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-

It is now possible to build machine learning systems whose behavior is consistent with data from human students. How can education use such simulated students? Applications that help three user groups are discussed. Teachers can practice the art of tutoring byhaving them teach a simulated student. Using a simulation instead of a real student allows teachers to see how their actions affect that student's knowledge, to undo their actions, and to try their skills on students with varying prior knowledge and learning strategies. Students can learn in collaboration with a simulated student. Because the simulated student can be simultaneously an expert and a colearner, it can scaffold and guide the human's learning in subtle ways. Instructional developers can test their instruction on simulated students. Unlike formativeevaluations with real students, a simulation-based evaluation can indicate exactly what piece of the instruction caused which pieces of knowledge, and thus help developers troubleshoot their instructional designs early in the design process. For each of these three areas of application, inherent technical limitations, existing systems and prospective systems are discussed.

this paper, we describe a framework for goal-driven learning and its relationship to prior and current theories from each of these perspectives.

This paper concerns a knowledge structure called method , within a computational model for human oriented deduction. With human oriented theorem proving cast as an interleaving process of planning and verification, the body of all methods reflects the reasoning repertoire of a reasoning system. While we adopt the general structure of methods introduced by Alan Bundy, we make an essential advancement in that we strictly separate the declarative knowledge from the procedural knowledge. This is achieved by postulating some standard types of knowledge we have identified, such as inference rules, assertions, and proof schemata, together with corresponding knowledge interpreters. Our approach in effect changes the way deductive knowledge is encoded: A new compound declarative knowledge structure, the proof schema, takes the place of complicated procedures for modeling specific proof strategies. This change of paradigm not only leads to representations easier to understand, it also enables us...

In this article we formally describe a declarative approach for encoding plan operators in proof planning, the so-called methods. The notion of method evolves from the much studied concept tactic and was first used by Bundy. While significant deductive power has been achieved with the planning approach towards automated deduction, the procedural character of the tactic part of methods, however, hinders mechanical modification. Although the strength of a proof planning system largely depends on powerful general procedures which solve a large class of problems, mechanical or even automated modification of methods is nevertheless necessary for at least two reasons. Firstly methods designed for a specific type of problem will never be general enough. For instance, it is very difficult to encode a general method which solves all problems a human mathematician might intuitively consider as a case of homomorphy. Secondly the cognitive ability of adapting existing methods to suit novel situa...

Conventional behaviors develop from practice for regularly occurring problems of coordination within a community of actors. Re-using and extending conventional methods for coordinating behavior is the task of everyday reasoning. The

Experiments 1-2 examined generic knowledge and episodic memories of putting in novice and expert golfers. Impoverished episodic recollection of specific putts among experts indicated that skilled putting is encoded in a procedural form that supports performance without the need for step-by-step attentional control. According to explicit monitoring theories of choking, such proceduralization makes putting vulnerable to decrements under pressure. Experiments 3-4 examined choking and the ability of training conditions to ameliorate it in putting and a nonproceduralized alphabet arithmetic skill analogous to mental arithmetic. Choking occurred in putting but not alphabet arithmetic. In putting, choking was unchanged by dual-task training but eliminated by self-consciousness training. These findings support explicit monitoring theories of choking and the popular but infrequently tested belief that attending to proceduralized skills hurts performance. Why does the execution of a well-learned skill fail under pressure? Research investigating skill and expertise has produced a number of important findings regarding the variables that mediate optimal skill performance (Allard & Starkes, 1991; Anderson,

is difficult. In this article we develop a cognitive framework to characterize sense-making in the implementation process that is especially relevant for recent education policy initiatives, such as standards-based reforms that press for tremendous changes in classroom instruction. From a cognitive perspective, a key dimension of the implementation process is whether, and in what ways, implementing agents come to understand their practice, potentially changing their beliefs and attitudes in the process. We draw on theoretical and empirical literature to develop a cognitive perspective on implementation. We review the contribution of cognitive science frames to implementation research and identify areas where cognitive science can make additional contributions.

In this paper we generalize the notion of method for proof planning. While we adopt the general structure of methods introduced by Alan Bundy, we make an essential advancement in that we strictly separate the declarative knowledge from the procedural knowledge. This change of paradigm not only leads to representations easier to understand, it also enables modeling the important activity of formulating meta-methods, that is, operators that adapt the declarative part of existing methods to suit novel situations. Thus this change of representation leads to a considerably strengthened planning mechanism. After presenting our declarative approach towards methods we describe the basic proof planning process with these. Then we define the notion of meta-method, provide an overview of practical examples and illustrate how meta-methods can be integrated into the planning process.