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A Dashboard to Support Teachers During Students’ Self-paced AI-Supported Problem-Solving Practice

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Book cover Educating for a New Future: Making Sense of Technology-Enhanced Learning Adoption (EC-TEL 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13450))

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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.

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Acknowledgments

The research was supported by US Dept. of Education (IES) grant R305A180301 and NSF (IIS) grant 1822861 We gratefully acknowledge their contributions. Opinions expressed in the paper are those of the authors, not the sponsor.

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Authors and Affiliations

  1. Human-Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, USA

    Vincent Aleven & Tomohiro Nagashima

  2. Computer Science and Artificial Intelligence, Bernoulli Institute for Mathematics, University of Groningen, Groningen, The Netherlands

    Jori Blankestijn & Niels Taatgen

  3. Instructional Technologies and Learning Sciences Department, Utah State University, Logan, USA

    LuEttaMae Lawrence

Authors
  1. Vincent Aleven
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  2. Jori Blankestijn
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  3. LuEttaMae Lawrence
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  4. Tomohiro Nagashima
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  5. Niels Taatgen
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Corresponding author

Correspondence to Vincent Aleven .

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Editors and Affiliations

  1. Pontificia Universidad Católica de Chile, Santiago, Chile

    Isabel Hilliger

  2. Universidad Carlos III de Madrid, Madrid, Spain

    Pedro J. Muñoz-Merino

  3. KU Leuven, Leuven, Belgium

    Tinne De Laet

  4. Universidad de Valladolid, Valladolid, Spain

    Alejandro Ortega-Arranz

  5. The Open University, Milton Keynes, UK

    Tracie Farrell

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Aleven, V., Blankestijn, J., Lawrence, L., Nagashima, T., Taatgen, N. (2022). A Dashboard to Support Teachers During Students’ Self-paced AI-Supported Problem-Solving Practice. In: Hilliger, I., Muñoz-Merino, P.J., De Laet, T., Ortega-Arranz, A., Farrell, T. (eds) Educating for a New Future: Making Sense of Technology-Enhanced Learning Adoption. EC-TEL 2022. Lecture Notes in Computer Science, vol 13450. Springer, Cham. https://doi.org/10.1007/978-3-031-16290-9_2

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  • DOI: https://doi.org/10.1007/978-3-031-16290-9_2

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