Rules and principles in cognitive diagnoses

Cognitive simulation is concerned with constructing process models of human cognitive behavior. Our work on the ACM system (Automated Cognitive Modeler) is an attempt to automate this process. The basic assumption is that all goal-oriented cognitive behavior involves search through some problem space. Within this framework, the task of cognitive diagnosis is to identify the problem space in which the subject is operating, identify solution paths used by the subject, and find conditions on the operators that explain those solution paths and that predict the subject's behavior on new problems. The work presented in this paper uses techniques from machine learning to automate the tasks of finding solution paths and operator conditions. We apply this method to the domain of multi-column subtraction and present results that demonstrate ACM's ability to model incorrect subtraction strategies. Finally, we discuss the difference between procedural bugs and misconceptions, proposing that errors due to misconceptions can be viewed as violations of principles for the task domain

An interactive multimedia learning environment for VLSI built with COSMOS

This paper presents Bigger Bits, an interactive multimedia learning environment that teaches students about VLSI within the context of computer electronics. The system was built with COSMOS (Content Oriented semantic Modelling Overlay Scheme), which is a modelling scheme that we developed for enabling the semantic content of multimedia to be used within interactive systems

OFMTutor: An operator function model intelligent tutoring system

The design, implementation, and evaluation of an Operator Function Model intelligent tutoring system (OFMTutor) is presented. OFMTutor is intended to provide intelligent tutoring in the context of complex dynamic systems for which an operator function model (OFM) can be constructed. The human operator's role in such complex, dynamic, and highly automated systems is that of a supervisory controller whose primary responsibilities are routine monitoring and fine-tuning of system parameters and occasional compensation for system abnormalities. The automated systems must support the human operator. One potentially useful form of support is the use of intelligent tutoring systems to teach the operator about the system and how to function within that system. Previous research on intelligent tutoring systems (ITS) is considered. The proposed design for OFMTutor is presented, and an experimental evaluation is described

An intelligent tutoring system for the investigation of high performance skill acquisition

The issue of training high performance skills is of increasing concern. These skills include tasks such as driving a car, playing the piano, and flying an aircraft. Traditionally, the training of high performance skills has been accomplished through the use of expensive, high-fidelity, 3-D simulators, and/or on-the-job training using the actual equipment. Such an approach to training is quite expensive. The design, implementation, and deployment of an intelligent tutoring system developed for the purpose of studying the effectiveness of skill acquisition using lower-cost, lower-physical-fidelity, 2-D simulation. Preliminary experimental results are quite encouraging, indicating that intelligent tutoring systems are a cost-effective means of training high performance skills

