BigSUR: Large-scale Structured Urban Reconstruction

The creation of high-quality semantically parsed 3D models for dense metropolitan areas is a fundamental urban modeling problem. Although recent advances in acquisition techniques and processing algorithms have resulted in large-scale imagery or 3D polygonal reconstructions, such data-sources are typically noisy, and incomplete, with no semantic structure. In this paper, we present an automatic data fusion technique that produces high-quality structured models of city blocks. From coarse polygonal meshes, street-level imagery, and GIS footprints, we formulate a binary integer program that globally balances sources of error to produce semantically parsed mass models with associated facade elements. We demonstrate our system on four city regions of varying complexity; our examples typically contain densely built urban blocks spanning hundreds of buildings. In our largest example, we produce a structured model of 37 city blocks spanning a total of 1, 011 buildings at a scale and quality previously impossible to achieve automatically

Improving E-Learning Videos Using Personalization and Social Signals

Videos can be more engaging and relevant to a course than a textbook, but their linear format frustrates many students. Content based topic detection and tracking algorithms are unlikely to be able to exploit the variety of presentation methods in video lectures; therefore we propose to develop a tool to make videos more navigable and more effective for information retrieval and teaching by tracking and exploiting behavior patterns of students who watch the video. Students will be able to personalize the content by highlighting the video, adding comments, and hiding portions of the video in order to skim the video more effectively. This will allow instructors to segment students based on their habits and preferred mode of learning and adapt the course material accordingly. In this paper we present our early results of indexing video lectures into chapters and developing a discussion board based on each chapter

An Adaptive Time Reduction Technique for Video Lectures

Lecture videos are a widely used resource for learning. A simple way to create videos is to record live lectures, but these videos end up being lengthy, include long pauses and repetitive words making the viewing experience time consuming. While pauses are useful in live learning environments where students take notes, we question the value of pauses in video lectures. Techniques and algorithms that can shorten such videos can have a huge impact in saving students\u27 time and reducing storage space. We study this problem of shortening videos by removing long pauses and adaptively modifying the playback rate by emphasizing the most important sections ofthe video and its effect on the student community. The playback rate is designed in such a way to play uneventful sections faster and significant sections slower. Important and unimportant sections of a video are identified using textual analysis. We use an existing speech-to-text algorithm to extract the transcript and apply latent semantic analysis and standard information retrieval techniques to identify the relevant segments of the video. We compute relevance scores of different segments and propose a variable playback rate for each of these segments. The aim is to reduce the amount of time students spend on passive learning while watching videos without harming their ability to follow the lecture. We validate our approach by conducting a user study among computer science students and measuring their engagement. The results indicate no significant difference in their engagement when our method is compared to the original unedited video

Evaluating the Effectiveness of Flipped Classrooms for Teaching CS1

An alternative to the traditional classroom structure that has seen increased use in higher education is the flipped classroom. Flipping the classroom switches when assignments (e.g. homework) and knowledge transfer (e.g. lecture) occur. Flipped classrooms are getting popular in secondary and postsecondary teaching institutions as evidenced by the marked increase in the study, use, and application of the flipped pedagogy as it applies to learning and retention. The majority of the courses that have undergone this change use applied learning strategies and include a significant “learning-by-doing” component. The research in this area is skewed towards such courses and in general there are many considerations that educators ought to account for if they were to move to this form of teaching. Introductory courses in computer programming can appear to have all the elements needed to move to a flipped environment; however, initial observations from our research identify possible pitfalls with the assumption. In this work in progress the authors discuss early results and observations of implementing a flipped classroom to teach an introductory programming course (CS1) to engineering, engineering technology, and software engineering undergraduates