Systematic errors in performance are an important aspect of human behavior that have not received adequate explanation. One such systematic error is termed post-completion error; a typical example is leaving one’s card in the automatic teller after withdrawing cash. This type of error seems to occur when people have an extra step to perform in a procedure after the main goal has been satisfied. The fact that people frequently make this type of error, but do not make this error every time, may best be explained by considering the working memory load at the time the step is to be performed: the error is made when the load on working memory is high, but will not be made when the load is low. A model of performance in the task was constructed using Just and Carpenter’s (1992) CAPS that predicted that high working memory load should be associated with post-completion errors. Two experiments confirmed that such errors can be produced in a laboratory as well as a naturalistic setting, and that the conditions under which the CAPS model makes the error are consistent with the conditions under which the errors occur in

This research was partially sponsored by NCCOSC under Contract No. N66001-94-C-6037, Arpa OrderNo. B326, and partially by NSF under grant number IRI-9319969. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official poli-cies, either expressed or implied, of the U.S. Government.

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Student modeling (SM) is recognized as one of the central problems in the area of Intelligent Tutoring Systems. Numerous SM approaches have been proposed and used with more or less success. Constraint-based modeling is new approach, which has been used successfully in three tutors developed in our group. The approach is extremely efficient, and it overcomes many problems that other student modelling approaches suffer from. We present the advantages of CBM over other similar approaches, describe three constraint-based tutors and present our future research plans.

This paper will provide a mechanism

The computer and communication systems that office workers currently use tend to interrupt at inappropriate times or unduly demand attention because they have no way to determine when an interruption is appropriate. Sensor-based statistical models of human interruptibility offer a potential solution to this problem. Prior work to examine such models has primarily reported results related to social engagement, but it seems that task engagement is also important. Using an approach developed in our prior work on sensor-based statistical models of human interruptibility, we examine task engagement by studying programmers working on a realistic programming task. After examining many potential sensors, we implement a system to log low-level input events in a development environment. We then automatically extract features from these low-level event logs and build a statistical model of interruptibility. By correctly identifying situations in which programmers are non-interruptible and minimizing cases where the model incorrectly estimates that a programmer is non-interruptible, we can support a reduction in costly interruptions while still allowing systems to convey notifications in a timely manner. Author Keywords Situationally appropriate interaction, managing human attention, sensor-based interfaces, context-aware computing, machine learning, interruptibility.

Over the decades, computational models of human cognition have advanced from programs that produce output similar to that of human problem solvers to systems that mimic both the products and processes of human performance. In this paper, we describe a model that achieves the next step in this progression: predicting individual participants' performance across multiple tasks after estimating a single individual difference parameter. We demonstrate this capability in the context of a model of working memory, where the individual difference parameter for each participant represents working memory capacity. Specifically, our model is able to make zero-parameter predictions of individual participants' performance on a second task after separately fitting performance on a preliminary task. We argue that this level of predictive ability offers an important test of the theory underlying our model.

This paper proposes a new programming language and environment for children. This system will be designed to be easy to learn and use, without sacrificing the power necessary to create sophisticated programs that rival commercial software such as games and simulations. Throughout the design and refinement of this system, I will apply prior results from empirical studies of programmers and the psychology of programming, my own empirical studies about the ways that nonprogrammers naturally express solutions to programming tasks, and usability testing.

Models of programming and debugging suggest many causes of errors, and many classifications of error types exist. Yet, there has been no attempt to link causes of errors to these classifications, nor is there a common vocabulary for reasoning about such causal links. This makes it difficult to compare the abilities of programming styles, languages, and environments to prevent errors. To address this issue, this paper presents a model of programming errors based on past studies of errors. The model was evaluated with two observational of Alice, an event-based programming system, revealing that most errors were due to attentional and strategic problems in implementing algorithms, language constructs, and uses of libraries. In general, the model can support theoretical, design, and educational programming research.

Current fashion in "user-friendly" softw overreliance on direct manipulation inte: (and thus trhly user-friendly), applicatk faes and domain-enriched languages th This paper discusses some of the desigqr ation of such programmable applications,5 "SchemePaint," a graphics application t interface with an interpreter for (a "gral; Copyright ) Massachusetts Institu This report describes research done at the Artificial Int Institute of Technology. Support for the laboratory's in part by the Advanced Research Projects Agency of It Naval Research contract N00014-89-J-3202 and by the number MIP-9001651.