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.

This article explicates the interaction between implicit and explicit processes in skill learning, in contrast to the tendency of researchers to study each type in isolation. It highlights various effects of the interaction on learning (including synergy effects). The authors argue for an integrated model of skill learning that takes into account both implicit and explicit processes. Moreover, they argue for a bottom-up approach (first learning implicit knowledge and then explicit knowledge) in the integrated model. A variety of qualitative data can be accounted for by the approach. A computational model, CLARION, is then used to simulate a range of quantitative data. The results demonstrate the plausibility of the model, which provides a new perspective on skill learning. The role of implicit learning in skill acquisition and the distinction between implicit and explicit learning have been widely recognized in recent years (see, e.g., Cleeremans, Destrebecqz, &

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-

If computer-based instructional systems are to reap the benefits of natural language interaction, they must be endowed with the properties that make human natural language interaction so effective. To identify these properties, we replaced the natural language component of an existing Intelligent Tutoring System (ITS) with a human tutor, and gathered protocols of students interacting with the human tutor. We then compared the human tutor's responses to those that would have been produced by the ITS. In this paper, I describe two critical features that distinguish human tutorial explanations from those of their computational counterparts. Introduction There is growing interest in teaching real world problem-solving tasks using computer-based intelligent apprenticeship environments in which students learn by doing (Gott, 1989). Such skills typically involve complex chains of hidden reasoning and one goal of an apprenticeship environment is to help externalize the cognitive processes th...

This paper describes progress toward a prototype implementation of a tool which aims to improve literacy in deaf high school and college students who are native (or near native) signers of American Sign Language (ASL). We envision a system that will take a piece of text written by a deaf student, analyze that text for grammatical errors, and engage that student in a tutorial dialogue, enabling the student to generate appropriate corrections to the text. A strong focus of this work is to develop a system which adapts this process to the knowledge level and learning strengths of the user and which has the flexibility to engage in multi-modal, multi-lingual tutorial instruction utilizing both English and the native language of the user.

This paper explores the interaction between implicit and explicit processes during skill learning, in terms of top-down learning (that is, learning that goes from explicit to implicit knowledge) versus bottom-up learning (that is, learning that goes from implicit to explicit knowledge). Instead of studying each type of knowledge (implicit or explicit) in isolation, we stress the interaction between the two types, especially in terms of one type giving rise to the other, and its e#ects on learning. The work presents an integrated model of skill learning that takes into account both implicit and explicit processes and both top-down and bottom-up learning. We examine and simulate human data in the Tower of Hanoi task. The paper shows how the quantitative data in this task may be captured using either top-down or bottom-up approaches, although top-down learning is a more apt explanation of the human data currently available. These results illustrate the two different directions of learning (top-down versus bottom-up), and thereby provide a new perspective on skill learning.

In this paper we present our research on identifying and modeling the strategies that human tutors use for integrating previous explanations into current explanations. We have used this work to develop a computational model that has been partially implemented in an explanation facility for an existing tutoring system known as SHERLOCK. We are implementing a system that uses case-based reasoning to identify previous situations and explanations that could potentially affect the explanation being constructed. We have identified heuristics for constructing explanations that exploit this information in ways similar to what we have observed in instructional dialogues produced by human tutors.

To further explore the interaction between the implicit and explicit learning processes in skill acquisition (which have been tackled before, e.g., in Sun et al 2001, 2005), this paper explores details of the interaction of different learning modes: implicit learning, explicit hypothesis testing learning, and implicit-to-explicit knowledge extraction. Contrary to the common tendency in the literature to study each type of learning in isolation, this paper highlights the interaction among them and various effects of the interaction on learning, including the synergy effect. This work advocates an integrated model of skill learning that takes into account both implicit and explicit learning processes; moreover, it also uniquely embodies a bottom-up (implicit-to-explicit) learning approach in addition to other types of learning. The paper shows that this model accounts for various effects in the human behavioral data from the psychological experiments with the process control task, in addition to accounting for other data in other psychological experiments (which has been reported elsewhere). The paper shows that to account for these effects, implicit learning, bottom-up implicit-to-explicit extraction, and explicit hypothesis testing learning are all needed.

This proposal paper is being presented in partial fulfillment of the Ph.D. requirements of the Department of Computer and Information Sciences at the University of Delaware. In this paper, I discuss a user modeling architecture for ICICLE, a natural language system intended for use as a writing tutor for deaf learners of written English. This proposed design, intended to model dynamic aspects of a learner over the passage of time, the acquisition of new knowledge, and multiple sessions with the system, includes components to track the history of interaction with a given user as well as a very complex, dynamic model of user interlanguage grammar and domain knowledge. It has been based on research in language acquisition and in the acquisition of cognitive skills. The focus of the work described in this proposal is the development of the model of interlanguage status, which will be used in the analysis of user language production and in the generation of user-tailored explanations. Conte...

Hints are an important ingredient of natural language tutorial dialogues. Existing models of hints, however, are limited in capturing their various underlying functions, since hints are typically treated as a unit directly associated with some problem solving script or discourse situation. Putting emphasis on making cognitive functions of hints explicit, we present a multi-dimensional hint taxonomy where each dimension defines a decision point for the associated function. Hint categories are then conceived as convergent points of the dimensions. So far, we have elaborated five dimensions: (1) domain knowledge reference, (2) inferential role, (3) elicitation status, (4) discourse dynamics, and (5) problem solving perspective. These fine-grained distinctions support the constructive generation of hint specifications from modular knowledge sources.