Search for: All records

The showcase will display a programmable sensor in action for understanding a person’s relationship to embodied data collection practices. The activity design is called a Data Scavenger Hunt, where participants record data and search through the data to identify different actions within automated graphs. The sensor can be adapted to record many variables from acceleration to light level. Our activity is designed for 8th to 12th graders in out-of-school contexts, so that they explore their physical movement as it relates to data and subsequent visualizations of that data. With this embodied data, there is an inherent relationship to the data, and we explore how that relationship creates opportunities for learning about agency in data collection processes. As such, we highlight the process of active data production rather than passive data collection in our activity. In initial testing, we see how learners are navigating discrepancies between their experiences and the data. 

For the past several years, our team has been developing and implementing curriculum and tools for integrating sports in data science learning experiences. We have developed a collection of activities and tools that we are eager to share with the larger data science community. The purpose of this session is to give participants an opportunity to explore some of these tools and activities in community with other data science educators and learners. The workshop will also serve as a space to try designing some new activities and ideate future directions for this work within the specific contexts of each workshop participant. Hence, we propose this workshop as a set of resources and a community that might spur new ideas that participants can adapt and extend based on the goals, needs, and affordances of their respective contexts. 

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. 

This experience report describes two years of work integrating coding with Micro:bits and Makecode into a Hawaiian immersion bilingual school setting to teach computer science (CS) skills in a place-based approach. This report highlights the collaborative partnerships and programs between a public Hawaiian immersion school, a non-profit organization that manages important cultural sites, and a university lab that develops sustainable technology. Students identified the importance of sustainability in computing by engaging with past, present, and future technologies in culturally relevant contexts. We describe ongoing work to improve the way we support students and teachers in a Hawaiian-immersion bilingual school setting. 

Around the world, many K-12 school systems are seeking ways to provide youth with computer science (CS) learning experiences. Often organizations aim to develop these opportunities by building capacity among science, technology, engineering, and mathematics teachers. In other instances, school may engage with language arts, history, and library teachers to teach computer science content. Seldom, however, do schools leverage the rich opportunities for integrating computer science with physical education (PE). This paper explores an on-going partnership among university researchers, and elementary school coding and PE teachers. During spring of 2021, the group designed and tested coding and physical movement related activities for students to complete across their PE and coding classes. The team iterated on those activities throughout 2021 and 2022. This paper highlights the utility of this unique collaboration and describes some of the initial designs that emerged. The paper also touches on preliminary evaluation of the activities, and notes some of the project team's plans for future iterations. Broadly speaking, the activities piqued student interest and helped advance new perspectives of themselves, CS, and their teachers. 

Programming can be an emotional experience, particularly for undergraduate students who are new to computer science. While researchers have interviewed novice programmers about their emotional experiences, it can be difficult to pinpoint the specific emotions that occur during a programming session. In this paper, we argue that electrodermal activity (EDA) sensors, which measure the physiological changes that are indicative of an emotional reaction, can provide a valuable new data source to help study student experiences. We conducted a study with 14 undergraduate students in which we collected EDA data while they worked on a programming problem. This data was then used to cue the participants’ recollections of their emotions during a retrospective interview about the programming experience. Using this methodology, we identified 21 distinct events that triggered student emotions, such as feeling anxiety due to a lack of perceived progress on the problem. We also identified common patterns in EDA data across multiple participants, such as a drop in their physiological reaction after developing a plan, corresponding with a calmer emotional state. These findings provide new information about how students experience programming that can inform research and practice, and also contribute initial evidence of the value of EDA data in supporting studies of emotions while programming.