Feedback is one of the most powerful influences on learning and achievement, but this impact can be either positive or negative. Its power is frequently mentioned in articles about learning and teaching, but surprisingly few recent studies have systematically investigated its meaning. This article provides a conceptual analysis of feedback and reviews the evidence related to its impact on learning and achievement. This evidence shows that although feedback is among the major influences, the type of feedback and the way it is given can be differentially effective. A model of feedback is then proposed that identifies the particular properties and circumstances that make it effective, and some typically thorny issues are discussed, including the timing of feedback and the effects of positive and negative feedback. Finally, this analysis is used to suggest ways in which feedback can be used to enhance its effectiveness in classrooms.

http://www.columbia.edu/~mvp19/ETF/Feedback.pdf

The Power of Feedback

Part I. Experimental Studies: 2. Experiment in psychology 3. Experiments on perceiving III Experiments on imaging 4-8. Experiments on remembering: (a) The method of description (b) The method of repeated reproduction (c) The method of picture writing (d) The method of serial reproduction (e) The method of serial reproduction picture material 9. Perceiving, recognizing, remembering 10. A theory of remembering 11. Images and their functions 12. Meaning Part II. Remembering as a Study in Social Psychology: 13. Social psychology 14. Social psychology and the matter of recall 15. Social psychology and the manner of recall 16. Conventionalism 17. The notion of a collective unconscious 18. The basis of social recall 19. A summary and some conclusions.

Remembering. A Study in Experimental and Social Psychology, Cambridge (University Press) 1964.

We outline a model for types and levels of automation that provides a framework and an objective basis for deciding which system functions should be automated and to what extent. Appropriate selection is important because automation does not merely supplant but changes human activity and can impose new coordination demands on the human operator. We propose that automation can be applied to four broad classes of functions: 1) information acquisition; 2) information analysis; 3) decision and action selection; and 4) action implementation. Within each of these types, automation can be applied across a continuum of levels from low to high, i.e., from fully manual to fully automatic. A particular system can involve automation of all four types at different levels. The human performance consequences of particular types and levels of automation constitute primary evaluative criteria for automation design using our model. Secondary evaluative criteria include automation reliability and the costs of decision/action consequences, among others. Examples of recommended types and levels of automation are provided to illustrate the application of the model to automation design.

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A model for types and levels of human interaction with automation

Automation is often problematic because people fail to rely upon it appropriately. Because people respond to technology socially, trust influences reliance on automation. In particular, trust guides reliance when complexity and unanticipated situations make a complete understanding of the automation impractical. This review considers trust from the organizational, sociological, interpersonal, psychological, and neurological perspectives. It considers how the context, automation characteristics, and cognitive processes affect the appropriateness of trust. The context in which the automation is used influences automation performance and provides a goal-oriented perspective to assess automation characteristics along a dimension of attributional abstraction. These characteristics can influence trust through analytic, analogical, and affective processes. The challenges of extrapolating the concept of trust in people to trust in automation are discussed. A conceptual model integrates research regarding trust in automation and describes the dynamics of trust, the role of context, and the influence of display characteristics. Actual or potential applications of this research include improved designs of systems that require people to manage imperfect automation.

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Trust in Automation: Designing for Appropriate Reliance

This paper addresses theoretical, empirical, and analytical studies pertaining to human use, misuse, disuse, and abuse of automation technology. Use refers to the voluntary activation or disengagement of automation by human operators. Trust, mental workload, and risk can influence automation use, but interactions between factors and large individual differences make prediction of automation use difficult. Misuse refers to over reliance on automation, which can result in failures of monitoring or decision biases. Factors affecting the monitoring of automation include workload, automation reliability and consistency, and the saliency of automation state indicators. Disuse, or the neglect or underutilization of automation, is commonly caused by alarms that activate falsely. This often occurs because the base rate of the condition to be detected is not considered in setting the trade-off between false alarms and omissions. Automation abuse, or the automation of functions by designers and implementation by man...

Humans and Automation: Use, Misuse, Disuse, Abuse

This book describes, for the first time in pedagogical form, an approach to computer-based work in complex sociotechnical systems developed over the last 30 years by Jens Rasmussen and his colleagues at Riso National Laboratory in Roskilde, Denmark. This approach is represented by a framework called cognitive work analysis. Its goal is to help designers of complex sociotechnical systems create computer-based information support that helps workers adapt to the unexpected and changing demands of their jobs. In short, cognitive work analysis is about designing for adaptation. The book is divided into four parts. Part I provides a motivation by introducing three themes that tie the book together--safety, productivity, and worker health. The ecological approach that serves as the conceptual basis behind the book is also described. In addition, a glossary of terms is provided. Part II situates the ideas in the book in a broader intellectual context by reviewing alternative approaches to work analysis. The limitations of normative and descriptive approaches are outlined, and the rationale behind the formative approach advocated in this book is explored. Part III describes the concepts that comprise the cognitive work analysis framework in detail. Each concept is illustrated by a case study, and the implications of the framework for design and research are illustrated by example. Part IV unifies the themes of safety, productivity, and health, and shows why the need for the concepts in this book will only increase in the future. In addition, a historical addendum briefly describes the origins of the ideas described in the book.

