Integrating robotics activities in science curriculum provides rich opportunities to engage students in real world science and help them to develop conceptual understanding of physics principles through the process of investigation, data analysis, engineering design, and construction. In addition, students become more confident learners and develop better problem-solving and teamwork skills. In this paper we describe a successful use of LEGO® MINDSTORMS® in designing robotics-based activities for teaching high school physics classes. Students design and perform novel science investigations with a toolset that helps them achieve a high reproducibility in their experimental designs. Several example projects that utilize LEGO MINDSTORMS are presented.

Roboticists need to have a solid understanding of hardware and software. The standard computer science education in the United States, however, tends to teach students only about software. To remedy this situation, we explore new ways of teaching them about hardware in a playful way. Realizing that pinball machines are simple robots, we have developed a pinball machine interface between a PC and a recent Lord of the Rings pinball machine, which enables students to implement pinball games and gain knowledge of hardware and interface programming in the process. This paper describes both our pinball machine interface and our experience developing it. As far as we know, this is the first time that anyone has managed to control an existing pinball machine completely.

As educators, we have high hopes for Electronic Tangibles (ETs), we expect ETs to: Interest more students in the study of computing Broaden students' views of computing Invite non-majors to learn something about the computing Attract students to computer science as a major Help students learn about particular ETs Attract students to our classes by incorporating a flashy ET in the course material Improve student understanding of some difficult topics Maintain student interest throughout the class However some important questions arise: Can we and should we extend these benefits throughout the K-20 curriculum? And if we can't, are we guilty of bait-and-switch?

This paper discusses the development of a sequence of undergraduate courses forming the core curriculum in the Robotics Engineering (RBE) B.S. program at Worcester Polytechnic Institute (WPI). The laboratory robotics kit developed for the junior-level courses is presented in detail. The platform is designed to be modular and cost-effective and it is suitable for laboratory based robotics education. The system is ideal not only for undergraduate coursework but also may be adapted for graduate and undergraduate research as well as for exposing K-12 students to STEM.

In 3 experiments, participants learned bone anatomy by using a hand-held controller to rotate an on-screen 3D bone model. The on-screen bone included (OR condition) or did not include (no-OR condition) orientation references—visible lines marking its axes. The learning task involved rotating the on-screen bone to match target orientations. Learning outcomes were assessed by having participants identify anatomical features from different orientations. On the learning task, the OR group performed more accurately, directly, and quickly than the control group and high-spatial individuals outperformed low-spatial individuals. Assessments of anatomy learning indicated that under more challenging conditions, ORs elevated learning by low-spatial individuals to near that of high-spatial individuals. In Experiment 3, orientation references were shown to help learners avoid disorientation due to the symmetrical shape of the object.

This paper presents a brief summary of a number of different approaches to the semantic representation and automated interpretation of engineering data. In this context, engineering data is represented as Computer-Aided Design (CAD) files, 3D models or assemblies. Representing and reasoning about these objects is a highly interdisciplinary problem, requiring techniques that can handle the complex interactions and data types that occur in the engineering domain. This paper presents several examples, taken from different problem areas that have occupied engineering and computer science researchers over the past 15 years. Many of the issues raised by these problems remain open, and the experience of past efforts can serve to identify fertile opportunities for investigation today.

Medical experts routinely need to identify the shapes of anatomical structures, and surgeons report that they depend substantially on touch to help them with this process. In this paper, we discuss possible reasons why touch may be especially important for anatomical shape recognition in surgery, and why in this domain haptic cues may be at least as informative about shape as visual cues. We go on to discuss modern surgical methods, in which these haptic cues are substantially diminished. We conclude that a potential future challenge is to find ways to reinstate these important cues and to help surgeons recognize shapes in the restricted sensory conditions of minimally invasive surgery.

Diabetic retinopathy, an eye disorder caused by diabetes, is the primary cause of blindness in America and over 99% of cases in India. India and China currently account for over 90 million diabetic patients and are on the verge of an explosion of diabetic populations. This may result in an unprecedented number of persons becoming blind unless diabetic retinopathy can be detected early. Aravind Eye Hospitals is the largest eye care facility in the world, handling over 2 million patients per year. The hospital is on a massive drive throughout southern India to detect diabetic retinopathy at an early stage. To that end, a group of 10-15 physicians are responsible for manually diagnosing over 2 million retinal images per year to detect diabetic retinopathy. While the task is extremely laborious, a large fraction of cases turn out to be normal indicating that much of this time is spent diagnosing completely normal cases. This paper describes our early experiences working with Aravind Eye Hospitals to develop an automated system to detect diabetic retinopathy from retinal images. The automated diabetic retinopathy problem is a hard computer vision problem whose goal is to detect features of retinopathy, such as hemorrhages and exudates, in retinal color fundus images. We describe our initial efforts towards building such a system using a range of computer vision techniques and discuss the potential impact on early detection of diabetic retinopathy.

Feature extraction is one of the fundamental challenges in improving the accuracy of document classification. While there has been a large body of research literature on document classification, most existing approaches either do not have a high classification accuracy or require massive training sets. In this paper, we propose a simple feature extraction algorithm that can achieve high document classification accuracy in the context of development-centric topics. Our feature extraction algorithm exploits two distinct aspects in development-centric topics: most of these topics tend to be very focused (unlike semantically hard classification topics such as chemistry or banks) due to local language and cultural underpinnings in these topics, the authentic pages tend to use several region specific features. Our algorithm uses a combination of popularity and rarity as two separate metrics to extract features that describe a topic. Given a topic, our output feature set comprises of: (i) a list of popular keywords closely related to the topic; (ii) a list of rare keywords closely related to the topic. We show that a simple joint classifier based on these two feature sets can achieve high classification accuracy while each feature sub-set in itself is insufficient. We have tested our algorithm across a wide range of development-centric topics.

Food shortages are increasing in many areas of the world. In this paper, we consider the problem of understanding the causal relationships between socioeconomic factors in a developing-world household and their risk of experiencing famine. We analyse the extent to which it is possible to predict famine in a household based on these factors, looking at a data collected from 5404 households in Uganda. To do this we use a set of causal structure learning algorithms, employed as a committee that votes on the causal relationships between the variables. We contrast prediction accuracy of famine based on feature sets suggested by our prior knowledge and by the models we learn.