(1992) Design Experiments: Theoretical and Methodological Challenges in Creating Complex Interventions in Classroom Settings Journal of the Learning Sciences: Vol 2, No 2, pp 141-178

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Design Experiments: Theoretical and Methodological Challenges in Creating Complex Interventions in Classroom Settings

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

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The concept of mind

A Common Network of Functional Areas for Attention and Eye Movements

Normal accidents. Living with high-risk technologies

Contents: Robert J. Mislevy, Introduction. J.R. Frederiksen, B. White, Intelligent Tutors as Intelligent Testers. J.R. Anderson, Analysis of Student Performance With the LISP Tutor. D.E. Kieras, The Role of Cognitive Simulation Models in the Development of Advanced Training and Testing Systems. A. Collins, Reformulating Testing to Measure Learning and Thinking: Comments on Chapters 1, 2, and 3. C.S. Gadd, H.E. Pople, Jr., Evidence From Internal Medicine Teaching Rounds of the Multiple Roles of Diagnosis in the Transmission and Testing of Medical Expertise. R.S. Siegler, J. Campbell, Diagnosing Individual Differences in Strategy Choice Procedures. J.C. Campione, A.L. Brown, Guided Learning and Transfer: Implications for Approaches to Assessment. S.P. Gott, The Assisted Learning of Strategic Skills: Comments on Chapters 5, 6, and 7. J.A. Reggia, C.L. D'Autrechy, Parsimonious Covering Theory in Cognitive Diagnosis and Adaptive Instruction. P. Langley, J. Wogulis, S. Ohlsson, Rules and Principles in Cognitive Diagnosis. S. Ohlsson, Trace Analysis and Spatial Reasoning: An Example of Intensive Cognitive Diagnosis and Its Implications for Testing. J. Wesley Regian, W. Schneider, Assessment Procedures for Predicting and Optimizing Skill Acquisition After Extensive Practice. A. Lesgold, S. Lajoie, D. Logan, G. Eggan, Applying Cognitive Task Analysis and Research Methods to Assessment. C.H. Frederiksen, A. Breuleux, Monitoring Cognitive Processing in Semantically Complex Domains. J.M. Orasanu, Diagnostic Approaches to Learning: Measuring What, How, and How Much: Comments on Chapters 12, 13, and 14. S. Embretson, Diagnostic Testing by Measuring Learning Processes: Psychometric Considerations for Dynamic Testing. S.P. Marshall, Generating Good Items for Diagnostic Tests. K.K. Tatsuoka, Toward an Integration of Item-Response Theory and Cognitive Error Diagnosis. R.L. Linn, Diagnostic Testing: Comments on Chapters 16, 17, and 18.

Diagnostic Monitoring of Skill and Knowledge Acquisition

How can a suspect's guilt or innocence be reliably tested? The validity of the polygraph, which measures changes in physiological arousal during a "guilty knowledge" test, is controversial (e.g., T. R. Bashore & P. E. Rapp, 1993; T. P. Cross & L. Saxe, 1992; D. T. Lykken, 1998; J. P. Rosenfeld, 1995; R. Steinbrook, 1992). One alternative to the polygraph examines event-related potentials recorded during a memory interference task (L. A. Farwell & E. Donchin, 1991). The present study extended this paradigm to determine whether response times (RTs) can accurately identify participants possessing specific guilty knowledge. Results from Experiment 1 showed that RT alone can reliably discriminate "guilty" from "innocent" participants. Experiments 2a and 2b indicated that an RT-based paradigm is more resistant to strategic manipulation than previously suggested (Farwell & Donchin, 1991). This RT-based paradigm may be a viable alternative to the polygraph for detecting guilty knowledge.

Using response time measures to assess guilty knowledge

Hughes (1984) has reported that the magnitude of the cue-validity effect in luminance detection is unaffected by target luminance. In three experiments, we explored the possible basis of this counterintuitive finding. The experiments focused on the design of the Hughes study, in which target luminance was treated as a between-blocks variable. Our results reveal that when target luminance is varied randomly within trial blocks, the cue-validity effect grows with declining target luminance. The difference between our findings and those of Hughes is interpreted in terms of cue-utilization strategies, which may adapt to target luminance when luminance remains invariant within trial blocks. Several alternative conceptions of the nature and locus of the cuevalidity effect in luminance detection are considered in light of these results.

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Effects of target luminance and cue validity on the latency of visual detection.

This article surveys and discusses human interaction with automated control systems that employ modes. We focus our discussion on the features of the control system that lead to mode confusion and error. We first provide working definitions of the term mode and discuss key constructs that contribute to mode error, such as mode ambiguity and user expectations. Following this, we discuss human interaction with automated control systems in general and cockpit automation in particular. To provide a formal description of human interaction with such control systems, we introduce a modeling language, Statecharts, which is used by system engineers to specify complex control systems. We use the Statecharts language to describe the 3 types of modes that are commonly found in modem control systems: interface, functional, and supervisory modes. Examples from cockpit automation are used to illustrate each type of mode. The article concludes with a brief discussion of the links between the mode constructs and formal re...

Michael Shafto

Papers

Diagnostic Monitoring of Skill and Knowledge Acquisition

Using response time measures to assess guilty knowledge

Effects of target luminance and cue validity on the latency of visual detection.

Modes in Human-Machine Systems: Constructs, Representation, and Classification

The Reliability, Validity, and Design of the Defining Issues Test.