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Connection, Translation, Off-Loading, and Monitoring: A Framework for Characterizing the Pedagogical Functions of Educational Technologies

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Abstract

Distributed cognition offers powerful tools for conceptualizing the role that technology plays in learning environments, yet it can be challenging to apply. This paper presents an analytical framework that focuses on four pedagogical functions that technology can perform in learning environments: connection, translation, off-loading, and monitoring. The framework is drawn from theories of distributed cognition and, in particular, the idea that learning is increased coordination between two cognitive systems. Each pedagogical function is first explicated individually, along with examples. The framework is then applied to several cases, including three technology development and research cases drawn from the literature. The paper concludes with a summary of the strengths and weaknesses of the framework for use in research and design.

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Notes

  1. Although the focus here is on pedagogical functions, the same functions could certainly be used in service of non-pedagogical goals.

  2. The choice of labels is always somewhat arbitrary, and alternative labels, such as access or contact, could also be appropriate here.

  3. Assessment would also be a reasonable label for this function. The choice of the term monitoring is inspired by its use in research on tutoring (e.g., Chi et al. 2004).

  4. In each case, by “system of rules,” I mean the rules as coded into the software, rather than ahistorical mathematical or scientific truths. Although the rules are in fact embedded within the larger software system, separating them analytically makes it easier to employ the metaphor of coordination.

References

  • Amory, A. (2007). Game object model version II: A theoretical framework for educational game development. Educational Technology Research and Development, 55(1), 51–77.

    Article  Google Scholar 

  • Anderson, J. R., Corbett, A. T., Koedinger, K. R., & Pelletier, R. (1995). Cognitive tutors: Lessons learned. The Journal of the Learning Sciences, 4(2), 167–207.

    Article  Google Scholar 

  • Artigue, M., Cerulli, M., Haspekian, M., & Maracci, M. (2009). Connecting and integrating theoretical frames: The TELMA contribution. International Journal of Computers for Mathematical Learning, 14(3), 217–240.

    Article  Google Scholar 

  • Barr, D. J., & Keysar, B. (2007). Perspective taking and the coordination of meaning in language use. In M. J. Traxler & M. A. Gernsbacher (Eds.), Handbook of psycholinguistics (2nd ed., pp. 901–938). New York: Academic Press.

    Google Scholar 

  • Boyd, D. & Ellison, N. (2007). Social network sites: Definition, history, and scholarship. Journal of Computer-Mediated Communication, 13(1), 210–230.

    Google Scholar 

  • Brey, P. (2005). The epistemology and ontology of human–computer interaction. Minds and Machines, 15, 383–398.

    Article  Google Scholar 

  • Brown, J. S., & Burton, R. R. (1978). Diagnostic models for procedural bugs in basic mathematical skills. Cognitive Science, 2(2), 155–192.

    Article  Google Scholar 

  • Bush, V. (1945). As we may think. The Atlantic Monthly, 176(1), 101–108.

    Google Scholar 

  • Carr, N. (2011). The shallows: What the Internet is doing to our brains. New York: WW Norton & Co Inc.

    Google Scholar 

  • Chi, M. T. H., Siler, S. A., & Jeong, H. (2004). Can tutors monitor students’ understanding accurately? Cognition and Instruction, 22(3), 363–387.

    Article  Google Scholar 

  • Chin, D. B., Dohmen, I. S., Cheng, B. H., Oppezzo, M. A., Chase, C. C., & Schwartz, D. L. (2010). Preparing students for future learning with teachable agents. Educational Technology Research and Development, 58(6), 649–669.

    Article  Google Scholar 

  • Clark, H. H. (1996). Using language. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Cole, M., & Griffin, P. (1980). Cultural amplifiers reconsidered. In D. R. Olson (ed.), The social foundations of language and thought: Essays in honor of Jerome S. Bruner. W. W. Norton and Company: New York.

  • Collins, A., Brown, J. S., & Newman, S. E. (1989). Cognitive apprenticeship: Teaching the craft of reading, writing, and mathematics. In L. B. Resnick (Ed.), Knowing, learning, and instruction: Essays in honor of Robert Glaser (pp. 453–494). Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.

    Google Scholar 

  • diSessa, A. A. (1995). Designing Newton’s laws: Patterns of social and representational feedback in a learning task. In R.-J. Beun, M. Baker, & M. Reiner (Eds.), Dialogue and interaction: Modeling interaction in intelligent tutoring systems (pp. 105–122). Berlin: Springer.

