An investigation of explanation multiple-choice items in science assessment

Educational Assessment

Volumn 16, Issue 3, 2011, Pages 164-184

  Liu, Ou Lydia ^a   Lee, Hee Sun ^b   Linn, Marcia C ^b  

^a EDUCATIONAL TESTING SERVICE   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 80052533473     PISSN: 10627197     EISSN: None     Source Type: Journal    
DOI: 10.1080/10627197.2011.611702     Document Type: Article

References (53)

1. American Association for the Advancement of Science. (1993). Benchmarks for science literacy. New York: Oxford University Press.
2. Baker, F. (1971). Automation of test scoring, reporting and analysis. In R. L. Thorndike (Ed.), Educational measurement (pp. 203-234). Washington, DC: American Council on Education.
3. Bennett, R. E., Rock, D. A., & Wang, M. (1991). Equivalence of free-response and multiple-choice items. Journal of Educational Measurement, 28, 77-92.
4. Black, P., & Wiliam, D. (1998). Inside the black box: Raising standards through classroom assessment. Phi Delta Kappan, 80, 139-148.
5. Bridgeman, B., & Rock, D. (1993). Relationships among multiple-choice and open-ended analytical items. Journal of Educational Measurement, 30(4), 313-329.
6. Briggs, D., Alonzo, A., Schwab, C., & Wilson, M. (2006). Diagnostic assessment with ordered multiple-choice items. Educational Assessment, 11, 33-63.
7. Carraher, S. M., & Buckley, M. R. (1995). The effect of retention rule on the number of components retained: The case of cognitive complexity and the PSQ. Proceedings of the Southern Management Association, 474-476.
8. Clark, D., & Linn, M. C. (2003). Designing for knowledge integration: The impact of instructional time. Journal of the Learning Sciences, 12, 451-493.
9. Ebel, R. L. (1954). Procedures for the analysis of classroom tests. Educational and Psychological Measurement, 14, 352-364.
10. Ferrara, S., Huynh, H., & Michaels, H. (1999). Contextual explanations of local dependence in item clusters in a large scale hands-on science performance assessment. Journal of Educational Measurement, 36, 119-140.
11. Ferrara, S., Michaels, H., & Huynh, H. (1995). A beginning validation of causes of local item dependence in a large scale hands-on science performance assessment. Paper presented at the Annual Meeting of the National Council on Measurement in Education, San Francisco, CA.
12. Heubert, J. P., & Hauser, P. M. (1999, April). High-stakes testing for tracking, promotion, and graduation. Washington, DC: National Academy Press.
13. Horn, J. L. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 30, 179-185.
14. International Association for the Evaluation of Educational Achievement. (1995a). TIMSS science items: Released set for population 1 (third and fourth grades). Chestnut Hill, MA: Boston College.
15. International Association for the Evaluation of Educational Achievement. (1995b). TIMSS 1999 science items: Released set for population 2 (seventh and eighth grades). Chestnut Hill, MA: Boston College.
16. International Association for the Evaluation of Educational Achievement. (1999). TIMSS 2003 science items: Released set eighth grade. Chestnut Hill, MA: Boston College.
17. International Association for the Evaluation of Educational Achievement. (2003). TIMSS science items: Released set for eighth grade. Chestnut Hill, MA: Boston College.
18. Kelley, T., Ebel, R., & Linacre, J. M. (2002). Item discrimination indices. Rasch Measurement Transactions, 16, 883-884.
19. Kennedy, P., & Walstad, W. B. (1997). Combiningmultiple-choice and constructed-response test scores: An economist's view. Applied Measurement in Education, 10, 359.
20. Klein, S., Liu, O. L., Sconing, J., Bolus, R., Bridgeman, B., Kugelmass S, et al. (2009, September 29). Test Validity Study (TVS) report: Supported by the Fund for Improvement of Postsecondary Education (FIPSE). Retrieved from http://www.voluntarysystem.org/index.cfm?page=research
21. Kolen, M. J., & Brennan, R. L. (2004). Test equating, scaling, and linking: Methods and practices (2nd ed.). New York: Springer-Verlag.
22. Lee, H. S., & Liu, O. L. (2010). Assessing learning progression of energy concepts across middle school grades: The knowledge integration perspective. Science Education, 94, 665-688.
23. Lee, H. S., Varma, K., Linn, M. C., & Liu, O. L. (2010). Impact of visualization-based inquiry science experience on classroom learning. Journal of Research in Science Teaching, 47, 71-90.
24. Linn, M. C. (1995). Designing computer learning environments for engineering and computer science: The Scaffolded Knowledge Integration framework. Journal of Science Education and Technology, 4, 103-126.
25. Linn, M. C., Davis, E. A., & Bell, P. (Eds.). (2004). Internet environments for science education. Mahwah, NJ: Erlbaum.
