, R. Recommender Systems in Technology Enhanced Learning. In Recommender Systems Handbook: A Complete Guide for Research Scientists and Practitioners; Kantor, P., Ricci, F., Rokach, L., Shapira, B., Eds.; Springer: Berlin, Germany, 2010. (PDF) RECOMMENDED TO EVERYONE
    • BOBADILLA, J., ORTEGA, F., HERNANDO, A., AND GUTIERREZ, A. 2013. Recommender systems survey. Know.-Based Syst. 46, 109�132. (for a PDF, ask the teacher)
    Adaptive Sequencing and Course Generation: Karthik Srinivasan
    • Brusilovsky, P., & Vassileva, J. (2003). Course sequencing techniques for large-scale web-based education. International Journal of Continuing Engineering Education and Life Long Learning, 13(1), 75�94. (PDF)
    • C. Ullrich and E. Melis. Pedagogically Founded Courseware Generation based on HTN-Planning, Proceedings of in ESWA 2008. RECOMMENDED TO EVERYONE (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)
  9. (27/01/15)
    Item Response Theory and Computer-based Testing: Anu Sundar
    • Baker, Frank (2001). The Basics of Item Response Theory. ERIC Clearinghouse on Assessment and Evaluation, University of Maryland, College Park, MD. (ONLY FIRST THREE CHAPTERS)(PDF)
    • Conejo, R., Guzm�n, E., Mill�n, E., Trella, M., & P�rez-De, J. L. (2004). SIETTE: A Web�Based Tool for Adaptive Testing, 14(1), 29-61. IOS Press. (PDF) RECOMMENDED TO EVERYONE
    Evaluation of Computer-based educational systems: Alla Kornoukhova
    • David Chin (2001). Empirical Evaluations of User Models and User-Adapted Systems. User Modelling and User-Adapted Interaction 11: 181-194, 2001. (PDF)
    • Brusilovsky P, Karagiannidis C, and Sampson D (2004) Layered evaluation of adaptive learning systems. International Journal of Continuing Engineering Education and Life Long Learning, 14(4), 402-421. (PDF)
    • Koedinger, K.R., Anderson, J.R., Hadley, W.H., et al. Intelligent tutoring goes to school in the big city. International Journal of Artificial Intelligence in Education (1997) 8:30�43. (PDF) RECOMMENDED TO EVERYONE
  10. (28/01/15)
    Peer-tutoring and Collaborative Learning: Mart�n Ignacio Fallas Hidalgo
    • K.J. TOPPING (1996). The effectiveness of peer tutoring in further and higher education: A typology and review of the literature. Higher Education 32: 321-345, 1996. (PDF)
    • Walker, E., Rummel, N., & Koedinger, K. R. (2011). Designing automated adaptive support to improve student helping behaviors in a peer tutoring activity. International Journal of Computer-Supported Collaborative Learning, 6(2), 279-306. (PDF) RECOMMENDED TO EVERYONE
    • Soller, A., Mon�s, A. M., Jermann, P., & M�hlenbrock, M. (2005). From Mirroring to Guiding: A Review of State of the Art Technology for Supporting Collaborative Learning. International Journal on Artificial Intelligence in Education, 15(4), 261-290. (PDF)
    Peer-review and peer-assessment: Nikita Dutta
    • Cho, K., & Schunn, C. D. (2007). Scaffolded writing and rewriting in the discipline: A web-based reciprocal peer review system. Computers & Education, 48(3), 409-426. (PDF)
    • Goldin, I. M., & Ashley, K. D. (2010). Eliciting Informative Feedback in Peer Review: Importance of Problem-Specific Scaffolding. In V. Aleven, J. Kay, & J. Mostow (Eds.), Proceedings of the 10th International Conference on Intelligent Tutoring Systems (ITS-2010) (pp. 95-104), Berlin: Springer. (PDF)
    • Ken Reily, Pam Ludford Finnerty, and Loren Terveen. 2009. Two peers are better than one: aggregating peer reviews for computing assignments is surprisingly accurate. In Proceedings of the ACM 2009 international conference on Supporting group work (GROUP '09). ACM, New York, NY, USA, 115-124. (PDF) RECOMMENDED TO EVERYONE
  11. (03/02/15)
    Educational Data Mining: optimisation of system behaviour: Juan Gabriel Uma�a Quir�s
    • C. Romero, S. Ventura, Educational data mining: A survey from 1995 to 2005, Expert Systems with Applications, Volume 33, Issue 1, July 2007, Pages 135-146 (PDF) RECOMMENDED TO EVERYONE
    • Joseph E. Beck, Kai-min Chang, Jack Mostow, and Albert Corbett Does help help? Introducing the Bayesian Evaluation and Assessment methodology (PDF)
    • Martin, B., Mitrovic, T., Mathan, S., & Koedinger, K.R. (2011). Evaluating and improving adaptive educational systems with learning curves. User Modeling and UserAdapted Interaction (2011) 21:249�283. (PDF)
    Educational Data Mining: detection of important learning events: Ralph Eberhard Gerbracht
