Human verbal explanations are essentially interactive. If someone is giving a complex explanation, the hearer will be given the opportunity to indicate whether they are following as the explanation proceeds, and if necessary interrupt with clarification questions. These interactions allow the speaker to both clear up the hearer's immediate difficulties as they arise, and to update assumptions about their level of understanding. Better models of the hearer's level of understanding in turn allow the speaker to continue the explanation in a more appropriate manner, lessening the risk of continuing confusion.
This study provides experiment results as an educational reference for instructors to help student obtain a better way to learn orthographic views in graphical course. A visual experiment was held to explore the comprehensive differences between 2D static and 3D animation object features; the goal was to reduce the possible misunderstanding factors in the learning process. This empirical study provided one hundred and twenty Taiwanese freshmen four types of visualization, which includes two 2D static depictions (2DT, 2DR), and two 3D animations (3DT, 3DR), to meet five surface styles on orthographic views. The responses to views ability test and interviews illustrated that applying 3D animations shows better performance in understanding the appearances and features of objects constructed by oblique and double-curved surfaces. The application of 3D animations results also demonstrates a better visual comprehension for students, especially when objects are constructed by the complicated features.
The Virtual Classroom® consists of software enhancements to the basic capabilities of a computer-mediated communication system in order to support collaborative learning. Results of quasi-experimental field trials which included matched sections of college courses delivered in the traditional and virtual classrooms indicate that there is no consistent significant difference between the two modes in mastery of material by students, as measured by grades: in a computer science course, grades were better in the on-line section. Subjectively, most students report that the Virtual Classroom improves access to educational activities and is, overall, a "better" mode of learning. However, these favorable outcomes are contingent upon a number of variables, including student characteristics, adequate equipment access, and instructor-generated collaborative learning processes.
Researchers in the human-computer interaction field have advocated that interface designers use analytical models of the user (e.g. the GOMS model) to help them consider user needs during the design process. This paper surveys the literature on analytical models for interface designers, focusing initially on empirical studies of the validity of these models. This survey shows that analytical models can by used by interface designers in two ways: (1) as task analytic tools that help in generating preliminary design ideas, and (2) as tools for evaluating preliminary designs by predicting user performance and satisfaction. Empirical studies have demonstrated that analytical models can be used in the task analysis phase of design; in these studies, models were successfully used to generate new designs for existing interfaces, with the new designs leading to improved user performance.
In recent years the emphasis in natural language understanding research has shifted from studying mechanisms for understanding isolated utterances to developing strategies for interpreting sentences within the context of a discourse or an extended dialogue. A very fruitful approach to this problem has derived from a view of human behavior as goal-directed and understanding as explanation-based. According to this view, people perform actions and communicate to advance their goals, and language understanding therefore involves recognizing and reasoning about the goals and plans of others. This paper explores plan inference in natural language understanding. It presents a core set of ideas on which most models of plan recognition are based and illustrates these by critically analysing three systems in detail. It then discusses issues that have been addressed by various research efforts, explores the major problems that limit the capability of current plan recognition systems, and describes current research directed toward solving some of these problems.
Much of the literature concerned with understanding the nature of programming skill has focused explicitly upon the declarative aspects of programmers' knowledge. This literature has sought to describe the nature of stereotypical programming knowledge structures and their organization. However, one major limitation of many of these knowledge-based theories is that they often fail to consider the way in which knowledge is used or applied. Another strand of literature is less well represented. This literature deals with the strategic elements of programming skill and is directed towards an analysis of the strategies commonly employed by programmers in the generation and the comprehension of programs. In this paper an attempt is made to unify various analyses of programming strategy. This paper presents a review of the literature in this area, highlighting common themes and concerns, and proposes a model of strategy development which attempts to encompass the central findings of previous research in this area. It is suggested that many studies of programming strategy are descriptive and fail to explain why strategies take the form they do or to explain the typical strategy shifts which are observed during the transitions between different levels of skill. This paper suggests that what is needed is an explanation of programming skill that integrates ideas about knowledge representation with a strategic model, enabling one to make predictions about how changes in knowledge representation might give rise to particular strategies and to the strategy changes associated with developing expertise. This paper concludes by making a number of brief suggestions about the possible nature of this model and its implications for theories of programming expertise.
In a two experiment sequence, the authors investigated whether a blinking cursor facilitates performance for word processing and form-entry type applications. Previous reported work has not focused on this important aspect of blink, but rather on the blink of complete groups of target elements to distinguish them from other non-target elements. The results of both experiments reported here demonstrate that a blinking cursor does in fact produce significantly faster performance.
Human reasoning under uncertainty has been shown to consist of a series of "local computations" in which a complex problem is broken into a series of simpler decisions. For practical and epistemological reasons, expert systems which must reason under uncertainty have taken a similar approach. Thus, choice of an appropriate Uncertainty Representation Scheme (URS) is dependent upon the robustness of the scheme to variations in the local computations that are expected in the application area. Researchers have based their claims for robustness of a particular URS on the apparent match between the scheme's stated assumptions and the expected "statistical" characteristics of the local computations. The limited empirical data that are available suggest that these claims may be ill-founded. Rather, this paper argues that a scheme's robustness should be measured relative to humans' local computations which are used to solve the problem, and we present a methodology for doing so. Application of this methodology suggests that representations of local computations are strongly influenced by the choice of a URS and that the accuracy of the resulting solutions are substantially influenced. Implications for full scale applications and directions for further research are also discussed.
