S. Dijkstra

Instructional Models in Computer-Based Learning Environments

For over thirty years, there has been a vast research interest in computer-based learning environments. However, the last decade is characterized by rapid developments in the field. On the surface level, these changes largely pertain to the constitution of the research community, in which more and more cognitive scientists and Artificial Intelligence (AI) specialists are operating, and to the type of learning environments which are developed. The term Computer Assisted Instruction (CAI) quickly became old-fashioned and is replaced by the term ICAI, where the “I” denotes the assumed intelligence built in the learning environment; for example, its knowledge of the domain to be taught, its knowledge of the learner’s cognitive processes, its knowledge of instruction and communication, and — last but not least — its inference capabilities to use one or more of these knowledge bases in order to control the learning process. A whole new family of computer-based learning environments appeared under the label ICAI, including microworlds, Intelligent Tutoring Systems (ITS), (intelligent) hypermedia, adaptive help systems, and so further.

The Effects of the Design and Development of a Chemistry Curriculum Reform on Teachers’ Professional Growth: A Case Study

A curriculum innovation requires new learning material for students and a preparation program for teachers, in which teacher learning is a key ingredient. In this paper we describe how three experienced teachers, involved in the development and subsequent classroom enactment of student learning material for context-based chemistry education, professionalized. For data collection a questionnaire, three interviews and discussion transcripts were used. Our results show that: (a) teachers, cooperating in a network under supervision of an expert, can develop innovative learning material; (b) the development of learning material can be seen as a powerful program to prepare teachers for an innovation; and (c) teachers’ knowledge increased in all five pedagogical content knowledge domains during the development and class enactment phases.

The integration of instructional systems design models and constructivistic design principles

This article first addresses the development of information and problem-solving procedures as “objectified” knowledge and the continuously increasing amount of it. For education decisions must be made which part of it should be passed on to future generations as obligatory for all members of a community or only for those persons who will prepare themselves for a special position. Than a description of instructional design is provided and the recent criticisms are discussed. This leads to an outline of an integrative framework for the description of information and problem-solving procedures and to a problem-solving approach for the acquisition of knowledge and skills. Three categories of problems are distinguished, categorization or description problems, interpretation problems and design problems. The solution of the many problems result in different cognitive constructs. Once these are published and selected for education they form the content of the subjects. For the development or construction of knowledge and the practicing of skills the learners should be involved in problem-solving activities, such as exploration, imagination, discovery, application and design. The required results of learning may differ. Three levels of performance, illustrated within the categories of problems are distinguished. It is shown that the content of the subjects and levels of performance can guide the selection of instructional strategies and make it possible to classify the learning situations. Finally some implications for curriculum design and for the selection of problems to be solved by the students are discussed.

Instructional Design for Tele-Learning

IN ANY COURSE IN INSTRUCTIONAL DESIGN, the design principles should not only be covered in the course content, but also demonstrated by the structure of the course itself. Telematics applications of various sorts can bring new dimensions into the instructional design of the course to better illustrate the subject matter. In this article we describe the design of a WWW-based course-support environment for a course in instructional design, given an overview of how the environment was used as part of the course experience, and summarize the student evaluation of the course. We call such an augmented learning process a “tele-learning” situation because telematics applications are involved. We conclude that such a course-support environment can extend the teaching and learning process, if well designed, by bringing added opportunities for communication and coaching, and by increasing student self-responsibility. We do not see such a tool as replacing the instructor, but enhancing instruction. The design of the environment should reflect this. Although the article describes a particular course on instructional design, we argue that the conclusions can be valid for a variety of disciplines and instructional approaches.

The design space for solving instructional-design problems

The development of design science is outlinedin this article followed by a description ofthe concept of design space, which isillustrated by maritime design. The example isused to illuminate and situate the currentstate of instructional-design science. Thearticle summarizes the developments of aninstructional design science, both in Europeand in the United States of America. Followingthis discussion, instructional design isrelated to the general goals of education andthe concept of situatedness is discussed.Attention is paid to the description ofinstructional communication and how to solveinstructional design problems. Finally, anoverview is given of the issues addressed inthe other articles comprising this specialissue.

Plan-based Sequencing of Problems for Introductory Programming

Rules for sequencing problems via an Intelligent Tutoring System for introductory computer programming are specified within four criteria of instruction: providing problem solving opportunity; providing solvable problems; opportunity for knowledge construction; advancing applicability of concepts learned. Aims of an introductory course are specified in terms of goals and plans: to decompose a problem into a structure of programming goals, to produce a solution consisting of a structure of programming plans, and to translate the structure of programming plans into executable code. Measures of the four criteria are presented based on goal/plan analyses of the problems in the ITS’s knowledge bases.

The Development of the Representation of Conceptual Knowledge in Memory and the Design of Instruction.

Knowledge comprises facts, concepts and principles. Skills are categorized as either cognitive or motor skills, which are essential for solving problems. The acquisition of knowledge and skills is guided by instructions and by presenting problems to students. Firstly, the instructions for acquiring concepts, based on principles, are discussed and a model for teaching is presented. Further, the integration of class and relational concepts, together with principles, is shown to be necessary for solving problems.

Scalability in instructional method specification: An experiment-directed approach

An intelligent tutoring system (ITS) is, in principle, well suited for instructional experimentation as an automated environment which allows for controlled variation of variables. These variables, aspects of the instructional and domain models, can be varied by replacing parts representing these aspects by other parts. However, if ITSs are to be used as vehicles for instructional experimentation the architecture, the knowledge representation, and the authoring environment should fulfil additional requirements. This chapter discusses the requirements for experimentation-directed ITSs, the shell for ITS development and the scalable instructional method specification (SIMS) paradigm.

The study of problem characteristics in programming tutors

The study of instructional design rules for problems in programming courses may be supported by the application of intelligent tutoring shells. Problems in a programming course appear at three levels, namely as personal problems or quandaries, as instructional problems or assignments, and as underlying problems or questions. This is illustrated for four problem characteristics, namely the programming concepts, the context, the structure and the difficulty. Requirements for an intelligent tutoring shell enabling the study of these characteristics are discussed.