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Summer School Application

2014 Application Process Open

Instructions: Please complete the following form. There is space at the bottom to attach your curriculum vitae and to apply for a scholarship for full time graduate students,

Application Form





My English Proficiency is:




LearnLab Courses (see http://www.LearnLab.org/learnlabs/):







Carnegie Mellon University Open Learning Initiative Courses (see http://www.cmu.edu/oli/ ):










Mark what you'd prefer your primary emphasis to be for the summer school. Your interest should be either to A) design and create a prototype of an experiment for a course B) analyze an educational data set using data mining tools and methods C) implement a prototype computer-based tutor using authoring tools or D) build an instructional environment.




Mark what you'd prefer your secondary emphasis to be for the summer school.




Note: Curriculum Vitae must be either .doc, .pdf, .txt, .rtf, or .ps file formats.
Curriculum Vitae:

Scholarship

Please indicate why a scholarship is necessary or desirable for you to attend.



For each area of primary or secondary interest, fill out the appropriate section(s) below.

A) In Vivo Experiment development (fill out only if of primary or secondary interest)

To find out more about in vivo experiments, see the research sections of the the LearnLab website







I would feel most comfortable using the following technologies to implement my experimental design:


B) Educational Data Mining, Statistics, and Machine Learning (fill out only if of primary or secondary interest)

To find out more about LearnLab and datamining see the DataShop

and the list of papers in Appendix B.















For your project you may use existing data set(s) in DataShop or other data that you have available to you. Which DataShop data set(s) are you most interested in exploring:







C) Intelligent Tutor and Collaborative Learning Environment development (fill out only if of primary or secondary interest)

To find out more about the tools for the development that will be used during the summer schools, see LearnLab enabling technologies. In particular, see Cognitive Tutor Authoring Tools (CTAT) and Tutorial dialogue system and authoring tools (TuTalk). Basilica, a prototyping infrastructure for computer supported collaborative learning environments will also be used. To find out more about on-line course development see the Open Learning Initiative(OLI).




























Write a paragraph describing your relevant programming experience and coursework. Highlight any experience particularly relevant to Intelligent Tutor development, and Artificial Intelligence programming, or on-line course development.
Write a paragraph describing the computer-based tutor or collaborative learning environment you plan to design or implement during the summer school.


If you wish to e-mail or mail your application please send all the information requested above to.

Jo Bodnar -
Human-Computer Interaction Institute
Carnegie Mellon University
5000 Forbes Ave
3507 Newell-Simon Hall
Pittsburgh, PA 15213
+1 (412) 268-6162

Appendix A. In Vivo Learning Experiments: Sample Hypotheses

Examples of hypothesis that have some prior lab support are listed below along with some relevant references:

  • Adding illustrations to text improves learning Mayer, R. C. (1989). Systematic thinking fostered by illustrations in scientific text. Journal of Educational Psychology, 81, 240-246.
  • Adding animations improves learning (under some conditions ...) Moreno, R. & Mayer, R. C. (1999). Multimedia-supported metaphors for meaning making in mathematics. Cognition and Instruction, 17, 215-248. Pane, J.F., Corbett, A.T., and John, B.E. (1996). Assessing Dynamics in Computer-Based Instruction. In Proceedings of ACM CHI'96 Conference on Human Factors in Computing Systems. Vancouver, pp. 197-204.
  • Adding worked examples improves learning Trafton, J. G., & Reiser, B.J. (1993). The contributions of studying examples and solving problems to skill acquisition. In M. Polson (Ed.), Proceedings of the Fifteenth annual conference of the Cognitive Science Society (1017-1022). Hillsdale, N.J.: Erlbaum.
  • Variability in worked examples improves learning Paas, F., & Van Merrienboer, J. (1994). Variability of worked examples and transfer of geometry problem-solving skills: A cognitive-load approach. Journal of Educational Psychology, 86, 122-133.
  • Fading worked examples as skill increases improves learning Kalyuga, S., Chandler, P., Tuovinen, J., & Sweller, J. (2001). When problem solving is superior to studying worked examples. Journal of Educational Psychology, 93, 579-588.
  • Labeling subgoals in worked examples improves learning Catrambone, R. (1998). The subgoal learning model: Creating better examples so that students can solve novel problems. Journal of Experimental Psychology: General, 127(4), 355-376.
  • Self-explanation improves learning Aleven, V.A.W.M.M., & Koedinger, K. R. (2002). An effective metacognitive strategy: Learning by doing and explaining with a computer-based Cognitive Tutor. Cognitive Science, 26(2).
  • Audio helps learning (but animated agents do not): Moreno, R., Mayer, R.E., Spires, H., and Lester, J. (2001) The Case for Social Agency in Computer-Based Teachings: Do Students Learn More Deeply When They Interact with Animated Pedagogical Agents? Cognition and Instruction, 19, 177-214.
  • Text in addition to audio can hurt learning Mayer, R.E., Heiser, J., and Lonn, S. (2001) Cognitive constraints on multimedia learning: When presenting more material results in less understanding. Journal of Educational Psychology, 93, 187-198.
  • Motivation enhancements can help learning Cordova, D. I. & Lepper, M. R. (1996). Intrinsic motivation and the process of learning: Beneficial effects of contextualization, personalization, and choice. Journal of Educational Psychology, 88(4), 715-730.
  • Adding interesting material can hurt learning Moreno, R., & Mayer, R.E. (2000) A coherence effect in multimedia learning: The case of minimizing irrelevant sounds in the design of multimedia instructional messages. Journal of Educational Psychology, 92, 117-125. Harp, S. F., & Mayer, R.E. (1998) How seductive details do their damage: A theory of cognitive interest in science learning. Journal of Educational Psychology, 90, 414-434.
  • Encouraging integration from multiple sources improves learning Wiley, J. & Voss, J. F. (1999) Constructing arguments from multiple sources: Tasks that promote understanding and not just memory for text. Journal of Educational Psychology, 91, 301-311.
  • Peer collaboration can improve learning (under some circumstances ...) Fantuzzo, J. W., King, J. A., & Heller, L. R. (1996). Effects of reciprocal peer tutoring on mathematics and school adjustment: A component analysis. Journal of Educational Psychology, 84(3), 331-339. Johnson D.W., Maruyana G., Johnson R,T, (1981). Effects of cooperative, competitive, and individualistic goal structures on achievement: A meta-analysis. Psychology Bulletin, 89(1), 47-62.
  • System control improves learning, but may reduce motivation Schnackenberg, H. L., & Sullivan, H. J. (2000). Learner control over full and lean computer based instruction under differing ability levels. Educational Technology Research and Development, 48, 19-35. Young, J. D. (1996). The effect of self-regulated learning strategies on performance in learner controlled computer-based instruction. Educational Technology Research and Development, 44, 17-27.
  • Smart delayed feedback can improve learning Mathan, S. & Koedinger, K. R. (2003). Pedagogical effects of modeling error detection and correction skills in an Intelligent Tutoring System. In U. Hoppe, F. Verdejo, & J. Kay (Eds.), Artificial Intelligence in Education: Shaping the Future of Learning through Intelligent Technologies, Proceedings of AI-ED 2003 (pp. 13-18). Amsterdam, IOS Press.

