This paper presents a skill learning model CLARION. Different from existing models of mostly high-level skill learning that use a top-down approach (that is, turning declarative knowledge into procedural knowledge through practice), we adopt a bottom-up approach toward low-level skill learning, where procedural knowledge develops first and declarative knowledge develops later. Our model is formed by integrating connectionist, reinforcement, and symbolic learning methods to perform on-line reactive learning. It adopts a two-level dual-representation framework (Sun, 1995), with a combination of localist and distributed representation. We compare the model with human data in a minefield navigation task, demonstrating some match between the model and human data in several respects.

In this paper we review the literature relating to the psychological/educational study of programming. We identify general trends comparing novice and expert programmers, programming knowledge and strategies, program generation and comprehension, and objectoriented versus procedural programming. (We do not cover research relating specifically to other programming styles.) The main focus of the review is on novice programming and topics relating to novice teaching and learning. Various problems experienced by novices are identified, including issues relating to basic program design, to algorithmic complexity in certain language features, to the ‘‘fragility’ ’ of novice knowledge, and so on. We summarise this material and suggest some practical implications for teachers. We suggest that a key issue that emerges is the distinction between effective and ineffective novices. What characterises effective novices? Is it possible to identify the specific deficits of ineffective novices and help them to become effective learners of programming? 1.

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, &

The success of large software engineering projects depends critically on the specification, which must represent the requirements of a large number of people with widely differing perspectives. Conventional approaches to software engineering do not address the process of identifying and integrating these perspectives, but instead concentrate on the maintenance of a single consistent description. This results in a specification which represents only one point of view, often the analyst's, excluding suggestions which do not fit with this view. The processes which led to the adoption of this point of view will go unrecorded, making any rationale attached to such a specification incomplete. Other participants will not be able to validate it properly, as it does not relate to their requirements. This thesis integrates ideas drawn from the study of knowledge acquisition, computer-supported co-operative work and negotiation into a model of the specification activity which allows the capture ...

This paper presents a learning theory pertinent to dynamic decision making (DDM) called instancebased learning theory (IBLT). IBLT proposes five learning mechanisms in the context of a decision-making process: instance-based knowledge, recognition-based retrieval, adaptive strategies, necessity-based choice, and feedback updates. IBLT suggests in DDM people learn with the accumulation and refinement of instances, containing the decision-making situation, action, and utility of decisions. As decision makers interact with a dynamic task, they recognize a situation according to its similarity to past instances, adapt their judgment strategies from heuristic-based to instance-based, and refine the accumulated knowledge according to feedback on the result of their actions. The IBLT’s learning mechanisms have been implemented in an ACT-R cognitive model. Through a series of experiments, this paper shows how the IBLT’s learning mechanisms closely approximate the relative trend magnitude and performance of human data. Although the cognitive model is bounded within the context of a dynamic task, the IBLT is a general theory of decision making applicable to other dynamic environments.

Emerging parallel processing and increased flexibility during the acquisition of cognitive skills form a combination that is hard to reconcile with rule-based models that often produce brittle behavior. Rule-based models can exhibit these properties by adhering to 2 principles: that the model gradually learns task-specific rules from instructions and experience, and that bottom-up processing is used whenever possible. In a model of learning perfect time-sharing in dual tasks (Schumacher et al., 2001), speedup learning and bottom-up activation of instructions can explain parallel behavior. In a model of a

Contents Danish Summary (Dansk Resumé) 1 Acknowledgements 4 1. Introduction 6 1.1 Background........................................................... ..................................... 7 1.2 Cooperative analysis ..................................................................... .......... 11 1.3 Notes on vocabulary ..................................................................... ........... 11 1.4 Progression of this thesis ..................................................................... ... 14 2. Empirical background 18 2.1 The AT-project........................................................... .............................. 20 2.2 The EuroCoOp and EuroCODE projects................................................ 31 3. Six approaches to analysis 46 3.1 Yourdon: Managing the System Life Cycle............................................ 47 3.2 Jackson: System Development ......................................................

This paper is concerned with modeling the nature of expertise and its role in society in relation to research on expert systems and enterprise models. It argues for the adoption of a collective stance in which the human species is viewed as a single organism recursively partitioned in space and time into sub-organisms that are similar to the whole. These parts include societies, organizations, groups, individuals, roles, and neurological functions. Notions of expertise arise because the organism adapts as a whole through adaptation of its interacting parts. The phenomena of expertise correspond to those leading to distribution of tasks and functional differentiation of the parts. The mechanism is one of positive feedback from parts of the organism allocating resources for action to other parts on the basis of those latter parts past performance of similar activities. Distribution and differentiation follow if performance is rewarded, and low performers of tasks, being excluded by the f...

The Internet (the net) and World Wide Web (the web) have grown rapidly in the past decade and have come to play a major role in supporting discourse and publication in scholarly communities. The development and application of new services has been very rapid with little central planning, and, despite the widespread use, there is little information as yet on the human factors of the use of the net and web. In particular, models of the human factors of individuals interacting with workstations have to be extended to take into account the essential social aspects of computer-mediated discourse and publication. This article provides a framework for analyzing the utility, usability and likeability of net and web services, and illustrates its application to significant aspects of supporting scholarly communities. The utility of the net and web are measured in terms of the growth of usage, and the different services involved are distinguished in terms of their specific utilities. A layered pr...

Abstract. This paper describes an extension to reinforcement learning (RL), in which a standard RL algorithm is augmented with a mechanism for transferring experience gained in one problem to new but related problems. In this approach, named Progressive RL, an agent acquires experience of operating in a simple environment through experimentation, and then engages in a period of introspection, during which it rationalises the experience gained and formulates symbolic knowledge describing how to behave in that simple environment. When subsequently experimenting in a more complex but related environment, it is guided by this knowledge until it gains direct experience. A test domain with 15 maze environments, arranged in order of difficulty, is described. A range of experiments in this domain are presented, that demonstrate the benefit of Progressive RL relative to a basic RL approach in which each puzzle is solved from scratch. The experiments also analyse the knowledge formed during introspection, illustrate how domain knowledge may be incorporated, and show that Progressive Reinforcement Learning may be used to solve complex puzzles more quickly.