Cognitive load theory has been designed to provide guidelines intended to assist in the presentation of information in a manner that encourages learner activities that optimize intellectual performance. The theory assumes a limited capacity working memory that includes partially independent subcomponents to deal with auditory/verbal material and visual/2- or 3-dimensional information as well as an effectively unlimited long-term memory, holding schemas that vary in their degree of automation. These structures and functions of human cognitive architecture have been used to design a variety of novel instructional procedures based on the assumption that working memory load should be reduced and schema construction encouraged. This paper reviews the theory and the instructional designs generated by it. KEY WORDS: cognition; instructional design; learning; problem solving.

Reaction-time and eye-fixation data are analyzed to investigate how people infer the kinematics of simple mechanical systems (pulley systems) from diagrams showing their static configuration. It is proposed that this mental animation process involves decomposing the representation of a pulley system into smaller units corresponding to the machine components and animating these components in a sequence corresponding to the causal sequence of events in the machine's operation. Although it is possible for people to make inferences against the chain of causality in the machine, these inferences are more difficult, and people have a preference for inferences in the direction of causality. The mental animation process reflects both capacity limitations and limitations of mechanical knowledge. Understanding the operation of deterministic systems, such as mechanical or electronic devices, includes the ability to infer the state of one component of the system given information about the states of the other system components and the relations between the components. This type of understanding is central to how people design, troubleshoot, and operate devices. This article describes how people infer the motion of components of a simple mechanical system (a pulley system) from knowledge of the configuration of the system and the movement of one of the system components. It provides an account of the process of inferring motion, the type of knowledge that allows people to infer motion, and the characteristics of human information processing that constrain the inference process. I refer to this process as mental animation.

We identify and analyze basic principles which necessarily underlie software measures research. In the prevailing paradigm for the validation of software measures there is a fundamental assumption that the sets of measured documents are ordered, and that measures should report these orders. We describe mathematically the nature of such orders. Consideration of these orders suggests a hierarchy of software document measures, a methodology for developing new measures, and a general approach to the analytical evaluation of measures. We also point out the importance of units for any type of measurement and stress the perils of equating document structure complexity and psychological complexity. Keywords: software measures, abstractions of software documents, software structure, analytical evaluations of measures 1 Introduction This paper presents some underlying principles for software measures research. By "software measures" we mean measures which are obtainable directly from software d...

Modern computer systems routinely present information to the user as a combination of text and diagrammatic images, described as "graphical user interfaces". Practitioners and researchers in Human-Computer Interaction (HCI) generally believe that the value of these diagrammatic representations is derived from metaphorical reasoning; they communicate abstract information by depicting a physical situation from which the abstractions can be inferred. This assumption has been prevalent in HCI research for over 20 years, but has seldom been tested experimentally. This thesis analyses the reasons why diagrams are believed to assist with abstract reasoning. It then presents the results of a series of experiments testing the contribution of metaphor to comprehension, problem solving, explanation and memory tasks carried out using a range of different diagrams. The results indicate that explicit metaphors provide surprisingly little benefit for cognitive tasks using diagrams as an external re...

This paper describes the underlying principles of a computer model, CHREST+, which learns to solve problems using diagrammatic representations. Although earlier work has determined that experts store domain-specific information within schemata, no substantive model has been proposed for learning such representations. We describe the different strategies used by subjects in constructing a diagrammatic representation of an electric circuit known as an AVOW diagram, and explain how these strategies fit a theory for the learnt representations. Then we describe CHREST+, an extended version of an established model of human perceptual memory. The extension enables the model to relate information learnt about circuits with that about their associated AVOW diagrams, and use this information as a schema to improve its efficiency at problem solving.

After their beginnings in computer-aided instruction, automated tutors have re-emerged as intelligent tutoring systems. These intelligent tutors have obtained considerable success by using results from cognitive psychology and artificial intelligence to permit non-traditional instruction which is tailored to their individual students. The success of these automated tutors is due to their precise understanding and modeling of both the student and the domain being taught. A common measure of the robustness of an automated tutor is the size of the domain that it can understand. The schema-based Prolog tutor described in this dissertation is capable of recognizing a larger class of programs than existing Prolog tutors. By using powerful generalized transformations, our Prolog tutor can generate this class of programs from a very small set of normal form programs. Thus, our Prolog tutor recognizes a larger class of programs using fewer normal form programs than existing Prolog tutors. One o...

Research on expertise in visual-spatial tasks such as chess has ignored the introspective reports of top-level practitioners, overemphasized pattern recognition as the sole mechanism underlying skilled performance, and neglected the role of mental imagery in the thinking process. In addressing these limitations I propose a new theory of expertise, the “mental cartoons hypothesis,” and illustrate its properties by application to chess, which has historically been the primary domain for psychological studies of human expertise. Experiment 1 uses a classic image-scanning paradigm to show that chess masters and novices differ substantially in their ability to visualize chess moves, even in semantically impoverished contexts, extending the range of novice-expert differences from pattern recognition and knowledge representation to mental imagery processing. Experiments 2 and 3 exploit original very-long-term memory recognition and recall tasks to show that the memory representations of famous chess positions held by chess masters

The study of expertise has a very long history that has been discussed in several other chapters in this handbook (Ericsson, Chapter 1.1; Amirault & Branson, Chapter 2.2). The focus of this chapter is on influential developments within cognitive science and cognitive psychology for over the last three decades. Our chapter consists of two parts. In

a series of technology research roadmaps, or plans, developed over a three year period by the Federation of American Scientists and the Learning Federation, a partnership among industry, academia, and private foundations to stimulate research and development in learning science and technology. The full series of research roadmaps is available at www.FAS.org/learningfederation. We thank Dr. Jan Cannon-Bowers for her major contribution in writing this roadmap. And, we thank Dr. Marianne Bakia, who left FAS just prior to completion of the roadmap, for her contributions to the Learning Federation Project and development of this roadmap. We gratefully acknowledge the funding support of the 2003 Congressional appropriation to the Federation of American Scientists for the Digital Opportunity Investment Trust (DO IT). A major part of that funding supported the Learning Federation's Learning Sciences and Technology Research and Development Roadmap, which appears in the DO IT Report to Congress. We also gratefully acknowledge the additional funding support of the organizations that sponsored this work and helped make possible the Roadmap:

Abstract: This paper presents a research project suggesting the use of real-time 3D techniques normally used in simulation environments as a navigation aid onboard ships. Based on a three-dimensional geographical database the surrounding world is presented in a “bridgeeye perspective ” with navigational information such as own track, other ships in the vicinity and their tracks, water depth and radar echoes integrated in a single display. The integrated display suggested, acts as a complement to traditional electronic charts. The main objective is to lessen the cognitive load of the bridge personal and particularly the helms man in hand steering situations in high-speeds. (See fig. 1.) 1.