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.

CATO is an intelligent learning environment designed to help beginning law students learn basic skills of making arguments with cases. Using CATO, students practice tasks of induction and analogical argumentation. They practice testing theories against a body of cases and making written arguments about a problem, comparing and contrasting it to past cases. CATO’s model addresses arguments in which two opponents analogize a problem to favorable cases, distinguish unfavorable cases, assess the significance of similarities and differences between cases in light of normative knowledge about the domain, and use that knowledge to organize multi-case arguments. CATO communicates the model to students by presenting dynamically-generated argumentation examples and by reifying argument structure based on the model. CATO also provides a case database and tools based on the model that help make students ’ tasks more manageable. CATO was evaluated in the context of an actual legal writing course, in a study involving 30 first-year law students. We found that instruction with CATO leads to statistically significant improvement in students ’ basic argumentation skills, comparable

Cognitive skills acquisition is acquiring the ability to solve problems in intellectual tasks, where success is determined more by the subjects' knowledge than their physical prowess. This chapter reviews reseach conducted in the last ten years on cognitive skill acquisition. It covers the initial stages of acquiring a single principle or rule, the initial stages of acquiring a collection of interacting pieces of knowledge, and the final stages of acquiring a skill, wherein practice causes increases speed and accuracy.

Worked examples are instructional devices that provide an expert's problem solution for a learner to study. Worked-examples research is a cognitive-experimental program that has relevance to classroom in-struction and the broader educational research community. A frame-work for organizing the findings of this research is proposed, leading to instructional design principles. For instance, one instructional de-sign principle suggests that effective examples have highly integrated components. They employ multiple modalities in presentation and em-phasize conceptual structure by labeling or segmenting. At the lesson level, effective instruction employs multiple examples for each concep-tual problem type, varies example formats within problem type, and employs surface features to signal deep structure. Also, examples should be presented in close proximity to matched practice problems. More-over, learners can be encouraged through direct training or by the structure of the worked example to actively self:explain examples. Worked examples are associated with early stages of skill develop-ment, but the design principles are relevant to constructivist research and teaching. The Historical Context In recent years, learning from "worked examples " has received a consider-able amount of attention from researchers (e.g., Chi, Bassok, Lewis, Reimann, & Glaser, 1989; Ward & Sweller, 1990), particularly in such fields as mathematics, physics, and computer programming. Although there is no precise definition, worked examples share certain family resemblance (Wittgenstein, 1953). As instructional devices, they typically include a problem statement and a proce-dure for solving the problem; together, these are meant to show how other similar problems might be solved. In a sense, they provide an expert's problem-

Intelligent tutoring systems are highly interactive learning environments that have been shown to improve upon typical classroom instruction. Cognitive Tutors are a type of intelligent tutor based on cognitive psychology theory of problem solving and learning. Cognitive Tutors provide a rich problem-solving environment with tutorial guidance in the form of step-by-step feedback, specific messages in response to common errors, and on-demand instructional hints. They also select problems based on individual student performance. The learning benefits of these forms of interactivity are supported, to varying extents, by a growing number of results from experimental studies. As Cognitive Tutors have matured and are being applied in new subject-matter areas, they have been used as a research platform and, particularly, to explore interactive methods to support metacognition. We review experiments with Cognitive Tutors that have compared different forms of interactivity and we reinterpret their results as partial answers to the general question: How should learning environments balance information or assistance giving and withholding to achieve optimal student learning? How best to achieve this balance remains a fundamental open problem in instructional science. We call this problem the “assistance dilemma ” and emphasize the need for further science to yield specific conditions and parameters that indicate when and to what extent to use information giving versus information withholding forms of interaction.

Traditionally, intelligent tutoring systems have provided feedback on the basis of a so-called expert model. Expert model tutors incorporate production rules associated with error free and efficient task performance. These systems intervene with corrective feedback as soon as a student deviates from a solution path. This thesis explores the effects of providing feedback on the basis of a so-called intelligent novice cognitive model. An intelligent novice tutor allows students to make errors, and provides guidance through the exercise of error detection and correction skills. The underlying cognitive model in such a tutor includes both rules associated with solution generation, and rules relating to error detection and correction. There are two pedagogical motivations for feedback based on an intelligent novice model. First, novice performance is often error prone and students may need error detection and correction skills in order to succeed in real world tasks. Second, the opportunity to reason about the causes and consequences of errors may allow students to form a better model of the behavior of domain operators. Learning outcomes associated with the two models were experimentally evaluated. Results show that learners who receive intelligent novice feedback demonstrate better learning overall, including better retention and transfer performance than students receiving expert model based feedback.

We present a computational framework for improving learning from examples by supporting self-explanation - the process of clarifying and making more complete to oneself the solution of an example. Many studies indicate that selfexplanation can improve problem solving performance, and that guiding selfexplanation can extend these benefits. Our goal is developing and testing a computer tutor --- the SE (Self-Explanation) Coach --- that can elicit and guide correct and effective self explanation, and thus improve problem solving performance in university-level Newtonian physics, a particularly complex and psychologically challenging domain. The self-explanations elicited by the SE Coach address how each component in the example solution can be justified in terms of (a) the theory of the instructional domain, and (b) the goal accomplished in the plan underlying the example solution. The SE Coach provides the student with a Workbench that interactively presents examples and provides tools t...

This article presents five principles of learning, derived from cognitive theory and supported by empirical results in cognitive psychology. To bridge the gap between theory and practice, each of these principles is transformed into a practical guideline and exemplified in a real teaching context. It is argued that this approach of putting cognitive theory into practice can offer several benefits to statistics education: a means for explaining and understanding why reform efforts work; a set of guidelines that can help instructors make well-informed design decisions when implementing these reforms; and a framework for generating new and effective instructional innovations

Several investigations have found that students learn more when they explain examples to themselves while studying them. Moreover, they refer less often to the examples while solving problems, and they read less of the example each time they refer to it. These ndings, collectively called the self-explanation effect, have been reproduced by our cognitive simulation program, Cascade. Moreover, when Cascade is forced to explain exactly the parts of the examples that a subject explains, then it predicts most (60 to 90%) of the behavior that the subject exhibits during subsequent problem solving. Cascade has two kinds of learning. It learns new rules of physics (the task domain used in the human data modeled) by resolving impasses with reasoning based on overly-general, nondomain knowledge. It acquires procedural competence by storing its derivations of problem solutions and using them as analogs to guide its search for solutions to novel problems.

Multimedia repositories, libraries, and databases offer the potential for providing students with access to a wide variety of interconnected information resources. However, in order to realize this potential, multimedia systems should provide access to information and activities that support effective knowledge construction and learning by students. This article proposes a theoretical framework for organizing information and activities in educational hypermedia systems. We show that such systems should not be characterized primarily in terms of the kinds of physical media types that can be accessed; instead, the important aspect is the content that can be represented within a physical media, rather than the physical media itself. We propose a theory of "cognitive media types" based on the inferential and learning processes of human users. The theory highlights specific media characteristics that facilitate specific problem solving actions, which in turn are enabled by specific kinds of...