Research and practice models in education

Draft

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Introduction

Research and practice (R<-->P) models in education refer to frameworks and practice that articulate the relationship between theory and practice.

See also:

• design-based research
• Educational technology research approaches

The Burkhardt and Schoenfeld typology and model

This section summarizes some thoughts and findings from Burkhardt and Schoenfeld(2003)

Typology of research and practice in education

Model 1: Teachers read research and implement it in their classrooms.

• Doesn't work since teachers do not have time to read much research, make sense of it, and then use it productively in a classroom. (Magidson, 2002).

Model 2: Summary guides

• These guides are often produced by either professional organization or support centres. Not very explicit and not enough to be useful.

Model 3: General professional development

• Long-term professional development for teachers can be effective if text materials provided are consistent. (Briars, 2001; Briars & Resnick, 2000).

Model 4: The policy route.

• Doesn't work well, since accelerated diagnosis of causes is inevitably speculative, time scales are not effective, policy can outrun the research base, etc. (Dillon, 2003).

Model 5: The long route

• There can be a productive dialectic between educational research and practice. (E.g. Gardner, 1985; Senk & Thompson, 2002).)
• Time scale for substantial R<-->P impact in this case was 25 years, and that evidence on the real impact of such curricula is just beginning to accumulate.

Model 6: Design experiments.

• “Design experiments represent a much-needed melding of research and practice. Typically, however, they embody only the early ("alpha") stages of the design and refinement process” (Burkhardt and Schoenfeld, 2003: 4)
• See design-based research

A model for effective R<-->P

1. Robust mechanisms for taking ideas from laboratory scale to widely used practice.
2. Norms for research methods and reporting that are rigorous and consistent, resulting in a set of insights and/or prototype tools on which designers can rely. The goal, achieved in other fields, is cumulativity (p. 5)
3. A reasonably stable theoretical base
4. Teams of adequate size to grapple with large tasks, over the relatively long time scales required for sound work
5. Sustained funding to support the R<-->P process on realistic time scales
6. Individual and group accountability for ideas and products

Basically, we can summarise this as more research in Pasteur's quadrant (Stokes, 1997).

The Pasteur quadrant (Stokes, 1997)

	not science	science
applied	Edison (invention)	Pasteur (both)
not applied	PhD students ;)	Bohr (pure theory)

“Our point is that the same profitable dialectic between theory and practice can and should occur (with differing emphases on the R&D components) from the initial stages of design all the way through robust implementation on a large scale.” (Burkhardt and Schoenfeld, 2003: 5)

Bibliography

• Barret, Angeline et al. (2007). Initiatives to improve the quality of teaching and learning. A review of recent literature. Background paper prepared for the Education for All Global Monitoring Report 2008 Eucation for All by 2015: will we make it?, Unesco, PDF from psu.edu

• Briars,D . (March, 2001). Mathematics performance in the Pittsburgh public schools. Presentation at a Mathematic Assessment Resource Service conferenceo on tools for systemic improvement, San Diego, CA.

• Briars, D, & Resnick,L . (2000). Standards, assessments-and what else? The essential elements of standards-based school improvement. Pittsburgh, PA: University of Pittsburgh.

• BurkhardtH, ., FraserR, ., & Ridgway,J . (1990) The dynamics of curriculum change. In I. Wirszup& R. Streit (Eds.), Developments in school mathematics around the world, Vol.2 (pp. 3-30). Reston,VA: National Council of Teachers of Mathematics.

• Dillon, S. (2003, February6 ). Thousands of schools may run afoul of new law. New York Times. Retrieved September 12, 2003, from http://www.nytimes.com/2003/02/16/education/16EDUC.html

• Gardner, H. (1985). The mind's new science: A history of the cognitive revolution. New York: Basic Books.

• Burkhardt, Hugh and Alan H. Schoenfeld, Improving Educational Research: Toward a More Useful, More Influential, and Better-Funded Enterprise, Educational Researcher , Vol. 32, No. 9 (Dec., 2003), pp. 3-14 JStor

• Magidson, S. (2002). Teaching, research, and instructional design: Bridging communities in mathematics education. Dissertation Abstracts International 63/09, p. 3139. (UMI No. AAT 3063466)

• Pellegrino, J. W. (2001). Setting research agendas in science, mathematics, and technology education: The National Research Council’s How People Learn report. Proceedings of the Second AAAS Technology Education Research Conference. Retrieved from http://www.project2061.org/events/meetings/technology/tech2/Pellegrino.htm

• Senk, S. L., & Thompson, D. R. (Eds.). (2002). Standards-based school mathematics curricula: What are they? What do students learn? Mahwah, NJ: Erlbaum.

• Stokes, D. (1997). Pasteur's Quadrant: Basic science and technological innovation. Washington, DC: Brookings Institution Press.

• Vu NV, Bader CR, Vassalli JD. The redesigned undergraduate medical curriculum at the University of Geneva. In Scherpbier A, van der Vleuten C, Tethans J (Eds.), Advances in medical education (pp. 532–5). Dordrecht, The Netherlands: Kluwer, 1997.