Cognitive Work Analysis: Toward Safe, Productive, and Healthy Computer-Based Work

A theoretical framework for designing interfaces for complex human-machine systems is proposed. The framework, called ecological interface design (EID), is based on the skills, rules, and knowledge taxonomy of cognitive control. The basic goals of EID are not to force processing to a higher level than the demands of the task require, and to support each of the three levels of cognitive control. Thus, an EID interface should not contribute to the difficulty of the task, and at the same time, it should support the entire range of activities that operators will be faced with. Three prescriptive design principles are suggested to achieve this objective, each directed at supporting a particular level of cognitive control. Particular attention is paid to presenting a coherent deductive argument justifying the principles of EID. Support for the EID framework is discussed. Some issues for future research are outlined. >

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Ecological interface design: theoretical foundations

Recently, a new class of artifacts has appeared in our environment: complex, high-technology work domains. An important characteristic of such systems is that their goal-relevant properties cannot be directly observed by the unaided eye. As a result, interface design is a ubiquitous problem in the design of these work environments. Nevertheless, the problem is one that has yet to be addressed in an adequate manner. An analogy to human perceptual mechanisms suggests that a smart instrument approach to interface design is needed to supplant the rote instrument (single-sensor-single-indicator) approach that has dominated to this point. Ecological interface design (ED) is a theoretical framework in the smart instrument vein that postulates a set of general, prescriptive principles for design. The goal of E D is twofold: first, to reveal the affordances of the work domain through the interface in such a way as to take advantage of the powerful capabilities of perception and action; and second, to provide the a...

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The Ecology of Human-Machine Systems II: Mediating 'Direct Perception' in Complex Work Domains

Ecological interface design (EID) is a theoretical framework for designing human-computer interfaces for complex sociotechnical systems. Its primary aim is to support knowledge workers in adapting to change and novelty. This literature review shows that in situations requiring problem solving, EID improves performance when compared with current design approaches in industry. EID has been applied to industry-scale problems in a broad variety of application domains (e.g., process control, aviation, computer network management, software engineering, medicine, command and control, and information retrieval) and has consistently led to the identification of new information requirements. An experimental evaluation of EID using a full-fidelity simulator with professional workers has yet to be conducted, although some are planned. Several significant challenges remain as obstacles to the confident use of EID in industry. Promising paths for addressing these outstanding issues are identified. Actual or potential applications of this research include improving the safety and productivity of complex sociotechnical systems.

Ecological Interface Design: Progress and Challenges

Research during recent years has revealed that human errors are not stochastic events which can be removed through improved training programs or optimal interface design. Rather, errors tend to reflect either systematic interference between various models, rules, and schemata, or the effects of the adaptive mechanisms involved in learning. In terms of design implications, these findings suggest that reliable human-system interaction will be achieved by designing interfaces which tend to minimize the potential for control interference and support recovery from errors. In other words, the focus should be on control of the effects of errors rather than on the elimination of errors per se. In this paper, we propose a theoretical framework for interface design that attempts to satisfy these objectives. The goal of our framework, called ecological interface design, is to develop a meaningful representation of the process which is not just optimised for one particular level of cognitive control, but that supports all three levels simultaneously. The paper discusses the necessary requirements for a mapping between the process and the combined action/observation surface, and analyses of the resulting influence on both the interferences causing error and on the opportunity for error recovery left to the operator. There has been a rapidly growing interest in the analysis of human error caused by technological development. The growing complexity of technical installations makes it increasingly difficult for operators to understand the system’s internal functions. At the same time, the large scale of operations necessary for competitive production makes the effects of human errors increasingly unacceptable. Naturally enough, human error analysis has become an essential part of systems design. In order to conduct such an analysis, a taxonomy suited to describe human errors is essential. The structure and dimensions of the error taxonomy, however, will depend on the aim of the analysis. Therefore, different categorisations of human errors are useful during the various stages of systems design. At least two different perspectives can be identified, each with its own unique set of requirements. One point of view is useful for predicting the effects of human error on system performance, i.e. a failure-mode-and-effect analysis. For this purpose, a taxonomy based on a model of human error mechanisms should be adopted. A second perspective for error analysis is required for identifying possible improvements in system design. In order to meet the requirements of such an analysis, an error taxonomy based on cognitive control mechanisms (Rasmussen, 1983) is more appropriate. Both types of analyses are essential to system design. The failure-mode-and-effect analysis allows the designer to identify plausible human

Kim J. Vicente

Papers

Cognitive Work Analysis: Toward Safe, Productive, and Healthy Computer-Based Work

Ecological interface design: theoretical foundations

The Ecology of Human-Machine Systems II: Mediating 'Direct Perception' in Complex Work Domains

Ecological Interface Design: Progress and Challenges

Coping with human errors through system design: implications for ecological interface design