    • BAKER, R.S., CORBETT, A.T. and KOEDINGER, K.R. 2004. Detecting Student Misuse of Intelligent Tutoring Systems. In Proceedings of the 7th International Conference on Intelligent Tutoring Systems, Maceio, Brazil, 531-540. (PDF)
    • Baker, R.S.J.d., Goldstein, A.B., Heffernan, N.T. (2010) Detecting the Moment of Learning. Proc. of the 10th Annual Conference on Intelligent Tutoring Systems, 25-34.(PDF)
    • MCQUIGGAN, S., MOTT, B. and LESTER, J. 2008. Modeling Self-Efficacy in Intelligent Tutoring Systems: An Inductive Approach. User Modeling and User-Adapted Interaction 18, 81-123. (PDF)
  12. (04/02/15)
    Authoring Technologies for Adaptive Educational Systems: Sujith Reddy Varakantham
    • Tom Murray, Principles for pedagogy-oriented knowledge based tutor authoring systems (chapter 15 in Authoring tools for advanced technology learning environments, murray, blessing, and ainsworth eds) (pdf) RECOMMENDED TO EVERYONE
    • Peter Brusilovsky, Developing Adaptive Educational Hypermedia Systems: from design models to authoring tools (chapter 13 in Authoring tools for advanced technology learning environments, murray, blessing, and ainsworth eds) (pdf)
    • Aleven, V., McLaren, B.M., Sewall, J., & Koedinger, K. R. (2006). The Cognitive Tutor Authoring Tools (CTAT): Preliminary evaluation of efficiency gains. In M. Ikeda, K. D. Ashley, & T. W. Chan (Eds.), Proceedings of the 8th International Conference on Intelligent Tutoring Systems (ITS 2006), (pp. 61-70). Berlin: Springer Verlag. (pdf)
    Tutorial Dialogue Systems: Shruthi Yaddanapalli
    • 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) RECOMMENDED TO EVERYONE
    • Dzikovska, M. O., Steinhauser, N., Farrow, E., Moore, J.D., Campbell, G.E.(2014) "BEETLE II: Deep natural language understanding and automatic feedback generation for intelligent tutoring in basic electricity and electronics". International Journal of Artificial Intelligence in Education: Volume 24, Issue 3 (2014), Page 284-332 (PDF).
    • D. J. Litman and S. Silliman, �ITSPOKE: An intelligent tutoring spoken dialogue system�. Proc. 4th meeting of HLT/NAACL, 2004, pp. 52-54 (PDF)
  13. (10/02/15)
    Ubiquitous Learning Environments and Mobile e-Learning: Theresa Blug
    • 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) RECOMMENDED TO EVERYONE
    • 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)
    14. Learning Analytics: Robin Woll
    • Dyckhoff, A. L., Lukarov, V., Muslim, A., Chatti, M. A., & Schroeder, U. (2013). Supporting Action Research with Learning Analytics. Proceedings of the Third International Learning Analytics & Knowledge Conference (LAK13). (To get the PDF, ask the instructor)
    • Erik Duval. 2011. Attention please!: learning analytics for visualization and recommendation. In Proceedings of the 1st International Conference on Learning Analytics and Knowledge (LAK '11). (To get the PDF, ask the instructor)
    • Siemens, G. & Long, P. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46(5). (HTML) RECOMMENDED TO EVERYONE
    • Everaldo Aguiar, Nitesh V. Chawla, Jay Brockman, G. Alex Ambrose, and Victoria Goodrich. 2014. Engagement vs performance: using electronic portfolios to predict first semester engineering student retention. In Proceedings of the Fourth International Conference on Learning Analytics And Knowledge (LAK '14). (PDF)
  14. (11/02/15)
    Argumentation-based Learning Systems: Reni Seefeld
    • 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) RECOMMENDED TO EVERYONE
    • 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)
    Massive Open Online Courses: Imam Hasan
    • Glance, D.G., Forsey, M. and Riley, M. (2013). The pedagogical foundations of massive open online courses, First Monday. (HTML) RECOMMENDED TO EVERYONE
    • Koller, D.; Ng, A.; Do, C. and Chen, Z. (2013). Retention and intention in massive open online courses: in depth, Educause Review. (HTML)
    • Ferguson, R. & Sharples, M. (2014). Innovative pedagogy at massive scale: Teaching and learning in MOOCs. In C. Rensing, S. de Freitas, T. Ley & P. J. Mu�oz-Merino (Eds.) Open Learning and Teaching in Educational Communities, Proceedings of 9th European Conference on Technology Enhanced Learning, Graz, Austria, September 16-19. Heidelberg: Springer, pp. 98-111. (To get the PDF, ask the instructor)
    • Daniel, J. (2012). "Making sense of MOOCs: Musing in a maze of myth, paradox and possibility." Journal of Interactive Media in Education 18. (HTML)
  15. (17/02/15)
    Informal and Self-Regulated Learning Support: Karolina Meinck
