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Essentials of Computer-Based Diagnostics of Learning and Cognition

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Computer-Based Diagnostics and Systematic Analysis of Knowledge

Abstract

As a result of the rapid progress of computer technology in recent decades, researchers from different areas have adopted artificial intelligence to develop computer-aided instruction systems and diagnostic tools for the assessment of learning and cognition. Referring to the central questions on “What is knowledge?” and “How can we assess knowledge?” this introductory chapter will focus on some essentials of computer-based diagnostics of knowledge and cognition. First, some basic ideas of educational diagnostics and diagnoses are described, resulting in a distinction between “responsive” and “constructive” approaches of knowledge assessment. In the subsequent sections, computer-based procedures are described with regard to both approaches. They presuppose the application of external representations grounded on the semantics of natural language. The next section of this introduction focuses on computer-based and agent-based methodologies of knowledge diagnosis as a central component of automatic diagnostic systems. The final section will provide a brief preview of the major topics of this volume.

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References

  • Aebli, H. (1981). Denken: Das ordnen des Tuns. Band II: Denkprozesse. Stuttgart: Klett-Cotta. [Thinking: Sequencing of doing]

    Google Scholar 

  • Anderson, J. R. (1983). The architecture of cognition. Cambridge, MA: Harvard University Press.

    Google Scholar 

  • Brebner, J. M. T., & Welford, A. T. (1980). Introduction: An historical background sketch. In A. T. Welford (Ed.), Reaction times (pp. 1–23). New York: Academic Press.

    Google Scholar 

  • Bruner, J. (1990). Acts of meaning. Cambridge, MA: Harvard University Press.

    Google Scholar 

  • Clark, R. E. (2006). Not knowing what we don’t know: Reframing the importance of automated knowledge for educational research. In G. Clarebout & J. Elen (Eds.), Avoiding simplicity, confronting complexity. Advances in studying and designing (computer-based) powerful learning environments (pp. 3–14). Rotterdam, The Netherlands: Sense Publishers.

    Google Scholar 

  • Donders, F. C. (1869). On the speed of mental processes. In W. G. Koster (Ed.) (1969), /Attention a Performance II. Acta Psychologica, 30/, 412–431. (Original work published in 1868.)

    Google Scholar 

  • Frost, R. A. (1986). Introduction to knowledge base systems. London: Collins.

    Google Scholar 

  • Goodman, N. (1968). Languages of art. An approach to a theory of symbols. Indianapolis, IN: Bobbs-Merrill Comp.

    Google Scholar 

  • Gordon, A. D., & Jupp, P. E. (1989). The construction and assessment of mental maps. British Journal of Mathematical and Statistical Psychology, 42, 169–182.

    Google Scholar 

  • Hambleton, R. K., & Zaal, J. N. (Eds.). (1991). Advances in educational and psychological testing. Boston: Kluwer Academic Publishers.

    Google Scholar 

  • Helbig, H. (2006). Knowledge representation and the semantics of natural language. Berlin and New York: Springer.

    Google Scholar 

  • Howell, L. (2009). Web 3.0 has long since passed Web 2.0 in education for 2020. Retrieved 06-09-2009, from http://tagcblog.edublogs.org

  • Lajoie, S. (Ed.). (2000). Computers as cognitive tools: No more walls, II. Mahwah, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Larissa, O., & Hendler, J. (2007). Embracing “Web 3.0”. IEEE Internet Computing (May–June), 90–93.

    Google Scholar 

  • Liu, Y. C., Chiang, M. C., Chen, S. C., & Huang, T. H. (2007). An online system using dynamic assessment and adaptive material. Proceedings of the 37th ASEE/IEEE Frontiers in Education Conference, October 10–13, 2007, Milwaukee, WI.

    Google Scholar 

  • Markman, A. B. (1998). Knowledge representation. Mahwah, NJ: Erlbaum.

    Google Scholar 

  • Mislevy, R. J., Behrens, J. T., Bennett, R. E., Demark, S. F., Frezzo, D. C., Levy, R., et al. (2007). On the roles of external knowledge representations in assessment design. National Center for Research on Evaluation, Standards, and Student Testing (CRESST). Graduate School of Education and Information Studies. University of California, Los Angeles.

