Technology

Technology, Instruction, Cognition and Learning (TICL)

Technology, Instruction, Cognition and Learning (TICL) is an international, interdisciplinary journal in technology, instruction, cognition and learning. Important but heretofore largely parallel developments in recent years have been made in cognitive, learning and instructional theory and research, on the one hand, and in software technologies, on the other. In addition to the well publicized and revolutionary developments on the Internet, for example, major advances have been made in software engineering and distributed systems development that make it possible to implement systems that would have been infeasible just a few years ago. Conversely, deeper understanding cognitive and instructional systems open major new opportunities in technology. These developments open a plethora of new opportunities for scientific and technological advance in learning and instruction that could barely have been foreseen even a few years ago. These opportunities will only be realized to the extent that advances in these often disparate domains can be synthesized to provide more inclusive solutions.

The JOURNAL will promote and disseminate advances in theory and research at the intersection of these disciplines: technology, instruction, cognition and learning. The emphasis will be on the accumulation of knowledge that impacts on automation and technology and vice versa on advances in technology that serve to deepen our understanding of instruction, cognition and learning. Scientific and technological progress will be emphasized although philosophical discourse that directly facilitates such progress will also be considered. To assure that articles build on the state of the art in each area, each will be peer-reviewed by experts in at least two complimentary areas. The JOURNAL seeks articles that will help answer such basic questions as:

What does it mean to know something? How can one represent knowledge in meaningful ways that have observable determinants or markers as well as relevance to individuals and groups?
How can one find out what a person knows? How can one assess or determine what a person knows based on behavior, performance and other observable activities?
How does knowledge effect learning? What are the respective roles and interactions of existing knowledge and cognitive capacities in the acquisition of new knowledge and skills?
How does knowledge change over time as a result of interactions between individuals and the environment? What are the dynamics of knowledge growth?

Can answers to the above questions be extended or enhanced with technology, and if so, how? In short, each of these questions has technological counterparts:

Which aspects of knowledge representation and task analysis can be automated? How does technology effect knowledge construction and representation? How can knowledge be represented in ways that can be automated on a computer?
Which aspects and to what degree can testing and diagnosis be automated or supported with technology and how?
How can human learning be enhanced via technology and/or learning systems implemented in software?
Which aspects of instructional design can be automated? To what extent can interactions between different individuals, such as but not limited to teachers and learners, be automated?
Conversely, for each of the above, what things cannot be automated, and how best might automation be supplemented by humans.

Technology, Instruction, Cognition and Learning are both focused and broadly interpreted. Cognition, for example, may include humans and/or intelligent computer agents, operating either individually or collectively. Moreover, the role of internal representations in understanding human behavior is considered vital. On the other hand, cognition and behavior are closely related, whether this involves individual and/or situated activities. Instruction encompasses learning and performance environments, and teacher-directed instruction. Special attention will be given to alternative modes of representation, especially as regards their advantages and limitations with respect to automation. Technology includes new developments with potential impact on instruction, cognition and/or learning: ranging from basic theory, environments and tools to methodologies and principled techniques. Attention will be given both to basic developments in theory and technology with potential impact on practice and to new developments in practice raising fundamental issues requiring further investigation.

Answers to the above questions will have broad application in many areas as well as a profound effect on a wide range of disciplines. Potential application areas include but are hardly limited to:

intelligent tutoring systems,
intelligent performance support systems,
automated learning systems and instructional technologies,
software engineering,
simulation and automated performance aids,
powerful tools to enhance and extend the abilities of designers, learners and teachers,
automated instructional design systems,
knowledge construction environments,
dynamic generation of problem representations in context and knowledge specific settings.

To the extent that they shed light on computer-based teaching and learning, research on interactions between other kinds of intelligent agents will also be considered (e.g., automated negotiation servers in automated supply chains in business-to-business communications over the web). Equally important are recent advances in software technology, including component-based software, distributed systems, modeling and simulation environments, and web-based technologies. These advances open new opportunities in instructional technology that could barely be envisioned just a few years ago. Additionally, there are still many open-ended areas of investigation ranging from the more practical (e.g., promoting reuse of CBI content, dynamic generation of just-in-time instruction) to the more theoretical (e.g., dynamic generation of problem representations for arbitrary problem domains). The JOURNAL encourages applications of these ideas to learning, performance and instruction by publishing theory, empirical research, integrative reviews and innovative demonstrations in these areas.

From a disciplinary perspective, the scope of the JOURNAL includes research in artificial intelligence, cognitive and development psychology, software engineering, cognitive science, distributed cognition, educational research, constructivism, instructional design, intelligent tutors, structural learning, problem solving and system dynamics. Instructional applications of computational linguistics, discourse theory, logic programming, and knowledge modeling are also relevant. The JOURNAL will also publish articles describing new and/or advanced instructional technologies that raise new questions and/or build on, demonstrate or evaluate existing theories.

Methodological and philosophical issues and relevant theory and research are also considered to the extent that they further the above aims (e.g., new concept mapping methods and epistemological frameworks, new methods for structural and cognitive task analysis, advances in learning theory, learning of structured content, applications of a theory to practical problems, adaptive methods for assessing acquisition of expertise or progress of learning, etc.). Particular emphasis is given to multi-disciplinary efforts that cut across core questions or provide insight into fundamental questions (e.g., how internal representations of knowledge change with experience). Particular importance is given to scientific and technical contributions that both add to current understanding and have practical significance for cognition, learning and instructional technology. The goal is to promote work that is cumulative in nature, progressively increasing depth of understanding. Model applications of basic work are also encouraged, most especially in cognitive learning and instructional technology. Although the role of cognition in instructional technology will be emphasized, TICL is equally open to insightful applications in other relevant areas (e.g., automated supply chains consisting of distributed systems of interacting software components that can be used to support learning and performance improvement).

The TICL primary mission is to improve interdisciplinary communication and to promote scientific dialogue on both fundamental issues and their real world application. Systematic approaches to both simple and complex (structural) learning are encouraged (i.e., learning treated as a dynamic system in which structural changes can be studied and interpreted with regard to impacts and outcomes). TICL encourages debate on controversial issues (e.g., the degree to which various solutions can be automated) and makes every effort to remain on the cutting edge in technology, instruction, cognition and learning. Integrative reviews, especially those which cut across disciplinary lines, will be especially welcome.