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Articles

Programming Pluralism: Using Learning Analytics to Detect Patterns in the Learning of Computer Programming

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REFERENCES

  • Aleven, V., Roll, I., Bruce, M. M., & Kenneth, R. K. (2010). Automated, unobtrusive, action-by-action assessment of self-regulation during learning with an intelligent tutoring system. Educational Psychologist, 45(4), 224–233.
  • Baker, R. S. J., 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.
  • Baker, R. S. J., & 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.
  • Barron, B., & Darling-Hammond, L. (2010). Prospects and challenges for inquiry-based approaches to learning. In H. Dumont, D. Istance, & F. Benavides (Eds.), The nature of learning: Using research to inspire practice (pp. 199–225). Paris, France: OECD Publishing.
  • Basawapatna, A., Koh, K. H., Repenning, A., Webb, D., & Marshall, K. (2011). Recognizing computational thinking patterns. In T. J. Cortina, E. L. Walker, L. A. S. King, & D. R. Musicant (Eds.), Proceedings of the 42nd ACM Technical Symposium on Computer Science Education (pp. 245–250). New York, NY: ACM.
  • Beckwith, L., Kissinger, C., Burnett, M., Wiedenbeck, S., Lawrance, J., Blackwell, A., & Cook, C. (2006, April). Tinkering and gender in end-user programmers’ debugging . Paper presented at the SIGCHI Conference on Human Factors in Computing Systems, Montréal, Quebèc, Canada.
  • Berland, M. (2008). VBOT: Motivating computational and complex systems fluencies with constructionist virtual/physical robotics (Unpublished doctoral dissertation). Northwestern University, Evanston, IL.
  • Berland, M., & Martin, T. (2011, April). Clusters and patterns of novice programmers. Presentation at the annual meeting of the American Educational Research Association, New Orleans, LA.
  • Blikstein, P. (2009). An atom is known by the company it keeps: Content, representation and pedagogy within the epistemic revolution of the complexity sciences (Unpublished doctoral dissertation). Northwestern University, Evanston, IL.
  • Blikstein, P. (2011a, April). Learning analytics: Assessing constructionist learning using machine learning. Paper presented at the annual meeting of the American Educational Research Association, New Orleans, LA.
  • Blikstein, P. (2011b). Using learning analytics to assess students’ behavior in open-ended programming tasks. In P. Long, G. Siemens, G. Conole, & D. Ga[sbreve]ević (Eds.), Proceedings of the Learning Analytics Knowledge Conference (pp. 110–116). New York, NY: ACM.
  • Blikstein, P. (2013, April). Multimodal learning analytics. Paper presented at the Third International Conference on Learning Analytics and Knowledge, Leuven, Belgium.
  • Booth, S. A. (1992). Learning to program: A phenomenographic perspective (Göteborg Studies in Educational Sciences No. 89). Göteborg, Sweden: Acta Universitatis Gothoburgensis.
  • Bruce, C., Buckingham, L., Hynd, J., McMahon, C., Roggenkamp, M., & Stoodley, I. (2004). Ways of experiencing the act of learning to program: A phenomenographic study of introductory programming students at university. Journal of Information Technology Education, 3, 143–160.
  • Burnett, M. M., Beckwith, L., Wiedenbeck, S., Fleming, S. D., Cao, J., Park, T. H., … Rector, K. (2011). Gender pluralism in problem-solving software. Interacting With Computers, 23, 450–460. doi:10.1016/j.intcom.2011.06.004
  • College Board AP. (n.d.). Computer Science A: Course description. Retrieved from http://apcentral.collegeboard.com/apc/public/repository/ap-computer-science-course-description.pdf
  • Computing curricula 2001. (2001). Journal on Educational Resources in Computing, 1(3es), Article 1.
  • Cooper, S., Cassel, L., Cunningham, S., & Moskal, B. (2005). Outcomes-based computer science education. In Proceedings of the 36th SIGCSE Technical Symposium on Computer Science Education ( pp. 260–261). New York, NY: Association for Computing Machinery.
  • Cope, C. (2000). Educationally critical aspects of a deep understanding of the concept of an information system. In A. E. Ellis ( Ed.), Proceedings of the Australasian Conference on Computing Education ( pp. 48–55). New York, NY: Association for Computing Machinery.
  • Cutting, D. R., Karger, D. R., Pedersen, J. O., & Tukey, J. W. (1992). Scatter/gather: A cluster-based approach to browsing large document collections. In N. Belkin, P. Ingwesen, & A. M. Pejtersen (Eds.), Proceedings of the 15th annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 318–329). New York, NY: ACM.
