Session: Dashboards and Knowledge representation

Vincent Aleven[1], Jori Blankestijn[2], LuEttaMae Lawrence[3], Tomohiro Nagashima[1] and Niels Taatgen[2]
[1] Carnegie Mellon University, USA
[2] University of Groningen, Groningen, The Netherlands
[3] Utah State University, USA

Abstract: Past research has yielded ample knowledge regarding the design of analytics-based tools for teachers and has found beneficial effects of several tools on teaching and learning. Yet there is relatively little knowledge regarding the design of tools that support teachers when a class of students uses AI-based tutoring software for self-paced learning. To address this challenge, we conducted design-based research with 20 middle school teachers to create a novel real-time dashboard, Tutti, that helps a teacher monitor a class and decide which individual students to help, based on analytics from students’ tutoring software. Tutti is fully implemented and has been honed through prototyping and log replay sessions. A partial implementation was piloted in remote classrooms. Key design features are a two-screen design with (1) a class overview screen showing the status of each student as well as notifications of recent events, and (2) a deep dive screen to explore an individual student’s work in detail, with both dynamic replay and an interactive annotated solution view. The project yields new insight into effective designs for a real-time analytics-based tool that may guide the design of other tools for K-12 teachers to support students in self-paced learning activities.

📄 Read More: https://link.springer.com/chapter/10.1007/978-3-031-16290-9_2

Katia Oliver-Quelennec[1,2], François Bouchet[1], Thibault Carron[1], Kathy Fronton Casalino[2] and Claire Pinçon[2]
[1] Sorbonne Université, France
[2] Univ. Lille, France

Abstract: earning Analytics Dashboards (LADs) are becoming a key element in enabling learners to monitor their learning, plan and actually learn. However, LADs are sometimes not completely adapted to students, who are rarely involved in their design. Moreover, even when they are, the implemented LADs are often the same for all students, whereas previous works have shown the value of adapted LADs. Here we investigate which adaptations are requested by students, and attempt to identify which data and visualizations are suitable depending on the student’s profile. More specifically, we consider dynamic profiles as students’ expectations can vary over the course duration. By using LADs co-design sessions both online and on-site, we collected needs from N = 386 university students from different disciplines and degree level, split in 108 groups (2 to 4 students). After a manual annotation, we identified a total of 54 types of data and indicators, divided into 12 thematics. Our first analysis confirmed some previous results, particularly on the use of peer comparisons that do not fulfill every student’s needs. And we noticed other expectations according to the student’s learning context or the academic period. Future work will benefit from these results to define a model of adapted LADs.

📄 Read More: https://link.springer.com/chapter/10.1007/978-3-031-16290-9_22

Marco Kragten[1], Monique Pijls[1], Emile Jaspar[1], Malou Sprinkhuizen[1] and Bert Bredeweg[1,2]
[1] Amsterdam University of Applied Sciences, The Netherlands
[2] University of Amsterdam, The Netherlands

Abstract: We investigate a computer supported approach in which pairs co-construct a qualitative representation of the dynamics of the industrial revolution in a shared workspace. A key feature of this approach concerns the use of a meta-vocabulary for representing cause-and-effect relationships that facilitates the use of a predefined norm-representation to automatically steer the collaborative learning process. In particular, it provides focus on the set of ingredients that the learners should use. Additionally, the workspace offers each learner pair information about progress and content-related support. An evaluation study was executed in a real classroom. A workbook provided information for constructing the representation and gave advise on how to approach this task together. However, most pairs took an alternative approach and divided their actions in the shared workspace in an unbalanced way. Three types of task division occurred that showed differences in the number of errors and the number of requests for support. From this result, we formulate future directions for the development of a pedagogical approach that stimulates collaborative learning with qualitative representations and the support offered by the software.

📄 Read More: https://link.springer.com/chapter/10.1007/978-3-031-16290-9_12