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1. Teachers’ goals for seeing with data

   Jones, Ryan Seth; Salisbury, Sara; Scott, Fonya; Shepherd, Lisa (December 2022, Psychology of Mathematics Education North American Chapter)

   Lischka, Alyson; Dyer, Elizabeth; Jones, Ryan Seth; Lovett, Jennifer; Strayer, Jeremy; Drown, Samantha (Ed.)

   We present an interview study of 6th grade math and science teachers’ expressed goals for engaging their students with data. We explored this across disciplinary boundaries to contribute to a body of knowledge that can support the development of a more coherent experience for students across math and science classes. Our teachers were all highly motivated to engage their students with data, and all wanted their students to see things with their data models. However, we observed consequential differences in the kinds of things they wanted students to see. Here we describe these differences and discuss potential implications for practice. 

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2. Some Bridges Span More than Water: Engaging High School Java Learners with Data Structure Visualizations and Real-World Data

   https://doi.org/10.1145/3408877.3439558  

   Perry, Kathryn; Subramanian, Kalpathi; Saule, Erik (March 2021, Proceedings of the 52nd ACM Technical Symposium on Computer Science Education)

   Many high school mathematics teachers have stepped up to the charge of learning computer science and offering CS courses to their students. As CS grows in popularity, more students are completing AP CS A as sophomores or juniors, and looking for advanced opportunities while still in high school. Our project seeks to support high school teachers in their quests to meet students' needs for advanced CS coursework. I am one such teacher who faced that need, and was relieved to find the BRIDGES libraries and projects repository website for CS college professors. I began the work of adapting their data structures related projects for use in my courses. Solving Java programming challenges using BRIDGES libraries has helped my students visualize and program with one- and two-dimensional arrays and linked lists. In this talk, we encourage/recruit high school teachers to try our adapted-for-high-school BRIDGES materials, and share in the joy of cool visualizations that make data structures come alive. Using one sample project, I will show how an engaging problem, scaffolded learning materials, and dynamic visualizations converge to facilitate student understanding of, and programming agility with, two-dimensional arrays. 

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3. Leveraging multimodal classroom data for teacher reflection: teachers’ preferences, practices, and privacy considerations

   Yang, K; Borchers, C; Falhs, A-C; Echeverria, V; Karumbaiah, S; Rummel, N; Aleven, V (September 2024, Springer; European Conference on Technology Enhanced Learning, EC-TEL 2024)

   Ferreira_Mello, R; Rummel, N; Jivet; I; Pishtari, G; Ruipérez_Valiente, J A (Ed.)

   Teacher reflection is essential for K-12 classrooms, including effective and personalized instruction. Multimodal Learning Analytics (MMLA), integrating data from digital and physical learning environments, could support teacher reflection. Classroom data collected from sensors and TEL environments are needed to produce such analytics. These novel data collection methods pose an open challenge of how MMLA research practices can ensure alignment with teachers’ needs and concerns. This study explores K-12 teachers’ perceptions and preferences regarding MMLA analytics and data sharing. Through a mixed-method survey, we explore teachers’ (N=100) preferences for analytics that help them reflect on their teaching practices, their favored data collection modalities, and data-sharing preferences. Results indicate that teachers were most interested in student learning analytics and their interactions and ways of motivating students. However, they were also significantly less accepting of collecting students’ audio and position data compared to such data about themselves. Finally, teachers were less willing to share data about themselves than their students. Our findings contribute ethical, practical, and pedagogical considerations of MMLA analytics for teacher reflection, informing the research practices and development of MMLA within TEL. 

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4. Toward a Theoretical Framework for Data Fluency Teaching and Learning in Middle School STEM

   Wong, N; Elsayed, R; Perez, LR; Daehler, KR; Chen, P (March 2024, Ninety-seventh annual international conference of the National Association for Research in Science Teaching (NARST))

   This paper presents findings from a qualitative study of eleven experienced STEM educators who worked alongside developers to design and implement data-rich lessons in their grades 6–9 mathematics and science classrooms. In the context of a project that seeks to develop professional learning for data fluency, researchers documented the co-development process to articulate a model of what teachers need to know and be able to do in order to support their students’ data fluency. The project team distilled key findings into two framing documents: 1) a description of high-leverage areas of focus for PL which highlight challenges faced by teachers, which are common, important for data fluency, and represent opportunities for supporting teacher and student growth; and 2) a logic model that describes how the PL course under development is expected to influence teacher, classroom, and student outcomes. This paper contributes to the larger education community by defining the professional learning needs of educators who wish to integrate data into their STEM classrooms. These frameworks provide designers and researchers with touchpoints to structure and study PL experiences, lesson materials, and other classroom resources for both new and veteran educators. These tools can provide STEM teachers with guidance for reflecting on their current knowledge, skills, beliefs, and teaching practices that help their students become more data fluent. 

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5. Data Work in Education: Enacting and Negotiating Care and Control in Teachers' Use of Data-Driven Classroom Surveillance Technology

   https://doi.org/10.1145/3479596  

   Lu, Alex Jiahong; Dillahunt, Tawanna R.; Marcu, Gabriela; Ackerman, Mark S. (October 2021, Proceedings of the ACM on Human-Computer Interaction)

   Today, teachers have been increasingly relying on data-driven technologies to track and monitor student behavior data for classroom management. Drawing insights from interviews with 20 K--8 teachers, in this paper we unpack how teachers enacted both care and control through their data work in collecting, interpreting, and using student behavior data. In this process, teachers found themselves subject to surveilling gazes from parents, school administrators, and students. As a result, teachers had to manipulate the student behavior data to navigate the balance between presenting a professional image to surveillants and enacting care/control that they deemed appropriate. In this paper we locate two nuanced forms of teachers' data work that have been under-studied in CSCW: (1) data work as recontextualizing meanings and (2) data work as resisting surveillance. We discuss teachers' struggle over (in)visibility and their negotiation of autonomy and subjectivity in these two forms of data work. We highlight the importance of foregrounding and making space for informal data workers' (in our case, teachers') resistance and negotiation of autonomy in light of datafication. 

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