We describe an inertial gesture recognition framework composed of three parts. The first is a compact, wireless six-axis inertial measurement unit to fully capture three-dimensional motion. The second, a gesture recognition algorithm, analyzes the data and categorizes it on an axis-by-axis basis as simple motions (straight line, twist, etc.) with magnitude and duration. The third allows an application designer to combine recognized gestures both concurrently and consecutively to create specific composite gestures can then be set to trigger output routines.

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Inertial measurement components, which sense either acceleration or angular rate, are being embedded into common user interface devices more frequently as their cost continues to drop dramatically. These devices hold a number of advantages over other sensing technologies: they measure relevant parameters for human interfaces and can easily be embedded into wireless, mobile platforms. The work in this dissertation demonstrates that inertial measurement can be used to acquire rich data about human gestures, that we can derive efficient algorithms for using this data in gesture recognition, and that the concept of a parameterized atomic gesture recognition has merit. Further we show that a framework combining these three levels of description can be easily used by designers to create robust applications.

This study reports results from an empirical study of 54 software maintainers in the software maintenance department of a Norwegian company. The study addresses the relationship between amount of experience and maintenance skills. The findings were, amongst others: (1) While there may have been a reduction in the frequency of major unexpected problems from tasks solved by very inexperienced to medium experienced maintainers, additional years of general software maintenance experience did not lead to further reduction. More application specific experience, however, further reduced the frequency of major unexpected problems. (2) The most experienced maintainers did not predict maintenance problems better than maintainers with little or medium experience. (3) A simple one-variable model outperformed the maintainers' predictions of maintenance problems, i.e., the average prediction performance of the maintainers seems poor. Index terms: Software maintenance, predictions, learning from experience, human judgement. 1

This article provides an overview description of the four-component instructional design system (4C/ID-model) developed originally by van Merriënboer and others in the early 1990s (van Merriënboer, Jelsma, & Paas, 1992) for the design of training programs for complex skills. It discusses the structure of training blueprints for complex learning and associated instructional methods. The basic claim is that four interrelated components are essential in blueprints for complex learning: (a) learning tasks, (b) supportive information, (c) just-in-time (JIT) information, and (d) part-task practice. Instructional methods for each component are coupled to the basic learning processes involved in complex learning and a fully worked-out example of a training blueprint for “searching for literature ” is provided. Readers who benefit from a structured advance organizer should consider reading the appendix at the end of this article before reading the entire article. The instructional design enterprise is a bit like an ocean liner—huge, slow, ponderous, and requiring large amounts of energy and a great deal of time to move it even one degree off its current path. Recent discussions and developments in the field concern rapid technological and societal changes and the resulting need for very complex knowledge at work (Berryman, 1993; Cascio, 1995); new constructivist design theories for problem solving (Jonassen, 1994;

This article is concerned with the implications of the surprisingly different experimental practices in economics and in areas of psychology relevant to both economists and psychologists, such as behavioral decision making. We consider four features of experimentation in economics, namely, script enactment, repeated trials, performance-based monetary payments, and the proscription against deception, and compare them to experimental practices in psychology, primarily in the area of behavioral decision making. Whereas economists bring a precisely defined ìscriptî to experiments for

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On the Robustness of the Winner’s Curse Phenomenon ∗ We set out to find ways to help decision makers overcome the “winner’s curse, ” a phenomenon commonly observed in asymmetric information bargaining situations, and instead found strong support for its robustness. In a series of manipulations of the “Acquiring a Company Task, ” we tried to enhance decision makers ’ cognitive understanding of the task. We did so by presenting them with different parameters of the task, having them compare and contrast these different parameters, giving them full feedback on their history of choices and resulting outcomes, and allowing them to interact with a human opponent instead of a computer program. Much to our surprise, none of these manipulations led to a better understanding of the task. Our results demonstrate and emphasize the robustness of the winner’s curse phenomenon.

This study reports results from an empirical study of 109 software maintenance tasks in the software maintenance department of a Norwegian company. Findings were, amongst others, that: . While there was a reduction in the frequency of major unexpected problems from tasks solved by very inexperienced to medium experienced maintainers, additional years of experience did not lead to further reduction. . Increased experience did not lead to higher accuracy when predicting maintenance problems. . The prediction performance seems to be poor. A simple one-variable model outperformed the maintainer's predictions of maintenance problems. Our findings are supported by other studies reporting similar poor learning from experience in other domains and can be explained by learning hindrances in the maintenance process. To improve the learning from experience we recommend a stronger focus on creating better learning situations, higher quality of feedback, and training in the principles of probab...

Inaccurate estimates of software development effort is a frequently reported cause of IT-project failures. We report results from a study that investigated the effect of introducing lessons learned sessions on estimation accuracy and the assessment of uncertainty. Twenty software professionals were randomly allocated to a Learning group or a Control group and instructed to estimate and complete the same five development tasks. Those in the Learning group, but not those in the Control group, were instructed to spend at least 30 minutes on identifying, analyzing, and summarizing their effort estimation and uncertainty assessment experience after completing each task. We found that the estimation accuracy and the realism of the uncertainty assessment were not better in the Learning group than in the Control group. A follow-up study with 83 software professionals was completed to better understand this lack of improvement from lessons-learned sessions. The follow-up study found that receiving feedback about other software professionals’ estimation performance led to more realistic uncertainty assessments than receiving the same feedback of one’s own estimates. Lessons-learned sessions, we argue, have to be carefully designed to avoid wasting resources on learning processes that stimulate rather than reduce learning biases related to assessment of own estimation performance.