    Google Scholar 

  • Edelson, D. C., Gordin, D. N., & Pea, R. D. (1999). Addressing the challenges of inquiry-based learning through technology and curriculum design. Journal of the Learning Sciences, 8(3/4), 391–450.

    Google Scholar 

  • Ellington, A. J. (2003). A meta-analysis of the effects of calculators on students’ achievement and attitude levels in precollege mathematics classes. Journal for Research in Mathematics Education, 34(5), 433–463.

    Article  Google Scholar 

  • Gladwell, M. (2000). The tipping point: How little things can make a big difference. New York: Little Brown and Company.

    Google Scholar 

  • Hall, R., Stevens, R., & Torralba, A. (2002). Disrupting representational infrastructure in conversations across disciplines. Mind, Culture, and Activity, 9(3), 179–210.

    Article  Google Scholar 

  • Halverson, C. A. (2002). Activity theory and distributed cognition: Or what does CSCW need to do with theories? Computer Supported Cooperative Work, 11, 243–267.

    Article  Google Scholar 

  • Harrison, A., & Treagust, D. (2006). Teaching and learning with analogies. In P. Aubusson, A. Harrison, & S. Ritchie (Eds.), Metaphor and analogy in science education (Vol. 30, pp. 11–24). Dordrecht: Springer.

    Chapter  Google Scholar 

  • Herscovics, N., & Linchevski, L. (1994). A cognitive gap between arithmetic and algebra. Educational Studies in Mathematics, 27, 59–78.

    Article  Google Scholar 

  • Hutchins, E. (1995a). Cognition in the wild. Cambridge, MA: The MIT Press.

    Google Scholar 

  • Hutchins, E. (1995b). How a cockpit remembers its speeds. Cognitive Science, 19, 265–288.

    Article  Google Scholar 

  • Jones, C., Dirckinck-Holmfeld, L., & Lindström, B. (2006). A relational, indirect, meso-level approach to CSCL design in the next decade. International Journal of Computer-Supported Collaborative Learning, 1, 35–56.

    Article  Google Scholar 

  • Kaput, J. J. (1992). Technology and mathematics education. In D. A. Grouws (Ed.), Handbook of teaching and learning mathematics (pp. 515–556). New York: Macmillan.

    Google Scholar 

  • Kirschner, P. A., Strijbos, J., & Martens, R. L. (2004). CSCL in higher education. In J. A., Strijbos, P. A. Kirschner, & R. L. Martens (Eds.), What we know about CSCL: And implementing it in higher education. Boston, MA: Kluwer.

  • Kirsh, D. (1996). Adapting the environment instead of oneself. Adaptive Behavior, 4, 415–452.

    Article  Google Scholar 

  • Kobbe, L., Weinberger, A., Dillenbourg, P., Harrer, A., Hamalainen, R., Hakkinen, P., et al. (2007). Specifying computer-supported collaboration scripts. International Journal of Computer-Supported Collaborative Learning, 2(2), 211–224.

    Article  Google Scholar 

  • Latour, B. (2005). Reassembling the social: An introduction to actor-network-theory. USA: Oxford University Press.

    Google Scholar 

  • Malone, T. W., & Crowston, K. (2001). The interdisciplinary study of coordination. In G. M. Olson, T. W., Malone, & J. B. Smith (Eds.), Coordination theory and collaborative technology (pp. 7–50). Mahwah, NJ: Lawrence Erlbaum Associates.

  • Mehan, H. (1989). Microcomputers in classrooms: Educational technology or social practice? Anthropology & Education Quarterly, 20(1), 4–22.

    Article  Google Scholar 

  • Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers college record, 108(6), 1017–1054.

    Article  Google Scholar 

  • Paavola, S., Lipponen, L., & Hakkarainen, K. (2004). Models of innovative knowledge communities and three metaphors of learning. Review of Educational Research, 74(4), 557–576.

    Article  Google Scholar 

  • Pea, R. D. (1985). Beyond amplification: Using the computer to reorganize mental functioning. Educational Psychologist, 20(4), 167–182.

    Article  Google Scholar 

  • Pea, R. D. (1993). Practices of distributed intelligence and designs for education. In G. Salomon (Ed.), Distributed cognitions (pp. 47–87). New York: Cambridge University Press.

