Search for: All records

Abstract Music and computer science (CS) have profound historical and structural connections, with programming music offering a promising avenue for engaging children in CS through creative expression. To foster this engagement, our team developed M-Flow, a flow-based music programming platform designed to introduce students to CS via music. Despite extensive existing research in music and CS education, experience reports and empirical studies on K-12 teachers' implementation and its impact on young kids' learning are limited. Therefore, we recruit elementary school teachers and students with no or limited prior programming experience, introducing them to M-Flow and its curriculum through a professional development workshop, a semester's job embedded support, and classroom implementation. We describe the experiences of teachers as they attempt to integrate music and CS, the challenges they face, and the influence on students' attitudes toward learning computing concepts. Specifically, we reflect on our intervention by conducting a sequential mixed-method evaluation. During the qualitative phase, we collected multiple sources of data from three teachers through focus groups and debriefings after a semester of classroom implementation. Thematic analysis of workshop activities, interviews, and debrief videos revealed three themes with seven sub-themes on teachers' integration of flow-based music programming and two themes with five sub-themes on challenges faced by the teachers. In the quantitative phase, we gathered data on attitudes and self-efficacy from 75 students taught by these teachers. Results indicate that the flow-based music programming environment provided an engaging programming experience for students and significantly increased their self-efficacy towards learning programming. 

Abstract: Children can feel disengaged from STEM subjects taught in schools, which are often presented in ways that are not connected to their interests and everyday experiences. The subject of waves is fundamental for understanding a variety of scientific and engineering processes, from gravitation to telecommunications. Furthermore, the subject of waves presents an excellent opportunity to bring to the school activities connected to one of children’s deepest interests: music. For this, we created Listening to Waves, a program that has been developing web applications and curricular activities that allows users to connect with the science of waves by playfully exploring and creating sound and music. Previous work by our team has shown that these types of activities can be powerful for engaging children in science, especially those typically underrepresented in STEM domains. However, a fundamental step for their spreading is that they are also engaging for teachers. To disseminate the program and evaluate its potential to engage teachers, we created a three-day professional development workshop for teachers serving underserved communities. We administered quantitative and qualitative surveys before the workshop, immediately after the workshop, and after the teachers implemented the materials in their classrooms. The surveys indicate that the experience improved teachers’ attitudes toward the subject, including their comfort in teaching the subject, their enjoyment, and their perception of the children’s enjoyment. This effect was particularly relevant for teachers who were not initially engaged, either because of a lack of experience or lack of knowledge. Taken together, these results indicate that activities connecting music and STEM have the potential to spread throughout the formal educational system by engaging teachers, and that they can be instrumental in engaging children in STEM. This research is funded by NSF’s ITEST award “Increasing Students' Interest in STEM through the Science of Music.” 

Data literacy is becoming increasingly important for many professions due to the growing reliance on data-driven systems in the workplace. This means that children in school today need to be able to understand and work with data in order to be prepared for their future careers. However, many schools are having difficulty finding good resources for teaching data science. The authors of this paper describe the online tools and a workshop they developed to introduce students to working with data in a hands-on way, using sound and multimodal interaction. The initial feedback from participants indicated that the workshop helped improve their understanding of the practical applications of data and statistical operations. 

Students of all socioeconomic backgrounds love music and express their identity through music. There are strong historical connections between music and computing, and computer-based music has a heavy presence in contemporary popular culture. Thus, programming electronic music can provide the type of authentic learning experience that fosters participation in computer science (CS) by minoritized students. Although important efforts have been made in that direction, they have not reached young children in mainstream public classrooms, particularly in schools serving children from low-income and marginalized backgrounds. Developing a computational tool and educational program that reaches this key demographic holds the potential to greatly increase CS knowledge and participation in the future workforce. For this, our team has created M-flow, a flow-based music programming platform that seeks to be engaging for children from the outset, and that makes it extremely easy for non-specialized teachers to learn and implement CS activities in the classroom. 

Abstract Neurons in the CA1 area of the mouse hippocampus encode the position of the animal in an environment. However, given the variability in individual neurons responses, the accuracy of this code is still poorly understood. It was proposed that downstream areas could achieve high spatial accuracy by integrating the activity of thousands of neurons, but theoretical studies point to shared fluctuations in the firing rate as a potential limitation. Using high-throughput calcium imaging in freely moving mice, we demonstrated the limiting factors in the accuracy of the CA1 spatial code. We found that noise correlations in the hippocampus bound the estimation error of spatial coding to ~10 cm (the size of a mouse). Maximal accuracy was obtained using approximately [300–1400] neurons, depending on the animal. These findings reveal intrinsic limits in the brain’s representations of space and suggest that single neurons downstream of the hippocampus can extract maximal spatial information from several hundred inputs. 

Data literacy has taken a front seat in present day conversations on education reform primarily due to the need for education on disruptive technologies such as Artificial Intelligence and Internetof- Things that are rapidly transforming the future of work and life. School systems worldwide have already included data literacy several years ago in their curriculum, still the definition of data and the activities utilized to teach data handling are verily outdated and seek change to reflect the new relationship we are starting to form with data. This paper discusses a workshop conducted for data literacy education in schools. The hands-on activity based approach taken in the workshop seeks to offer a broad definition to data along the lines of real world application in terms of our human sensory perception of audition, vision, and haptics.