Appendix B - Data Mining using LearnLab"s Data Shop and Tag Helper

The following are relevant references for data mining in data sets of educational interactions. Some involve the use of machine learning or advanced statistics.

  1. Learning Curve Analyses Cen, H., Koedinger, K. R., & Junker, B. (to appear). Learning Factors Analysis - A general method of cognitive model evaluation and improvement. To appear in Proceedings of Intelligent Tutoring Systems, 2006. Corbett A.T., Anderson, J.R., O"Brien A.T. (1993) Student Modelling in the ACT Programming Tutor, Cognitively Diagnostic Assessment, Hillsdale, NJ: Erlbaum Draney, K., Pirolli, P., & Wilson, M. (1995). A Measurement Model for a Complex Cognitive Skill. Cognitively Diagnostic Assessment. Lawrence Erlbaum Associates, Publishers Martin, B., Mitrovic, T., Mathan, S., & Koedinger, K.R. (2005). On Using Learning Curves to Evaluate ITS. Automatic and Semi-Automatic Skill Coding With a View Towards Supporting On-Line Assessment. Proceedings of the 12th International Conference on Artificial Intelligence in Education (AIED2005). Amsterdam, IOS Press.
  2. Analyses of learning strategies Aleven, V., & Koedinger, K.R. (2000). Limitations of student control: Do students know when they need help? In Proceedings of the 5th International Conference on Intelligent Tutoring Systems, ITS 2000, edited by G. Gauthier, C. Frasson, and K. VanLehn, 292-303. Berlin: Springer Verlag. [Best paper.] Baker, R.S., Corbett, A., Koedinger, K. R., Roll, I. (2005). Detecting when students game the system, across tutor subjects and classroom cohorts. Proceedings of User Modeling 2005, 220-224. Roll, R., Baker, R., Aleven, V., McLaren, B., Koedinger, K. R. (2005). Modeling students' metacognitive errors in two intelligent tutoring systems. Proceedings of User Modeling 2005, 367-376.
  3. Error & Strategy Analyses Feng, M., Heffernan, N. T., & Koedinger, K. R. (2005). Looking for Sources of Error in Predicting Student"s Knowledge. In Proceedings of AAAI'05 workshop on Educational Data Mining. Koedinger, K. R. & Mathan, S. (2004). Distinguishing qualitatively different kinds of learning using log files and learning curves. In the Working Notes of the ITS2004 Workshop on Analyzing Student-Tutor Interaction Logs to Improve Educational Outcomes. Mark, M. A., & Koedinger, K. R. (1999). Strategic support of algebraic expression writing. In Kay, J. (Ed.) Proceedings of the Seventh International Conference on User Modeling, (pp. 149-158). SpringerWein, New York.
  4. Semi-Automated Coding Verbal Data Donmez, P., Rose, C. P., Stegmann, K., Weinberger, A., & Fischer, F. (2005). Supporting CSCL with Automatic Corpus Analysis Technology. To appear in the Proceedings of Computer Supported Collaborative Learning. Rose, C., P., Cohen, W., Donmez, P., Knight, A., Gweon, G., Heffernan, N., Junker, B. & Koedinger, K.R. (2005). Automatic and semi-automatic skill coding with a view towards supporting on-line assessment. Proceedings of the 12th International Conference on Artificial Intelligence in Education (AIED2005). Amsterdam, IOS Press.
  5. Mining On-line Course Data Scheines, R., Leinhardt, G., Smith, J., and Cho, K. (2005) Replacing lecture with web-based course materials. Journal of Eduational Computing Research, 32, 1, 1-26.