Research in the area of educational technology has often been critiqued for a lack of theoretical grounding. In this article we propose a conceptual framework for educational technology by building on Shulman’s formulation of ‘‘pedagogical content knowledge’’ and extend it to the phenomenon of teachers integrating technology into their pedagogy. This framework is the result of 5 years of work on a program of research focused on teacher professional development and faculty development in higher education. It attempts to capture some of the essential qualities of teacher knowledge required for technology integration in teaching, while addressing the complex, multifaceted, and situated nature of this knowledge. We argue, briefly, that thoughtful pedagogical uses of technology require the development of a complex, situated form of knowledge that we call Technological Pedagogical Content Knowledge (TPCK). In doing so, we posit the complex roles of, and interplay among, three main components of learning environments: content, pedagogy, and technology. We argue that this model has much to offer to discussions of technology integration at multiple levels: theoretical, pedagogical, and methodological. In this article, we describe the theory behind our framework, provide examples of our teaching approach based upon the framework, and illustrate the methodological contributions that have resulted from this work.

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Technological Pedagogical Content Knowledge: A Framework for Teacher Knowledge

In the midst of discussions about improving education, teacher education, equity, and diversity, little has been done to make pedagogy a central area of investigation. This article attempts to challenge notions about the intersection of culture and teaching that rely solely on microanalytic or macroanalytic perspectives. Rather, the article attempts to build on the work done in both of these areas and proposes a culturally relevant theory of education. By raising questions about the location of the researcher in pedagogical research, the article attempts to explicate the theoretical framework of the author in the nexus of collaborative and reflexive research. The pedagogical practices of eight exemplary teachers of African-American students serve as the investigative “site.” Their practices and reflections on those practices provide a way to define and recognize culturally relevant pedagogy.

https://www.jstor.org/stable/pdfplus/1163320.pdf

Toward a Theory of Culturally Relevant Pedagogy

1 On behalf of NASA’s Mars Exploration Program, this lesson was prepared by Arizona State University’s Mars Education Program, under contract to NASA’s Jet Propulsion Laboratory, a division of the California Institute of Technology. These materials may be distributed freely for non-commercial purposes. Copyright 2014; 2012; 2010; 2000. Last edited: April 24, 2014 Marsbound! Mission to the Red Planet

A Framework for K-12 Science Education: Practices, Crosscutting Concepts, and Core Ideas

Teacher professional development is essential to efforts to improve our schools. This article maps the terrain of research on this important topic. It first provides an overview of what we have learned as a field, about effective professional development programs and their impact on teacher learning. It then suggests some important directions and strategies for extending our knowledge into new territory of questions not yet explored.

Professional Development and Teacher Learning: Mapping the Terrain

National Science Education Standards

In this article, the authors first indicate the range of purposes and the variety of settings in which design experiments have been conducted and then delineate five crosscutting features that collectively differentiate design experiments from other methodologies. Design experiments have both a pragmatic bent—“engineering” particular forms of learning—and a theoretical orientation—developing domain-specific theories by systematically studying those forms of learning and the means of supporting them. The authors clarify what is involved in preparing for and carrying out a design experiment, and in conducting a retrospective analysis of the extensive, longitudinal data sets generated during an experiment. Logistical issues, issues of measure, the importance of working through the data systematically, and the need to be explicit about the criteria for making inferences are discussed.

Design Experiments in Educational Research

Social studies of scientific practice reveal considerable diversity in the methods and material means of production across scientific disciplines (e.g., Galison & Stump, 1995). Yet, in spite of this diversity and regardless of their domain, scientists' work involves building and refining models of the world (Giere, 1988; Hestenes, 1992; Stewart & Golubitsky, 1992). Scientific ideas derive their power from the models that instantiate them, and theories change as a result of efforts to invent, revise, and stage competitions among models. These efforts are mobilized to support socially grounded arguments about the nature of physical reality, so model-based reasoning is embedded within a wider world that includes networks of participants and institutions (Latour, 1999); specialized ways of talking and writing (Bazerman, 1988); development of representations that render phenomena accessible, visualizable, and transportable (Gooding, 1989; Latour, 1990); and efforts to manage material contingency, because no model specifies instrumentation and measurement in sufficient detail to prescribe practice (Pickering, 1995). Studies of modeling practices and model-based reasoning encompass a wide spectrum of approaches and disciplines. Some investigators rely on laboratory tasks that are intended to identify important aspects of model-based reasoning (e.g., Craig, Nersessian, & Catrambone, 2002; Gentner & Gentner, 1983). For example, Gentner and Gentner (1983) investigated how different analogical models of electricity influenced participants' reasoning about circuits. Participants who employed an analogy that emphasized the similarities of electricity flow to fluid flow made predictions about the consequences of different arrangements of batteries in a circuit that were more accurate than their predictions about the effects of resistors.

Cultivating Model-Based Reasoning in Science Education

This design study tracks the development of student thinking about natural variation as late elementary grade students learned about distribution in the context of modeling plant growth at the population level. The data-modeling approach assisted children in coordinating their understanding of particular cases with an evolving notion of data as an aggregate of cases. Students learned to “read” shapes of distributions as signatures of prospective mechanisms of plant growth and conducted sampling investigations to represent repeated growth. These investigations, in turn, supported students’ interpretations of the effects of added light and fertilizer. The authors argue for both the feasibility and the importance of tools such as distribution and inference for supporting education that builds on children’s own investigations of the world.

Modeling Natural Variation Through Distribution

Although the core work of science is oriented toward constructing, revising, applying, and defending models of the natural world, models appear only rarely in school science, and usually only as illustrations, rather than theory building tools We describe the rationale and structure for a learning progression to understand the development of modeling under supportive forms of instruction In this case, elementary and middle school students are modeling “big ideas” in the life sciences that hold the promise of serving as a conceptual foundation for reasoning about the theory of evolution later in their education In this conceptual paper, we sketch changes from grades K through 6 in representational and modeling practices across three interlocking constructs that, considered collectively, comprise the aforementioned conceptual foundation: Change (in individuals and populations), Variation, and Ecosystems The paper closes by delineating pedagogical principles for supporting the development of modeling across grades of instruction © 2012 Wiley Periodicals, Inc Sci Ed 96:701–724, 2012

Seeding Evolutionary Thinking by Engaging Children in Modeling Its Foundations.

(2003). Structuring Error and Experimental Variation as Distribution in the Fourth Grade. Mathematical Thinking and Learning: Vol. 5, No. 2-3, pp. 131-156.

Richard Lehrer

Papers

Design Experiments in Educational Research

Cultivating Model-Based Reasoning in Science Education

Modeling Natural Variation Through Distribution

Seeding Evolutionary Thinking by Engaging Children in Modeling Its Foundations.

Structuring Error and Experimental Variation as Distribution in the Fourth Grade