    Google Scholar 

  • Pea, R. D. (2004). The social and technological dimensions of scaffolding and related theoretical concepts for learning, education, and human activity. Journal of the Learning Sciences, 13(3), 423–451.

    Article  Google Scholar 

  • Piaget, J. (1950). The psychology of intelligence. New York: Harcourt Brace.

    Google Scholar 

  • Quintana, C., Reiser, B. J., Davis, E. A., Krajcik, J., Fretz, E., Duncan, R. G., et al. (2004). A scaffolding design framework for software to support science inquiry. The Journal of the Learning Sciences, 13(3), 337–386.

    Article  Google Scholar 

  • Resnick, M. (1998). Technologies for lifelong kindergarten. Educational Technology Research and Development, 46(4), 43–55. doi:10.1007/bf02299672.

    Article  Google Scholar 

  • Rogers, Y. (2004). New theoretical approaches for HCI. Annual Review of Information Science and Technology, 38(1), 87–143.

    Article  Google Scholar 

  • Roschelle, J., Rafanan, K., Bhanot, R., Estrella, G., Penuel, B., Nussbaum, M., et al. (2010). Scaffolding group explanation and feedback with handheld technology: Impact on students’ mathematics learning. Educational Technology Research and Development, 58(4), 399–419.

    Article  Google Scholar 

  • Salomon, G., & Perkins, D. (2005). Do technologies make us smarter? Intellectual amplification with, of, and through technology. In R. J. Sternberg & D. D. Preiss (Eds.), Intelligence and technology: The impact of tools on the nature and development of human abilities. Mahwah, NJ: Lawrence Erlbaum Associates, Publishers.

    Google Scholar 

  • Salomon, G., Perkins, D. N., & Globerson, T. (1991). Partners in cognition: Extending human intelligence with intelligent technologies. Educational Researcher, 20(3), 2–9.

    Google Scholar 

  • Sarama, J., & Clements, D. H. (2009). “Concrete” computer manipulatives in mathematics education. Child Development Perspectives, 3, 145–150.

    Article  Google Scholar 

  • Schoenfeld, A. H. (2011). How we think: A theory of goal-oriented decision making and its educational applications. New York: Routledge.

    Google Scholar 

  • Schwartz, D. L., & Martin, T. (2004). Inventing to prepare for future learning: The hidden efficiency of encouraging original student production in statistics instruction. Cognition and Instruction, 22(2), 129–184.

    Article  Google Scholar 

  • Sfard, A. (1998). On two metaphors for learning and the dangers of choosing just one. Educational Researcher, 27(2), 4–13.

    Google Scholar 

  • Shaffer, D. W., & Clinton, K. A. (2006). Toolforthoughts: Reexamining thinking in the digital age. Mind, Culture, and Activity, 13(4), 283–300.

    Article  Google Scholar 

  • Sweller, J. (1994). Cognitive load theory, learning difficulty, and instructional design. Learning and Instruction, 4(4), 295–312.

    Article  Google Scholar 

  • Takeuchi, L. (2007). Toward authentic scientific practice: Comparing the use of GIS in the classroom and laboratory. In C. Chinn, G. Erkens, & S. Puntambekar (Eds.), Proceedings of the computer supported collaborative learning conference (CSCL) 2007. New Brunswick, NJ.

  • Vygotsky, L. S. (1978). Mind in society. Cambridge, MA: Harvard University Press.

    Google Scholar 

  • White, T. (2008). Debugging an artifact, instrumenting a bug: Dialectics of instrumentation and design in technology-rich learning environments. International Journal of Computers for Mathematical Learning, 13(1), 1–26.

    Article  Google Scholar 

  • White, T., Wallace, M., & Lai, K. (2012). Graphing in groups: Learning about lines in a collaborative classroom network environment. Mathematical Thinking and Learning, 14(2), 149–172.

    Article  Google Scholar 

  • Wilensky, U. (1999). NetLogo. http://ccl.northwestern.edu/netlogo/. Center for Connected Learning and Computer-Based Modeling, Northwestern University. Evanston, IL.

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Martin, L. Connection, Translation, Off-Loading, and Monitoring: A Framework for Characterizing the Pedagogical Functions of Educational Technologies. Tech Know Learn 17, 87–107 (2012). https://doi.org/10.1007/s10758-012-9193-6

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