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We present a system for generating and visualizing interactive 3D Augmented Reality tutorials based on 2D video input, which allows viewpoint control at runtime. Inspired by assembly planning, we analyze the input video using a 3D CAD model of the object to determine an assembly graph that encodes blocking relationships between parts. Using an assembly graph enables us to detect assembly steps that are otherwise difficult to extract from the video, and generally improves object detection and tracking by providing prior knowledge about movable parts. To avoid information loss, we combine the 3D animation with relevant parts of the 2D video so that we can show detailed manipulations and tool usage that cannot be easily extracted from the video. To further support user orientation, we visually align the 3D animation with the real-world object by using texture information from the input video. We developed a presentation system that uses commonly available hardware to make our results accessible for home use and demonstrate the effectiveness of our approach by comparing it to traditional video tutorials.

A new algorithm is presented to find clusters in a dataset of points in Rn with no prior knowledge of possible clustering. The algorithm detects clusters in a top down fashion by testing modality of density functions generated from the dataset and splitting the set accordingly. Results on synthetic and text datasets demonstrate that the method is comparable to other established unsupervised learning algorithms, which do in fact require the number of clusters ab initio. The method proves to be particularly suitable for certain distributions and offers a valid alternative in situations where most of the well-known algorithms do not produce consistent results.

Exams are an integral assessment component in CS1 courses. However, little research exists concerning the impact of the exam format on student performance. To address this deficit, we conducted a year-long study to investigate student performance across two test formats, paper and IDE, with the goal of identifying any differences attributable to the format. Data was collected from the second and third exams over the course of two semesters, with five sections per semester. Each exam, whether paper or IDE, was constructed from a common list of concepts to ensure comparability across formats and sections. To assess the impact of exam format, we considered three points of concern. The first factor, grade received, provides a direct indication of student performance. Next, we examined the type of error committed by students (either syntax or logic) to determine influence of exam format (e.g., Does an IDE reduce the occurrence of syntax errors?). Finally, we considered exam completion rates to address concerns such as if the additional feedback might be overwhelming students in an already stressful environment (exam-taking) leading to lower completion rates.