Our goal in this article is to reflect on the role LEGO robotics has played in college engineering education over the last 15 years, starting with the introduction of the RCX in 1998 and ending with the introduction of the EV3 in 2013. By combining a modular computer programming language with a modular building platform, LEGO Education has allowed students (of all ages) to become active leaders in their own education as they build everything from animals for a robotic zoo to robots that play children's games. Most importantly, it allows all students to develop different solutions to the same problem to provide a learning community. We look first at how the recent developments in the learning sciences can help in promoting student learning in robotics. We then share four case studies of successful college-level implementations that build on these developments.
The Framework for K-12 Science Education (National Research Council, 2012) outlines eight practices to represent the diverse ways scientists construct and evaluate knowledge. Engaging students in these practices is a key instructional target in the science classroom. This target, however, creates particular challenges for online instruction, which has predominantly focused on delivering content.This study shows the possibility of addressing disciplinary practices online, here in the context of a professional development course for in-service science teachers, which we designed based on previous work in responsive teaching. We examine an episode of the participants' engagement in an online text-based message board and identify evidence of disciplinary practices in their work. We discuss design elements and instructional choices that supported disciplinary engagement: building instruction around learners' ideas, developing online spaces in interaction with learners, and privileging learners' engagement in disciplinary practices as an objective.
The resizing of data, either upscaling or downscaling based on need for increased or decreased resolution, is an important signal processing technique due to the variety of data sources and formats used in today's world. Image interpolation, the 2D variation, is commonly achieved through one of three techniques: nearest neighbor, bilinear interpolation, or bicubic interpolation. Each method comes with advantages and disadvantages and selection of the appropriate one is dependent on output and situation specifications. Presented in this paper are algorithms for the resizing of images based on the analysis of the sum of primary implicants representation of image data, as generated by a logical transform. The most basic algorithm emulates the nearest neighbor technique, while subsequent variations build on this to provide more accuracy and output comparable to the other traditional methods. Computer simulations demonstrate the effectiveness of these algorithms on binary and grayscale images.
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