8. Issues in Intelligent Computer-Assisted Instruction: Eval uation and Measurement

In this chapter we plan to explore two issues in the field of intelligent computer assisted instruction (ICAI) that we feel offer opportunities to advance the state of the art. These issues are evaluation of ICAI systems and the use of the underlying technology in ICAI systems to develop tests. For each issue we will provide a theoretical context, discuss key constructs, provide a brief window to the appropriate literature, suggest methodological solutions and conclude with a concrete example of the feasibility of the solution from our own research. INTELLIGENT COMPUTER-ASSISTED INSTRUCTION (ICAI) ICAI is the application of artificial intelligence to computer-assisted instruction. Artificial intelligence, a branch of computer science, is making computers smart in order to (a) make them more useful and (b) understand intelligence (Winston, 1977). Topic areas in artificial intelligence have included natural language processing (Schank, 1980), vision (Winston, 1975), knowledge representation (Woods, 1983), spoken language (Lea, 1980), planning (Hayes-Roth, 1980), and expert systems (Buchanan, 1981). The field of Artificial Intelligence (AI) has matured in both hardware and software. The most commonly used language in the field is LISP (List Processing). A major development in the hardware area is that personal LISP machines are now available at a relatively low cost (20-50K) with the power of prior mainframes. In the software area two advances stand out: (a) programming support environments such as LOOPS (Bobrow & Stefik, 1983) and (b) expert system tools. These latter tools are now running on powerful micros. The application of expert systems technology to a host of real-world problems has demonstrated the utility of artificial intelligence techniques in a very dramatic style. Expert system technology is the branch of artificial intelligence at this point most relevant to ICAI. Expert Systems Knowledge-based systems or expert systems are a collection of problem-solving computer programs containing both factual and experiential knowledge and data in a particular domain. When the knowledge embodied in the program is a result of a human expert elicitation, these systems are called expert systems. A typical expert system consists of a knowledge base, a reasoning mechanism popularly called an inference engine and a friendly user interface. The knowledge base consists of facts, concepts, and numerical data (declarative knowledge), procedures based on experience or rules of thumb (heuristics), and causal or conditional relationships (procedural knowledge). The inference engine searches or reasons with or about the knowledge base to arrive at intermediate conclusions or final results during the course of problem solving. It effectively decides when and what knowledge should be applied, applies the knowledge and determines when an acceptable solution has been found. The inference engine employs several problem-solving strategies in arriving at conclusions. Two of the popular schemes involve starting with a good description or desired solution and working backwards to the known facts or current situation (backward chaining), and starting with the current situation or known facts and working toward a goal or desired solution (forward chaining). The user interface may give the user choices (typically menu-driven) or allow the user to participate in the control of the process (mixed initiative). The interface allows the user: to describe a problem, input knowledge or data, browse through the knowledge base, pose question, review the reasoning process of the system, intervene as necessary, and control overall system operation. Successful expert systems have been developed in fields as diverse as mineral exploration (Duda & Gaschnig, 1981) and medical diagnosis (Clancy, 1981). ICAI Systems ICAI systems use approaches artificial intelligence and cognitive science to teach a range of subject matters. Representative types of subjects include: (a) collection of facts, for example, South American geography in SCHOLAR (Carbonell & Collins, 1973); (b) complete system models, for example, a ship propulsion system in STEAMER (Stevens & Steinberg, 1981) and a power supply in SOPHIE (Brown, Burton, & de Kleer, 1982); (c) completely described procedural rules, for example, strategy learning, WEST (Brown, Burton, & de Kleer, 1982), or arithmetic in BUGGY (Brown & Burton, 1978); (d) partly described procedural rules, for example, computer programming in PROUST (Johnson & Soloway, 1983); LISP Tutor (Anderson, Boyle, & Reiser, 1985); rules in ALGEBRA (McArthur, Stasz, & Hotta, 1987); diagnosis of infectious diseases in GUIDON (Clancey, 1979); and an imperfectly understood complex domain, causes of rainfall in WHY (Stevens, Collins, & Goldin, 1978). Excellent reviews by Barr and Feigenbaum (1982) and Wenger (1987) document many of these ICAI systems. Representative research in ICAI is described by O\u27Neil, Anderson, and Freeman (1986) and Wenger (1987). Although suggestive evidence has been provided by Anderson et al. (1985), few of these ICAI projects have been evaluated in any rigorous fashion. In a sense they have all been toy systems for research and demonstration. Yet, they have raised a good deal of excitement and enthusiasm about their likelihood of being effective instructional environments. With respect to cognitive science, progress has been made in the following areas: identification and analysis of misconceptions or bugs (Clement, Lockhead, & Soloway, 1980), the use of learning strategies (O\u27Neil & Spielberger, 1979; Weinstein & Mayer, 1986), expert versus novice distinction (Chi, Glaser, & Rees, 1982), the role of mental models in learning (Kieras & Bovair, 1983), and the role of self-explanations in problem solving (Chi, Bassok, Lewis, Reimann, & Glaser, 1987). The key components of an ICAI system consist of a knowledge base: that is, (a) what the student is to learn; (b) a student model, either where the student is now with respect to subject matter or how student characteristics interact with subject matters, and (c) a tutor, that is, instructional techniques for teaching the declarative or procedural knowledge. These components are described in more detail by Fletcher (1985)

