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 a call for more use-based research (top/right) 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.

• Burkhardt, H, Fraser, R., & 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.