  • Dehnadi, S. (2009). A cognitive study of learning to program in introductory programming courses (Doctoral thesis, Middlesex University, London, UK). Retrieved from http://eprints.mdx.ac.uk/6274/
  • Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society: Series B (Methodological), 39(1), 1–38.
  • Dewey, J. (1902). The school and society. Chicago, IL: University of Chicago Press.
  • diSessa, A. A. (2000). Changing minds: Computers, learning, and literacy. Cambridge, MA: MIT Press.
  • Dutson, A. J., Todd, R. H., Magleby, S. P., & Sorensen, C. D. (1997). A review of literature on teaching engineering design through project-oriented capstone courses. Journal of Engineering Education, 86(1), 17–28.
  • Dym, C. L. (1999). Learning engineering: Design, languages, and experiences. Journal of Engineering Education, 88(2), 145–148.
  • Dym, C. L., Agogino, A. M., Eris, O., Frey, D. D., & Leifer, L. J. (2005). Engineering design thinking, teaching, and learning. Journal of Engineering Education, 94(1), 103–120.
  • Fern, X., Komireddy, C., Grigoreanu, V., & Burnett, M. (2010). Mining problem-solving strategies from HCI data. ACM Transactions on Computer-Human Interaction, 17(1), 1–22. doi:10.1145/1721831.1721834
  • Freire, P. (1970). Pedagogia do oprimido [Pedagogy of the oppressed] (17th ed.). Rio de Janeiro, Brazil: Paz e Terra.
  • Fuller, U., Johnson, C., Ahoniemi, T., Cukierman, D., Hernán-Losada, I., Jackova, J., … Thompson, E. (2007). Developing a computer science-specific learning taxonomy. SIGCSE Bulletin, 39(4), 152–170.
  • Gall, H. C., Fluri, B., & Pinzger, M. (2009). Change analysis with evolizer and changedistiller. IEEE Software, 26(1), 26–33.
  • Ioannidou, A., Bennett, V., Repenning, A., Koh, K., & Basawapatna, A. (2011, April). Computational thinking patterns. Paper presented at the annual meeting of the American Educational Research Association, New Orleans, LA.
  • Jadud, M. (2006). Methods and tools for exploring novice compilation behaviour. In Proceedings of the Second International Workshop on Computing Education Research ( pp. 73–84). New York, NY: Association for Computing Machinery.
  • Kafai, Y. B. (2006). Playing and making games for learning: Instructionist and constructionist perspectives for game studies. Games and Culture, 1(1), 36 –40.
  • Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. Educational Psychologist, 41(2), 75–86.
  • Klahr, D., & Nigam, M. (2004). The equivalence of learning paths in early science instruction. Psychological Science, 15, 661–667.
  • Levy, F., & Murnane, R. J. (2004). The new division of labor: How computers are creating the next job market. Princeton, NJ: Princeton University Press.
  • Lister, R., Adams, E. S., Fitzgerald, S., Fone, W., Hamer, J., Lindholm, M., … Thomas, L. (2004). A multi-national study of reading and tracing skills in novice programmers. SIGCSE Bulletin, 36(4), 119 –150.
  • Maloney, J. H., Peppler, K., Kafai, Y., Resnick, M., & Rusk, N. (2008, March). Programming by choice: Urban youth learning programming with Scratch. ACM SIGCSE Bulletin, 40(1), 367 –371.
  • Marion, B., Impagliazzo, J., St. Clair, C., Soroka, B., & Whitfield, D. (2007). Assessing computer science programs: What have we learned. ACM SIGCSE Bulletin, 39(1), 131–132.
  • McCracken, M., Almstrum, V., Diaz, D., Guzdial, M., Hagen, D., Kolikant, Y., … Wilusz, T. (2001). A multi-national, multi-institutional study of assessment of programming skills of first-year CS students. SIGCSE Bulletin, 33(4), 1 –16.
  • Montessori, M. (1964). The advanced Montessori method. Cambridge, MA: R. Bentley.
  • Montessori, M. (1965). Spontaneous activity in education. New York, NY: Schocken Books.
  • National Research Council. (2012). A framework for K–12 science education: Practices, crosscutting concepts, and core ideas. Washington, DC: The National Academies Press.
  • Needleman, S. B., & Wunsch, C. D. (1970). A general method applicable to the search for similarities in the amino acid sequence of two proteins. Journal of Molecular Biology, 48, 443–453.