26. Linn, M. C., & Eylon B.-S. (in press). Science learning and instruction: Taking advantage of technology to promote knowledge integration. New York: Routledge.
27. Linn, M. C., & Hsi, S. (2000). Computers, teachers, peers: Science learning partners. Mahwah, NJ: Erlbaum.
28. Linn, M. C., Lee, H.-S., Tinker, R., Husic, F., & Chiu, J. L. (2006). Teaching and assessing knowledge integration in science. Science, 313, 1049-1050.
29. Liu, O. L., Lee, H. S., Hofstetter, C., & Linn, M. C. (2008). Assessing knowledge integration in science: Construct, measures and evidence. Educational Assessment, 13, 33-55.
30. Livingston, S. (2009). Constructed-response test questions: Why we use them; How we score them (ETS R&D Connections, RD-11-09). Princeton, NJ: Educational Testing Service.
31. Lukhele, R., Thissen, D., & Wainer, H. (1994). On the relative value of multiple-choice, constructed response, and examinee-selected items on two achievement tests. Journal of Educational Measurement, 31, 234-250.
32. Madaus, G. F., & O'Dwyer, L. M. (1999, May). A short history of performance assessment. Phi Delta Kappan, pp. 688-695.
33. Masters, G. (1982). A Rasch model for partial crediting scoring. Psychometrika, 49, 359-381.
34. National Research Council. (1996). National science education standards. Washington, DC: Author.
35. Nichols, P., & Sugrue, B. (1999). The lack of fidelity between cognitively complex constructs and conventional test development practice. Educational Measurement: Issues and Practice, 18, 18-29.
36. Resnick, L. B., & Resnick, D. P. (1992). Assessing the thinking curriculum: New tools for educationa reform. In B. R. Gifford & M. C. O'Conner (Eds.), Changing assessments: Alternative views of aptitude, achievement and instruction (pp. 37-76). Boston: Kluwer Academic.
37. Resnick, L. B., & Zurawsky, C. (2007). Science education that makes sense. American Educational Research Association Research Points, 5, 1.
38. Rodriguez, M. C. (2003). Construct equivalence of multiple-choice and constructed-response items: A random effects synthesis of correlations. Journal of Educational Measurement, 40, 163-184.
39. Roediger, H. L., III, & Marsh, E. J. (2005). The positive and negative consequences of multiple-choice testing. Journal of Experimental Psychology: Learning, Memory, and Cognition, 31, 1155-1159.
40. Sadler, P. M. (1998). Psychometric models of student conceptions in science: Reconciling qualitative studies and distractor-driven assessment instruments. Journal of Research in Science Teaching, 35, 265-296.
41. Shepard, L. A. (2000). The role of assessment in a learning culture. Educational Researcher, 29, 4-14.
42. Shepard, L. A., Flexer, R. J., Hiebert, E. H., Marion, S. F., Mayfield, V., & Weston, T. J. (2005). Effects of introducing classroom performance assessments on student learning. Educational Measurement: Issues and Practice, 15, 7-18.
43. Sisk-Hilton, S. (2009). Teaching and learning in public: Professional development through shared inquiry. New York: Teachers College Press.
44. Tamir, P. (1989). Some issues related to the use of justifications to multiple-choice answers. Journal of Biological Education, 23, 285-292.
45. Thissen, D., Wainer, H., & Wang, X. B. (1994). Are tests comprising both multiple-choice and free-response items necessarily less unidimensional than multiple-choice tests? An analysis of two tests. Journal of Educational Measurement, 31, 113-123.
46. Treagust, D. F. (1989). Development and use of diagnostic tests to evaluate students' misconceptions in science. International Journal of Science Education, 19, 159-169.
47. 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: Erlbaum.
48. Treagust, D. F. (2006, September). Diagnostic assessment in science as a means to improving teaching, learning and retention. Paper presented at the 2006 National UniServe Conference (Symposium of Science Teaching and Learning Research). Sydney, Australia.
49. Wainer, H., & Thissen, D. (1993). Combining multiple-choice and constructed-response test scores: Toward a Marxist theory of test construction. Applied Measurement in Education, 6, 103-118.
50. Wilson, M., & Wang, W. C. (1995). Complex composites: Issues that arise in combining different modes of assessment. Applied Psychological Measurement, 19, 51-71.
51. Wright, B., & Linacre, J. M. (1994). Reasonable mean-square fit values. Rasch Measurement Transactions, 8, 370.
52. Wu, M., Adams, R. J., Wilson, M., & Haldane, S. (2007). ACER ConQuest 2.0 [Computer program]. Hawthorn, Australia: ACER.
53. Yen, W. M. (1993). Scaling performance assessments: Strategies for managing local item dependence. Journal of Educational Measurement, 30(3), 187-213.