    • A. Nussbaumer, I. Dahn, S. Kroop, A. Mikroyannidis, D. Albert (2015). Supporting Self-Regulated Learning. In S. Kroop, A. Mikroyannidis, M. Wolpers (eds.) Responsive Open Learning Environments (pp 17-48). (PDF) RECOMMENDED TO EVERYONE
    • V. Marsick, K. Watkins (1998). Informal and Incidental Learning. In S. B. Merriam (Ed.), The new update on adult learning theory (pp. 25-34). New Directions for Adult and Continuing Education, No. 89. San Francisco: Jossey-Bass (PDF)
    • Azevedo, R., Johnson, A., Chauncey, A., & Burkett, C. (2010). Self-regulated learning with MetaTutor: Advancing the science of learning with MetaCognitive tools. In M. Khine & I. Saleh (Eds.), New science of learning: Computers, cognition, and collaboration in education (pp. 225-247). Amsterdam: Springer. (PDF)
    Virtual And Augmented Training Environments: David Nisense
    • 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)
    • Alaraj A, Lemole MG, Finkle JH, Yudkowsky R, Wallace A, Luciano C, et al. Virtual reality training in neurosurgery: Review of current status and future applications. Surg Neurol Int. 2011 (pp. 2-52). (HTML)
    • Sabine Webel, Uli Bockholt, Timo Engelke, Nirit Gavish, Manuel Olbrich, Carsten Preusche, An augmented reality training platform for assembly and maintenance skills, Robotics and Autonomous Systems, Volume 61, Issue 4, April 2013, Pages 398-403. (To get the PDF, ask the instructor)
    • 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) RECOMMENDED TO EVERYONE
    Interactive Pedagogical Agents and Learning Companions: Jil Thurmes
    • Paiva, A., J. Dias, et al. (2005). "Learning by Fealing: Evoking Empathy with Synthetic Characters." Applied Artificial Intelligence 19(3-4): 235-266. (PDF)
    • Johnson, W. L., J. Rickel, et al. (2000). "Animated Pedagogical Agents: Face-to-Face Interaction in Interactive Learning Environments." International Journal of Artificial Intelligence in Education 11: 47-78. (PDF) RECOMMENDED TO EVERYONE
    • Lester, J. C., S. A. Converse, et al. (1997). Animated pedagogical agents and problem-solving effectiveness: A large-scale empirical evaluation. Eighth World Conference on Artificial Intelligence in Education, IOS Press: 23-30. (PDF)
    16. Game-based Learning: Maximilian Messerschmidt
    • 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) RECOMMENDED TO EVERYONE
    • 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)
  16. (18/02/15)
    Semantic Web and Linked Data Technologies for e-Learning: Philipp M�ller
    • Stefan Dietze, Honq Qing Yu, Daniela Giordano, Eleni Kaldoudi, Nikolas Dovrolis, and Davide Taibi. 2012. Linked education: interlinking educational resources and the Web of data. In Proceedings of the 27th Annual ACM Symposium on Applied Computing (SAC '12). ACM, New York, NY, USA, 366-371. (PDF) RECOMMENDED TO EVERYONE
    • 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)
    Social e-Learning Systems: Carola Plesch
    • 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) RECOMMENDED TO EVERYONE
    • 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)
    17. Computer-based learning technologies for people with disabilties: Rafaella Antonyan
    • Tanja K�ser, Alberto Giovanni Busetto, Gian-Marco Baschera, Juliane Kohn, Karin Kucian, Michael von Aster, Markus Gross (2012). In Proceedings of Intelligent Tutoring Systems'12 (pp. 389-398). (PDF)
    • P. Wilson, N. Foreman, and D. Stanton, "Virtual reality, disability and rehabilitation", Disability Rehab., vol. 19, no. 6, pp.213 -220 1997. (PDF)
    • Hasselbring, T. G., & Glaser, C. H. W. (2000). Use of computer technology to help students with special needs. The Future of Children: Children and Computer Technology, 10(2), 102�122. (PDF) RECOMMENDED TO EVERYONE
    Computer-assisted language learning: Marina Oberwegner
    • W. L. Johnson, �Serious Use of a Serious Game for Language Learning,� International Journal of Artificial Intelligence in Education, vol. 20, no. 2, pp. 175-195, 2010. (PDF)
    • Aist, G. (2002). Helping Children Learn Vocabulary during Computer-Assisted Oral Reading. Educational Technology and Society, 5(2). 147-163. (PDF)
    • M.Levy (2009) Technologies in Use for Second Language Learning. The Modern Language Journal. 93, pp. 769-782.(PDF) RECOMMENDED TO EVERYONE

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 and Learning Theories is useful but not required.

Introductory Reading List

These should be read by everybody before the class meeting#2 (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 behaviour 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?



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