As enrollments and class sizes in postsecondary institutions have increased, instructors have sought automated and lightweight means to identify students who are at risk of performing poorly in a course. This identification must be performed early enough in the term to allow instructors to assist those students before they fall irreparably behind. This study describes a modeling methodology that predicts student final exam scores in the third week of the term by using the clicker data that is automatically collected for instructors when they employ the Peer Instruction pedagogy. The modeling technique uses a support vector machine binary classifier, trained on one term of a course, to predict outcomes in the subsequent term. We applied this modeling technique to five different courses across the computer science curriculum, taught by three different instructors at two different institutions. Our modeling approach includes a set of strengths not seen wholesale in prior work, while maintaining competitive levels of accuracy with that work. These strengths include using a lightweight source of student data, affording early detection of struggling students, and predicting outcomes across terms in a natural setting (different final exams, minor changes to course content), across multiple courses in a curriculum, and across multiple institutions.

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This paper presents a modified diary study that investigated how people performed personally motivated searches in their email, in their files, and on the Web. Although earlier studies of directed search focused on keyword search, most of the search behavior we observed did not involve keyword search. Instead of jumping directly to their information target using keywords, our participants navigated to their target with small, local steps using their contextual knowledge as a guide, even when they knew exactly what they were looking for in advance. This stepping behavior was especially common for participants with unstructured information organization. The observed advantages of searching by taking small steps include that it allowed users to specify less of their information need and provided a context in which to understand their results. We discuss the implications of such advantages for the design of personal information management tools.

Current computer-based design tools for mechanical engineers are not tailored to the early stages of design. Most designs start as pencil and paper sketches, and are entered into CAD systems only when nearly complete. Our goal is to create a kind of "magic paper" capable of bridging the gap between these two stages. We want to create a computer-based sketching environment that feels as natural as sketching on paper, but unlike paper, understands a mechanical engineer's sketch as it is drawn. One important step toward realizing this goal is resolving ambiguities in the sketch-determining, for example, whether a circle is intended to indicate a wheel or a pin joint-and doing this as the user draws, so that it doesn't interfere with the design process. We present a method and an implemented program that does this for freehand sketches of simple 2-D mechanical devices.