    Google Scholar 

  • Nisbett, R. E., & Wilson, T. D. (1977). Telling more than we can know: Verbal reports on mental processes. Psychological Review, 84, 231–259.

    Article  Google Scholar 

  • Overman, S. (Ed.). (1988). Methodology and epistemology for social science. Selected papers from Donald T. Campbell. Chicago: University of Chicago Press.

    Google Scholar 

  • Paivio, A. (1986). Mental representations: A dual coding approach. New York and Oxford: Oxford University Press.

    Google Scholar 

  • Prinz, W. (1983). Wahrnehmung und Tätigkeitssteuerung. Heidelberg: Springer. [Perception and action regulation]

    Google Scholar 

  • Rumelhart, D. E., & Norman, D. A. (1978). Accretion, tuning, and restructuring: Three models of learning. In J. U. Cotton & R. L. Klatzky (Eds.), Semantic facts in cognition (pp. 37–54). Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Rumelhart, D. E., Smolensky, P., McClelland, J. L., & Hinton, G. E. (1986). Schemata and sequential thought processes in PDP models. In J. L. McClelland, D. E. Rumelhart, & The PDP research group (Eds.), Parallel distributed processing. Explorations in the microstructure of cognition. Volume 2: Psychological and biological models (pp. 7–57). Cambridge, MA: MIT Press.

    Google Scholar 

  • Sarbadhikari, S. N. (2004). Automated diagnostic systems. Indian Journal of Medical Informatics, 1(1), 25–28.

    Google Scholar 

  • Searle, J. R. (1992). The rediscovery of the mind. Cambridge, MA: MIT Press.

    Google Scholar 

  • Seel, N. M. (1991). Mentale modelle und weltwissen. Göttingen: Hogrefe.

    Google Scholar 

  • Seel, N. M. (1995). Mental models, knowledge transfer, and teaching strategies. Journal of Structural Learning and Intelligent Systems, 12(3), 197–213.

    Google Scholar 

  • Seel, N. M. (1999). Educational semiotics: School learning reconsidered. Journal of Structural Learning and Intelligent Systems, 14(1), 11–28.

    Google Scholar 

  • Seel, N. M. (2008). Empirical perspectives on memory and motivation. In J. M. Spector, M. P. Driscoll, M. D. Merrill & J. van Merriënboer (Eds.), Handbook of research on educational communications and technology (3rd ed., pp. 39–54). Mahwah, NJ. Lawrence Erlbaum.

    Google Scholar 

  • Seel, N. M., & Ifenthaler, D. (2009). Online lernen und lehren. München: Rheinhardt Verlag.

    Google Scholar 

  • Seel, N. M., Ifenthaler, D., & Pirnay-Dummer, P. (2009). Mental models and problem solving: Technological solutions for measurement and assessment of the development of expertise. In P. Blumschein, W. Hung, D. H. Jonassen, & J. Strobel (Eds.), Model-based approaches to learning: Using systems models and simulations to improve understanding and problem solving in complex domains (pp. 17–40). Rotterdam: Sense Publishers.

    Google Scholar 

  • Seel, N. M., & Winn, W. D. (1997). Research on media and learning: Distributed cognition and semiotics. In R. D. Tennyson, F. Schott, S. Dijkstra, & N. M. Seel (Eds.), Instructional design international perspectives. Volume 1: Theories and models of instructional design (pp. 293–326). Hillsdale, NJ: Lawrence Erlbaum.

    Google Scholar 

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

    Article  Google Scholar 

  • van der Linden, W. J. (1991). Applications of decision theory to test-based decision making. In R. K. Hambleton, & J. N. Zaal (Eds.), Advances in educational and psychological testing (pp. 129–156). Boston, MA: Kluwer.

    Google Scholar 

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Correspondence to Norbert M. Seel .

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Seel, N.M. (2010). Essentials of Computer-Based Diagnostics of Learning and Cognition. In: Ifenthaler, D., Pirnay-Dummer, P., Seel, N. (eds) Computer-Based Diagnostics and Systematic Analysis of Knowledge. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-5662-0_1

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