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Summer School Application

2015 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) 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 A.















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:







B) 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.

Appendix A - 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.