Reasoning Strategies in the Context of Engineering Design with Everyday Materials

‘‘Making’’ represents an increasingly popular label for describing a form of engineering design. While making is growing in popularity, there are still open questions about the strategies that students are using in these activities. Assessing and improving learning in making/ engineering design contexts require that we have a better understanding of where students’ ideas are coming from and a better way to characterize student progress in open-ended learning environments. In this article, we use a qualitative analysis of students’ responses (N = 13) in order to identify the origins of their ideas. Four strategies emerged from this analysis: unexplained reasoning; materials-based reasoning; example-based reasoning; and principle-based reasoning. We examine key characteristics of each strategy and how each strategy relates to learning and expertise through in-depth case studies. Furthermore, we identify how these four strategies are a complement to prior work on analogical problem solving and creativity, and offer a number of unique contributions that are particularly relevant for engineering education. Finally, we include two coding schemes that can be used to classify students’ responses. Studying reasoning strategies in this way is a fruitful means for characterizing student learning in complex learning environments. Moreover, understanding reasoning strategies impacts the nature of student–teacher discussions and informs how to help students progress most effectively

Scratch for Sports: Athletic Drills as a Platform for Experiencing, Understanding, and Developing AI-Driven Apps

Culturally relevant and sustaining implementations of computing education are increasingly leveraging young learners' passion for sports as a platform for building interest in different STEM (Science, Technology, Engineering, and Math) concepts. Numerous disciplines spanning physics, engineering, data science, and especially AI based computing are not only authentically used in professional sports in today's world, but can also be productively introduced to introduce young learnres to these disciplines and facilitate deep engagement with the same in the context of sports. In this work, we present a curriculum that includes a constellation of proprietary apps and tools we show student athletes learning sports like basketball and soccer that use AI methods like pose detection and IMU-based gesture detection to track activity and provide feedback. We also share Scratch extensions which enable rich access to sports related pose, object, and gesture detection algorithms that youth can then tinker around with and develop their own sports drill applications. We present early findings from pilot implementations of portions of these tools and curricula, which also fostered discussion relating to the failings, risks, and social harms associated with many of these different AI methods – noticeable in professional sports contexts, and relevant to youths' lives as active users of AI technologies as well as potential future creators of the same