Automated Game Design Learning

While general game playing is an active field of research, the learning of game design has tended to be either a secondary goal of such research or it has been solely the domain of humans. We propose a field of research, Automated Game Design Learning (AGDL), with the direct purpose of learning game designs directly through interaction with games in the mode that most people experience games: via play. We detail existing work that touches the edges of this field, describe current successful projects in AGDL and the theoretical foundations that enable them, point to promising applications enabled by AGDL, and discuss next steps for this exciting area of study. The key moves of AGDL are to use game programs as the ultimate source of truth about their own design, and to make these design properties available to other systems and avenues of inquiry.Comment: 8 pages, 2 figures. Accepted for CIG 201

Multiple Viewpoints for Tutoring Systems.

This thesis investigates the issue of how a tutoring system, intelligent or otherwise, may be designed to utilise multiple viewpoints on the domain being tutored, and what benefits may accrue from this. The issue was relevant to earlier systems, such as WHY (Stevens et al. 1979) and STEAMER (Hollan et al. 1984). The relevant literature is reviewed, and criteria which must be met by our implementation of viewpoints are established. Viewpoints are conceptualised as pre-defined structures which can be represented in a tutoring system with the potential to increase its effectiveness and adaptability. A formalism is proposed where inferences are drawn from a model by a range of operators. The application of this combination to problems and goals is to be described heuristically. This formulation is then related to the educational philosophy of Cognitive Apprenticeship. The formalism is tested and refined in a protocol analysis study which leads to the definition of three classes of operators. The viewpoint structure is used to produce a detailed formulation of the domain of Prolog debugging for novices, with the goal that students should learn how different bugs may be localised using different viewpoints. Three models of execution are defined, based on those described by Bundy et al. (1985). These are mapped onto a restricted catalogue of bugs by specifying a number of conventions which produce a simplified and consistent domain suited to the needs of novices. VIPER, a tutoring system which can itself accomplish and explain the relevant domain tasks, is described. VIPER is based on a meta-interpreter which produces detailed execution histories which are then analysed. An evaluation of VIPER is reported, with generally favourable results. VIPER is discussed in relation to the research goals, the usefulness of Cognitive Apprenticeship in supporting such a design, and possible future work. This discussion exemplifies the use of established student modeling techniques, the implementation of other viewpoints on Prolog, and the application of the design strategy to other domains. Claims are made in relation to the formulation of viewpoints, the architecture of VIPER, and the relevance of Cognitive Apprenticeship to the use of multiple viewpoints

The effect of multiple knowledge sources on learning and teaching

Current paradigms for machine-based learning and teaching tend to perform their task in isolation from a rich context of existing knowledge. In contrast, the research project presented here takes the view that bringing multiple sources of knowledge to bear is of central importance to learning in complex domains. As a consequence teaching must both take advantage of and beware of interactions between new and existing knowledge. The central process which connects learning to its context is reasoning by analogy, a primary concern of this research. In teaching, the connection is provided by the explicit use of a learning model to reason about the choice of teaching actions. In this learning paradigm, new concepts are incrementally refined and integrated into a body of expertise, rather than being evaluated against a static notion of correctness. The domain chosen for this experimentation is that of learning to solve "algebra story problems." A model of acquiring problem solving skills in this domain is described, including: representational structures for background knowledge, a problem solving architecture, learning mechanisms, and the role of analogies in applying existing problem solving abilities to novel problems. Examples of learning are given for representative instances of algebra story problems. After relating our views to the psychological literature, we outline the design of a teaching system. Finally, we insist on the interdependence of learning and teaching and on the synergistic effects of conducting both research efforts in parallel

An explanation-driven understanding-directed model for intelligent tutoring systems

Imperial Users onl