  • Nemirovsky, R. (2011). Episodic feelings and transfer of learning. Journal of the Learning Sciences, 20, 308–337.
  • Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. New York, NY: Basic Books.
  • Papert, S. (1987). Computer criticism vs. technocentric thinking. Educational Researcher, 16(1), 22–30.
  • Pattis, R. E. (1981). Karel the robot: A gentle introduction to the art of programming. New York, NY: Wiley.
  • Pea, R., Kurland, D. M., & Hawkins, J. (1987). Logo and the development of thinking skills. In R. Pea & K. Sheingold ( Eds.), Mirrors of mind. Norwood, NJ: Ablex.
  • Perkins, D. N., Hancock, C., Hobbs, R., Martin, F., & Simmons, R. (1986). Conditions of learning in novice programmers. Journal of Educational Computing Research, 2(1), 37–55.
  • Piech, C., Sahami, M., Koller, D., Cooper, S., & Blikstein, P. (2012). Modeling how students learn to program. In L. A. S. King, D. R. Musicant, T. Camp, & P. T. Tymann (Eds.), Proceedings of the 43rd ACM Technical Symposium on Computer Science Education (pp. 153–160). New York, NY: ACM.
  • Rabiner, L., & Juang, B. (1986). An introduction to hidden Markov models. ASSP Magazine, IEEE, 3(1), 4–16.
  • Roll, I., Aleven, V., & Koedinger, K. (2010). The invention lab: Using a hybrid of model tracing and constraint-based modeling to offer intelligent support in inquiry environments. Intelligent Tutoring Systems, 6094, 115–124.
  • Roll, I., Aleven, V., McLaren, B., & Koedinger, K. (2011). Metacognitive practice makes perfect: Improving students’ self-assessment skills with an intelligent tutoring system. Artificial Intelligence in Education, 6738, 288–295.
  • Salton, G., Wong, A., & Yang, C. S. (1975). A vector space model for automatic indexing. Communications of the ACM, 18, 613–620.
  • Schoenfeld, A. H., Smith, J., P., & Arcavi, A. (1991). Learning: The microgenetic analysis of one student’s evolving understanding of a complex subject matter domain. In R. Glaser ( Ed.), Advances in instructional psychology ( pp. 55–175). Hillsdale, NJ: Erlbaum.
  • Shawe-Taylor, J., & Cristianini, N. (2000). An introduction to support vector machines and other kernel-based learning methods. Cambridge, England: Cambridge University Press.
  • Siegler, R. S., & Crowley, K. (1991). The microgenetic method: A direct means for studying cognitive development. American Psychologist, 46, 606–620.
  • Simon, B., Bouvier, D., Chen, T., Lewandowski, G., McCartney, R., & Sanders, K. (2008). Commonsense computing (episode 4): Debugging. Computer Science Education, 18(2), 117–133.
  • Simon, B., Chen, T., Lewandowski, G., McCartney, R., & Sanders, K. (2006). Commonsense computing: What students know before we teach (episode 1: sorting). In Proceedings of the Second International Workshop on Computing Education Research ( pp. 29–40). New York, NY: Association for Computing Machinery.
  • Smyth, P. (1997). Clustering sequences with hidden Markov models. In M. C. Mozer, M. I. Jordan, & T. Petsche (Eds.), Advances in neural information processing systems 9: Proceedings of the 1996 conference (pp. 648–654). Cambridge, MA: MIT Press.
  • Soloway, E., & Ehrlich, K. (1984). Empirical studies of programming knowledge. IEEE Transactions on Software Engineering, SE-10, 595–609.
  • Soloway, E., & Spohrer, J. (1988). Studying the novice programmer. Hillsdale, NJ: Erlbaum.
  • Turkle, S., & Papert, S. (1992). Epistemological pluralism and the revaluation of the concrete. Journal of Mathematical Behavior, 11(1), 3 –33.
  • VanDeGrift, T., Bouvier, D., Chen, T., Lewandowski, G., McCartney, R., & Simon, B. (2010). Commonsense computing (episode 6): Logic is harder than pie. In Proceedings of the 10th Koli Calling International Conference on Computing Education Research ( pp. 76–85). New York, NY: Association for Computing Machinery.
  • Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33 –35.
  • Worsley, M., & Blikstein, P. (2013, April). Towards the development of multimodal action based assessment. Paper presented at the Third International Conference on Learning Analytics and Knowledge, Leuven, Belgium.

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