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Suggested Citation:"References." National Research Council. 2001. Knowing What Students Know: The Science and Design of Educational Assessment. Washington, DC: The National Academies Press. doi: 10.17226/10019.
×

References

CHAPTER 1

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CHAPTER 3

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Barron, B.J.S., Vye, N., Zech, L., Schwartz, D., Bransford, J., Goldman, S., Pellegrino, J., Morris, J., Garrison, S., and Kantor, R. (1995). Creating contexts for community based problem solving: The Jasper Challenge series. In C.Hedley, P.Antonacci, and M.Rabinowitz (Eds.), Thinking and literacy: The mind at work. Hillsdale, NJ: Lawrence Erlbaum Associates.

Bell, P. (1997). Using argument representations to make thinking visible for individuals and groups. In R.Hall, N.Miyake, and N.Enyedy (Eds.), Proceedings of CSCL ’97: The second international conference on computer support for collaborative learning (pp. 10–19). Toronto: University of Toronto Press.

Bennett, R.E. (1999). Using new technology to improve assessment. Educational Measurement: Issues and Practice, 18(3), 5–12.

Bruckman, A. (1998). Community support for constructionist learning. Computer Supported Cooperative Work: The Journal of Collaborative Computing, 7, 47–86.


Casillas, A., Clyman, S., Fan Y., and Stevens, R. (2000). Exploring alternative models of complex patient management with artificial neural networks. Advances in Health Sciences Education 5, 23–41.

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Chang, H., Henriques, A., Honey, M., Light, D., Moeller, B., and Ross, M. (1998). The union city story: Technology and education reform. New York: Center for Children and Technology, Education Development Corporation.

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Cognition and Technology Group at Vanderbilt University. (1997). The Jasper Project: Lessons in curriculum, instruction, assessment, and professional development. Hillsdale, NJ: Erlbaum.

Conati, C, and VanLehn, K. (1999). Teaching meta-cognitive skills: Implementation and evaluation of a tutoring system to guide self-explanation while learning from examples. Proceedings of AIED Œ99, 9th World Conference of Artificial Intelligence and Education, 1999, Le Mans, France. 297–304. Winner of the Outstanding Paper Award, <http://www.cs.ubc.ca/conati/aied99.doc>. [February 9, 2001].

Dede, C. (2000). Emerging influences of information technology on school curriculum. Journal of Curriculum Studies, 32(2), 281–304.


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Hickey, D.T., Kindfield, A.C.H., and Horwitz, P. (1999). Large-scale implementation and assessment of the GenScope™ learning environment: Issue, solutions, and results. Paper presented at the meeting of the European Association for Research on Learning and Instruction, Goteborg, Sweden, August.

Horwitz, P. (1998). The inquiry dilemma: How to assess what we teach. Concord, Winter, 9–10.

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Katz, I.R., Martinez, M.E., Sheehan, K.M., and Tatsuoka, K.K. (1993). Extending the rule space model to a semantically-rich domain: Diagnostic assessment in architecture. Princeton, NJ: Educational Testing Service.

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Landauer, T.K. (1998). Learning and representing verbal meaning: The latent semantic analyasis theory. Current Directions in Psychological Science, 7(5), 161–164.

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Lawton, M. (1998, October 1). Making the most of assessments. (Case study number 9.) Education week. Technology counts ‘98.

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Mislevy, R.J., Steinberg, L.S., Breyer, F.J., Almond, R.G., and Johnson, L. (1999). A cognitive task analysis, with implications for designing a simulation-based performance assessment. Presented at the annual meeting of the American Educational Research Association, Montreal, Canada, April 1999.

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National Research Council. (1999b). Being fluent with information technology. Committee on Information Technology Literacy. Computer Science and Telecommunications Board. Commission on Physical Sciences, Mathematics, and Applications. Washington, DC: National Academy Press.

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O’Neil, H.F., and Klein, D.C.D. (1997). Feasibility of machine scoring of concept maps. (CSE Technical Report 460). Los Angeles, CA: Center for Research on Evaluation, Standards, and Student Testing, University of California.


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Vye, N.J., Schwartz, D.L., Bransford, J.D., Barren, B.J., Zech, L., and Cognition and Technology Group at Vanderbilt. (1998). SMART environments that support monitoring, reflection, and revision. In D.Hacker, J.Dunlosky, and A.Graesser (Eds.), Metacognition in educational theory and practice (pp. 305–346). Mahwah, NJ: Erlbaum.


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White, B.Y., and Frederiksen, J.R. (2000). Metacognitive facilitation: An approach to making scientific inquiry accessible to all. In J.Minstrell and E.van Zee (Eds.), Teaching in the inquiry-based science classroom. Washington, DC: American Association for the Advancement of Science.

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Next: Appendix Biographical Sketches »
Knowing What Students Know: The Science and Design of Educational Assessment Get This Book
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Education is a hot topic. From the stage of presidential debates to tonight's dinner table, it is an issue that most Americans are deeply concerned about. While there are many strategies for improving the educational process, we need a way to find out what works and what doesn't work as well. Educational assessment seeks to determine just how well students are learning and is an integral part of our quest for improved education.

The nation is pinning greater expectations on educational assessment than ever before. We look to these assessment tools when documenting whether students and institutions are truly meeting education goals. But we must stop and ask a crucial question: What kind of assessment is most effective?

At a time when traditional testing is subject to increasing criticism, research suggests that new, exciting approaches to assessment may be on the horizon. Advances in the sciences of how people learn and how to measure such learning offer the hope of developing new kinds of assessments-assessments that help students succeed in school by making as clear as possible the nature of their accomplishments and the progress of their learning.

Knowing What Students Know essentially explains how expanding knowledge in the scientific fields of human learning and educational measurement can form the foundations of an improved approach to assessment. These advances suggest ways that the targets of assessment-what students know and how well they know it-as well as the methods used to make inferences about student learning can be made more valid and instructionally useful. Principles for designing and using these new kinds of assessments are presented, and examples are used to illustrate the principles. Implications for policy, practice, and research are also explored.

With the promise of a productive research-based approach to assessment of student learning, Knowing What Students Know will be important to education administrators, assessment designers, teachers and teacher educators, and education advocates.

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