Semiotic approaches to design have recently shown that systems are messages sent from designers so users. In this paper we examine the nature of such messages and show that systems are messages that can send and receive other messages—they are metacommunication artefacts that should be engineered according to explicit semiotic principles. User interface languages are the primary expressive resource for such complex communication environments. Existing cognitively-based research has provided results which set the target interface designers should hit, but little is said about how to make successful decisions during the process of design itself. In an attempt to give theoretical support to the elaboration of user interface languages, we explore Eco's Theory of Sign Production (U. Eco, A Theory of Semiotics, Bloomington, IN: Indiana University Press, 1976) and build a semiotic framework within which many design issues can be explained and predicted.
This paper describes the ideas leading to the development and construction of a Graphical Integrated Programming Support Environment (GRIPSE) for a modified form of the Pascal language. In order to represent all procedural and declarative aspects of a Pascal program, the original Nassi-Shneiderman diagrams forming the graphical basis of GRIPSE have been augmented into a multi-level, three dimensional system capable of supporting all static and most dynamic aspects of a program in a uniform manner. The dynamic view exists by virtue of an interpreter that has been incorporated along with debugging aids. A planned extension to the environment is an interactive form of mutation testing called Firm mutation which exploits the middle-ground between Strong and Weak mutation, both of which have traditionally been applied in a non-conversational, non-interactive mode of use.
Current user interfaces fail to support some work habits that people naturally adopt when interacting with general-purpose computer environments. In particular, users frequently and persistently repeat their activities (e.g. command line entries, menu selections, navigating paths), but computers do little to help them to review and re-execute earlier ones. At most, systems provide ad hoc history mechanisms founded on the premise that the last few inputs form a reasonable selection of candidates for reuse.
This paper presents a methodology still under development for the analysis of human errors, named DREAMS (Dynamic Reliability technique for Error Assessment in Man-Machine Systems), which is dedicated to human reliability analysis and which identifies the origin of human errors in the dynamic interaction of the operator and the plant control system. This distinctive aspect differentiates DREAMS from the most commonly applied techniques in the nuclear as well as the conventional industries. Indeed, in the proposed methodology, the human behaviour depends on the working environment in which the operator acts ("external world"), i.e. the control room, and on the "internal world", i.e. his psychological conditions, which are related to stress, emotional factors, fixations, as well as to lack of intrinsic knowledge. As a logical consequence of the dynamic interaction of the human with the plant under control, either the error tendency or the ability to recover from a critical situation may be enhanced. The probability of erroneous actions and of recovery is thus a function of a generic "stress-in-action" correlation, which represents the effect of the internal-external worlds on the operator behaviour. This leads to the evaluation of Instantaneous Human Error/Recovery Probabilities (IHEP and IHRP) which are dynamically evaluated during the unfolding of the sequence of the man-machine interaction. These can then be used for evaluating an overall probability measure of plant safety related to human erroneous actions. A study case of application of the methodology to a control system of a real nuclear power plant is given in the paper as a sample case.
Computer systems based on cooperating agent architectures are currently introduced in industrial process supervision and control applications as operator support systems in tasks such as fault diagnosis, system restoration etc. Cooperating agents are relevant to these applications since they involve a high degree of physical distribution, the operators' decisions are often based on multiple conflicting views which can be moderated by the cooperating agents, and the domains are complex with high degree of modularity.
One invariant of problem solving is based on properties (e.g. memory capacity) of the symbol system used to process information and events. This invariant generalizes across agents and domains but usually lacks the power to explain success on specific problem-solving tasks. A second invariant is based on properties of the knowledge required to perform a given task. This invariant, often termed the knowledge principle, attempts to account for success in specific tasks but typically does not generalize from one domain to the next or from one agent to the next. In this paper a third invariant is proposed, one that is based on the relationship between a problem-solving agent and its environment. This invariant captures the requirements of a problem-solving task as well as the role of domain knowledge at a level that is independent of a particular agent, representation or implementation. We call this invariant "expertise". Five types of expertise are proposed. Features of each type are described using the concept of argument. For each type of expertise there is a corresponding type of argument. Examples of types of expertise are given from chess, business and social policy, and medicine. Evidence is provided for the presence of types of expertise from the analysis of the behavior of two individuals (Ph.D.-level statisticians) solving problems as consultants in the domain of industrial experimental design. Types of expertise represented in several first-generation expert systems are also identified and discussed.
Although the visual display unit (VDU) is becoming an increasingly popular means of displaying documents, users often show a strong preference for the "hard-copy" medium of document presentation when it comes to reading activities such as those that involve proof-reading or refereeing the document. This is partly attributed to the difficulties of annotating documents presented in the electronic medium. Voice recording may be a more acceptable medium for annotating documents that are presented on VDUs, as it overcomes many of the problems associated with the typed annotation of electronic documents. Experiment 1 compared two computer-based annotation media (typed and spoken input) with the method of writing annotations on the document. Findings suggested that writing was a superior method of annotation to the other media in terms of number of annotations elicited, speed of recording and user preference. Experiment 2 differed from the first experiment in the way in which written annotations were recorded and in the amount of pre-trial practice given to subjects. In the second experiment voice resulted in shorter task completion times than either writing or typing. This is taken as limited support for a theory that a small amount of pre-trial practice is of greater benefit to the utility of a voice annotation facility than it is to a facility for typing annotations. The majority of differences between writing and the other conditions observed in Experiment 1 were not found in Experiment 2. The contrast between the two sets of results is discussed in terms of the subjects' familiarity with the methods of annotation involved and the advantages of a facility for annotating on the document. The discussion concludes with a set of guide-lines for the implementation of a voice annotation facility.
