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

Prior to the twentieth century, theories of knowledge were inherently perceptual. Since then, developments in logic, statis- tics, and programming languages have inspired amodal theories that rest on principles fundamentally different from those underlying perception. In addition, perceptual approaches have become widely viewed as untenable because they are assumed to implement record- ing systems, not conceptual systems. A perceptual theory of knowledge is developed here in the context of current cognitive science and neuroscience. During perceptual experience, association areas in the brain capture bottom-up patterns of activation in sensory-motor areas. Later, in a top-down manner, association areas partially reactivate sensory-motor areas to implement perceptual symbols. The stor- age and reactivation of perceptual symbols operates at the level of perceptual components - not at the level of holistic perceptual expe- riences. Through the use of selective attention, schematic representations of perceptual components are extracted from experience and stored in memory (e.g., individual memories of green, purr, hot). As memories of the same component become organized around a com- mon frame, they implement a simulator that produces limitless simulations of the component (e.g., simulations of purr). Not only do such simulators develop for aspects of sensory experience, they also develop for aspects of proprioception (e.g., lift, run) and introspec- tion (e.g., compare, memory, happy, hungry). Once established, these simulators implement a basic conceptual system that represents types, supports categorization, and produces categorical inferences. These simulators further support productivity, propositions, and ab- stract concepts, thereby implementing a fully functional conceptual system. Productivity results from integrating simulators combinato- rially and recursively to produce complex simulations. Propositions result from binding simulators to perceived individuals to represent type-token relations. Abstract concepts are grounded in complex simulations of combined physical and introspective events. Thus, a per- ceptual theory of knowledge can implement a fully functional conceptual system while avoiding problems associated with amodal sym- bol systems. Implications for cognition, neuroscience, evolution, development, and artificial intelligence are explored.

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Perceptual symbol systems.

https://axon.cs.byu.edu/~dan/673/papers/gentner.pdf

Structure‐Mapping: A Theoretical Framework for Analogy*

The purpose of this article is to review basic empirical results from the psychological literature in a way that provides a useful foundation for research on consumer knowledge. A conceptual organization for this diverse literature is provided by two fundamental distinctions. First, consumer expertise is distinguished from product-related experience. Second, five distinct aspects, or dimensions, of expertise are identified: cognitive effort, cognitive structure, analysis, elaboration, and memory. Improvements in the first two dimensions are shown to have general beneficial effects on the latter three. Analysis, elaboration, and memory are shown to have more specific interrelationships. The empirical findings related to each dimension are reviewed and, on the basis of those findings, specific research hypotheses about the effects of expertise on consumer behavior are suggested.

Dimensions of Consumer Expertise

The education and research communities are abuzz with new (or at least re-discovered) ideas about the nature of cognition and learning. Terms like \"situated cognition,\" \"distributed cognition,\" and \"communities of practice\" fill the air. Recent dialogue in Educational Researcher (Anderson, Reder, & Simon, 1996,1997; Greeno, 1997) typifies this discussion. Some have argued that the shifts in world view that these discussions represent are even more fundamental than the now-historical shift from behaviorist to cognitive views of learning (Shuell, 1986). These new ideas about the nature of knowledge, thinking, and learning—which are becoming known as the \"situative perspective\" (Greeno, 1997; Greeno, Collins, & Resnick, 1996)—are interacting with, and sometimes fueling, current reform movements in education. Most discussions of these ideas and their implications for educational practice have been cast primarily in terms of students. Scholars and policymakers have considered, for example, how to help students develop deep understandings of subject matter, situate students' learning in meaningful contexts, and create learning communities in which teachers and students engage in rich discourse about important ideas (e.g., National Council of Teachers of Mathematics, 1989; National Education Goals Panel, 1991; National Research Council, 1993).

What Do New Views of Knowledge and Thinking Have to Say About Research on Teacher Learning

A central argument of this chapter is that there is a common basis for the 
failure of many instructional systems. The claim is that these deficiencies 
in the outcomes of learning are strongly influenced by underlying biases 
and assumptions in the design of instruction that represent the instructional domain and its associated performance demands in an unrealistically 
simplified and well-structured manner. We offer a constructivist theory of 
learning and instruction that emphasizes the real-world complexity and 
ill-structuredness of many knowledge domains. Any effective approach to 
instruction must simultaneously consider several highly intertwined topics, such as:• the constructive nature of understanding; 
• the complex and ill-structured features of many, if not most, knowl-edge domains;• patterns of learning failure; and 
• a theory of learning that addresses known patterns of learning failure.

Cognitive Flexibility, Constructivism, and Hypertext: Random Access Instruction for Advanced Knowledge Acquisition in Ill-Structured Domains

Schooling and the Acquisition of Knowledge

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Cognitive Flexibility Theory: Advanced Knowledge Acquisition in Ill-Structured Domains. Technical Report No. 441.

Traditional methods of instruction rely on linear media (eg, textbooks and
lectures) Linearity of media is not a problem when the subject matter being
taught is well structured and fairly simple However, as content increases in
complexity and ill-structuredness, increasingly greater amounts of important
information are lost with linear approaches and the unidimenionality of organization that typically accompanies them The advent of random access computer technologies makes practicable new forms of nonlinear and multidimensional learning and instruction that are better suited to conveying complex
content For example, it becomes a straightforward matter to revisit the same
content material in a variety of different contexts, with each visit bringing
out additional aspects of that content's complexity that are missed in the
single pass of linear coverage We use the expression random access instruction
to refer to a cluster of fundamental issues brought into play by nonlinear
learning with random access media This chapter discusses a unified theoretical approach to those fundamental issues, an approach that provides a foundation of principles to guide random access instruction

Rand J. Spiro

Papers

Cognitive Flexibility, Constructivism, and Hypertext: Random Access Instruction for Advanced Knowledge Acquisition in Ill-Structured Domains

Schooling and the Acquisition of Knowledge

Cognitive Flexibility Theory: Advanced Knowledge Acquisition in Ill-Structured Domains. Technical Report No. 441.

Schooling and the Acquisition of Knowledge

Cognitive Flexibility and Hypertext: Theory and Technology for the Nonlinear and Multidimensional Traversal of Complex Subject Matter