The Role of Abductive Reasoning in Cognitive-Based Assessment

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.

Keywords

• Educational assessment,abductive reasoning,cognitive-based assessment

The Role of Abductive Reasoning in Cognitive-Based Assessment

Öz

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.

Anahtar Kelimeler

• Eğitsel değerlendirme, çıkarımsal uslamlama, bilişsel temelli değerlendirme

Kaynakça

1. Anderson, J. R. (1983). Cognitive psychology and its implications. San Francisco, CA: W. H. Freeman and Company.
2. Anderson, J. R. (1990). The Adaptive Character of Thought. Hillsdale, NJ: Lawrence Erlbaum Associates.
3. Anderson, J. R. (1993). Rules of the mind. Hillsdale, N.J.: L. Erlbaum Associates.
4. Anderson, J. R. (1996). ACT: A simple theory of complex cognition. American Psychologist, 51, 355-365.
5. Anderson, J. R. (2007). How can the human mind occur in the physical universe? Oxford: Oxford University Press.
6. 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.
7. Bacon, F. (1620/1960). The new organon, and related writings. New York: Liberal Arts Press.
8. 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.

9. Briggs, D., Alonzo, A., Schwab, C., & Wilson, M. (2006). Diagnostic assessment with ordered multiple-choice items. Educational Assessment, 11, 33-63.
10. 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
11. de Regt, H. (1994). Representing the world by scientific theories: The case for scientific realism. Tilburg, The Netherlands: Tilburg University Press.
12. 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.
13. 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.
14. Goldman, S. L. (2006). Science wars: What scientists know and how they know it [CD-ROM]. Chantilly, VA: Teaching Company.
15. Hempel, C. G. (1966). Philosophy of natural science. Englewood Cliffs, NJ: Prentice–Hall.
16. Hoffmann, M. (1997). Is there a logic of abduction? Paper presented at the 6th congress of the International Association for Semiotic Studies, Guadalajara, Mexico.
17. Kleiner, S. (2003). Explanatory coherence and empirical adequacy: The problem of abduction, and the justification of evolutionary models. Biology and Philosophy 18, 513–527.
18. Ladner, M., & Stone, G. (2007, June). A test of credibility: NAEP versus TerraNova test score results in Arizona (Technical report 07-04). Phoenix, AZ: Goldwater Institute.
19. Mislevy, R. (1994). Evidence in educational assessment. Psychometrika, 59, 439–483.
20. Myrstad, J. A. (2004). The use of converse abduction in Kepler. Foundations of Science, 9, 321-338.
21. National Research Council. (2004). Knowing what students know: The science and design of educational assessment. Washington DC: National Academic Press.
22. Peirce, C. S. (1934/1960). Collected papers of Charles Sanders Peirce. Cambridge: Harvard University Press.
23. Penrose, R. (1989). The emperor’s new mind: Concerning computers, minds, and the laws of physics. Oxford: Oxford University Press.
24. Penrose, R. (1994). Shadows of the minds: A search for the missing science of consciousness. Oxford: Oxford University Press.
25. Penrose, R. (1997). The large, the small, and the human mind. Oxford: Oxford University Press.
26. Polya, G. (1954a). Mathematic and plausible reasoning -- Volume I: Induction and analogy in mathematics. Princeton: Princeton University Press.
27. Polya, G. (1954b). Mathematic and plausible reasoning -- Volume II Patterns of plausible inference. Princeton: Princeton University Press.
28. Polya, G. (1957) How to solve it: A new aspect of mathematical method. Princeton: Princeton University Press.
29. Quine, W. V. (1982). Methods of logic. Cambridge, MA.: Harvard University Press.
30. Seifert, C. M., Meyer, D. E., Davidson, N., Patalano, A. L., & Yaniv, I. (1995). Demystification of cognitive insight: Opportunistic assimilation and the prepared-mind perspective. In R. J. Sternberg, & J. E. Davidson (Eds), The nature of insight (pp. 65-124). Cambridge, MA: MIT Press.
31. Thagard, P., & Shelley, C. (1997). Abductive reasoning: Logic, visual thinking, and coherence. In M. Chiara (Eds), Logic and scientific methods (pp. 413–427). Dordrecht: Kluwer.
32. Treagust, D. F. (1995). Diagnostic assessment of students’ science knowledge. In S. M. Glynn & R. Duit. (Eds), Learning science in the schools: research reforming practice (pp. 327-346). Mahwah, NJ: Lawrence Erlbaum Associates, Inc.
33. Trundle, R. (1994). Ancient Greek philosophy: Its development and relevance to our time. Brookfield: Avebury.
34. Tursman, R. (1987). Peirce’s theory of scientific discovery: A system of logic conceived as semiotic. Indianapolis, IN: Indiana University Press.
35. von Baeyer, H. C. (1989, Jan/.Feb). A dream come true. Sciences, 29(1), 6-8.
36. Williamson, D.M., Bauer, M., Steinberg, L.S., Mislevy, R.J., Behrens, J.T., & DeMark, S. (2004). Design rationale for a complex performance assessment. International Journal of Measurement, 4, 303-332.
37. Yu, C. H. (2006a). SAS programs for generating Winsteps control files and Web-based presentations. Applied Psychological Measurement, 30, 247-248.
38. Yu, C. H. (1994, April). Induction? Deduction? Abduction? Is there a logic of EDA? Paper presented at the Annual Meeting of American Educational Research Association, New Orleans, LA. (ERIC Document Reproduction Service No. ED 376 173)
39. Yu, C. H. (2006b). Philosophical foundations of quantitative research methodology. Lanham, MD: University Press of America.
40. Yu, C. H. (2007). Step functions in Winsteps. Retrieved January 8, 2008 from http://www.creative- wisdom.com/computer/sas/sf.htm
41. Yu, C. H., DiGangi, S., Jannasch-Pennell, A., & Gorin, J. (2007 October). A multidisciplinary approach to cognitive- based assessment. Paper presented at the Annual Meeting of EDUCAUSE. Seattle, WA.