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Classroom orchestration requires teachers to concurrently manage multiple activities across multiple social levels (individual, group, and class) and under various constraints. Real-time dashboards can support teachers; however, designing actionable dashboards is a huge challenge. This paper describes a participatory design study to identify and inform critical features of a dashboard for displaying relevant, actionable, real-time data. We leveraged a Sense-Assess-Act framework to present dashboard mockups to teachers for feedback. Although the participating teachers differed in how they would use the presented information (during class or after class as a post hoc analysis tool), two common emerging themes were that they wanted to use the data to a) better support their students and b) to make broader instructional decisions. We present data from our study and propose a customizable, mobile dashboard, that can be adapted to a teacher's specific needs at a specific time, to help them better facilitate learning activities. 

Classroom orchestration is a multifaceted pedagogical challenge, requiring teachers to simultaneously manage activities across multiple social levels and under various constraints. Teacher dashboards are commonly developed tools to aid orchestration; however, many fall short in real-time classrooms. To address this impediment, we used participatory design sessions with teachers to better understand their needs, based on which, we plan to build a dynamic dashboard with real-time actionable metrics. 

This is a contribution to a Symposium This symposium will provide opportunities for discussion about how Artificial Intelligence can support ambitious learning practices in CSCL. To the extent that CSCL can be a lever for educational equitable educational change, AI needs to be able to support the kinds of practices that afford agency to students and teachers. However, AI also brings to the fore the need to consider equity and ethics. This interactive session will provide opportunities to discuss these issues in the context of the examples presented here. Our contribution is focused on two participatory design studies we conducted with 14 teachers to understand the kinds of automatic feedback they thought would support their students’ science explanation writing as well as how they would like summaries of information from students’ writing presented in a teacher’s dashboard. We also discuss how we developed our system, PyrEval, for automated writing support based on historical data and scoring from manual coding rubrics. 

Science writing skills depend on a student’s ability to co-ordinate conceptual understanding of science with the ability to articulate ideas independently, and to distinguish between gradations of importance in ideas. Real-time scaffolding of student writing during and immediately after the writing process could ease the cognitive burden of learning to co-ordinate these skills and enhance student learning of science. This paper presents a design process for automated support of real-time scaffolding of middle school students’ science explanations. We describe our adaptation of an existing tool for automatic content assessment to align more closely with a rubric, and our reliance on data mining of historical examples of middle school science writing. On a reserved test set of semi-synthetic examples of science explanations, the modified tool demonstrated high correlation with the manual rubric. We conclude the tool can support a wide range of design options for customized student feedback in real time. 

Software projects produce large quantities of data such as feature requests, requirements, design artifacts, source code, tests, safety cases, release plans, and bug reports. If leveraged effectively, this data can be used to provide project intelligence that supports diverse software engineering activities such as release planning, impact analysis, and software analytics. However, project stakeholders often lack skills to formulate complex queries needed to retrieve, manipulate, and display the data in meaningful ways. To address these challenges we introduce TiQi, a natural language interface, which allows users to express software-related queries verbally or written in natural language. TiQi is a web-based tool. It visualizes available project data as a prompt to the user, accepts Natural Language (NL) queries, transforms those queries into SQL, and then executes the queries against a centralized or distributed database. Raw data is stored either directly in the database or retrieved dynamically at runtime from case tools and repositories such as Github and Jira. The transformed query is visualized back to the user as SQL and augmented UML, and raw data results are returned. Our tool demo can be found on YouTube at the following link:http://tinyurl.com/TIQIDemo.