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Journals / İlköğretim Online / 2008 / Cilt: 7 Sayı: 2

The Role of Abductive Reasoning in Cognitive-Based Assessment

Chong Ho YU
Samuel DİGANGİ
Angel JANNASCH-PENNELL
Pages
310–322
Publication Date
2008-06-26
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Third, analogical reasoning in the abductive reasoning mode is indispensable for cognitive modeling.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:“Knowing what the students know” recently has become the guiding principle of cognitive-based assessment.
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Key Findings & Data:In the past, the process of assessment was built around a form of quasi-deductive reasoning, in which test developers deduced items from certain premises, the blue print or the objective list.
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Conclusion & Impact:Third, analogical reasoning in the abductive reasoning mode is indispensable for cognitive modeling.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Eğitsel değerlendirme · çıkarımsal uslamlama · bilişsel temelli değerlendirme

Abstract

“Knowing what the students know” recently has become the guiding principle of cognitive-based assessment. In the past, the process of assessment was built around a form of quasi-deductive reasoning, in which test developers deduced items from certain premises, the blue print or the objective list. In this paper, the author(s) discuss how abductive reasoning contributes to cognitive-based assessment in three routes. First, knowing alternative explanations is essential in understanding different levels of conceptions and misconceptions in order to develop the constructs being measured. Second, converse reasoning or reverse engineering applied in an abductive fashion is employed to retrospectively build the student’s mental model based on the end product. Third, analogical reasoning in the abductive reasoning mode is indispensable for cognitive modeling.

Özet

Yarı-tümdengelim uslamlaması (yarı-tümdengelim akıl yürütme) yaklaşımına geçmişmişti dayanmakta iken, günümüzde, özellikle eğitsel değerlendirmenin geleceğine yönelik olarak, "öğrencilerin ne bildiklerini öğrenmek "olgusu bilişsel temelli değerlendirmeye yön. Bu makalede, bilişsel temelli değerlendirmek çıkarımsal uslamlama (kaçırma akıl yürütme) yaklaşımının üç temel açıdan nasıl kaktı sağladığı tartışılacaktır. Ilk olarak, ölçülen yapılar tam olarak anlamak için farklı seviylerdeki doğru ve yanlış algılamaları ortaya koymada alternatif açıklamalar göz önüne alınacaktır. İkincisi, çıkarım uslamlama (kaçırma akıl yürütme) yaklaşımla başvuran tersine uslamlama açıklanacak ve üçüncüsü, çıkarım uslamlama (kaçırma akıl yürütme) yaklaşımında analojik çıkarsama (analog akıl yürütme) tartılacaktır.

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Cite This Article

YU, C. H., DİGANGİ, S., JANNASCH-PENNELL, A. (2008). The Role of Abductive Reasoning in Cognitive-Based Assessment. İlköğretim Online, Cilt: 7 Sayı: 2, 310–322. https://ajindex.com/articles/999351

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References (11)

  1. Anderson, J. R. (1983). Cognitive psychology and its implications. San Francisco, CA: W. H. Freeman and Company. Anderson, J. R. (1990). The Adaptive Character of Thought. Hillsdale, NJ: Lawrence Erlbaum Associates. Anderson, J. R. (1993).
  2. Rules of the mind. Hillsdale, N.J.: L. Erlbaum Associates. Anderson, J. R. (1996). ACT: A simple theory of complex cognition. American Psychologist, 51, 355-365.
  3. Anderson, J. R. (2007). How can the human mind occur in the physical universe? Oxford: Oxford University Press.
  4. Anderson, J. R., Reder, L. M., & Simon, H. A. (2000). Applications and misapplications of cognitive psychology to mathematics instruction. Texas Education Review, 1, 29-49.
  5. Bacon, F. (1620/1960). The new organon, and related writings. New York: Liberal Arts Press.
  6. Behrens, J. T., Mislevy, R.J., Bauer, M., Williamson, D.M., & Levy, R. (2004). Introduction to evidence centered design and lessons learned from its application in a global E-Learning program. International Journal of Measurement, 4, 295-301.
  7. Briggs, D., Alonzo, A., Schwab, C., & Wilson, M. (2006). Diagnostic assessment with ordered multiple-choice items. Educational Assessment, 11, 33-63.
  8. California Department of Education. (2007). 2003 - 2006 CST Released Test Questions. Retrieved January 7, 2008 from http://www.cde.ca.gov/ta/tg/sr/css05rtq.asp de Regt, H. (1994). Representing the world by scientific theories: The case for scientific realism. Tilburg, The Netherlands: Tilburg University Press.
  9. Dufresne, W., Gerace, W., Mestre, J., & Leonard, W. (2000). Accessing student knowledge with instructional technology. Technical report PERG-2000#09-SEP. Boston, MA: University of Massachusetts. Gobet, F. (1996). Discrimination nets, production systems and semantic networks: elements of a unified framework. Proceedings of the 1996 International Conference on Learning sciences, Bloomington, IN, 398-403.
  10. Goldman, S. L. (2006). Science wars: What scientists know and how they know it [CD-ROM]. Chantilly, VA: Teaching Company. Hempel, C. G. (1966). Philosophy of natural science. Englewood Cliffs, NJ: Prentice–Hall. Hoffmann, M. (1997). Is there a logic of abduction? Paper presented at the 6th congress of the International Association for Semiotic Studies, Guadalajara, Mexico. Kleiner, S. (2003). Explanatory coherence and empirical adequacy: The problem of abduction, and the justification of evolutionary models. Biology and Philosophy 18, 513–527.
  11. Ladner, M., & Stone, G. (2007, June). A test of credibility: