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ClassInSight: Designing Conversation Support Tools to Visualize Classroom Discussion for Personalized Teacher Professional Development

Tricia J. Ngoon, S Sushil, Angela Stewart, Ung-Sang Lee, Saranya Venkatraman, Neil Thawani, Prasenjit Mitra, Sherice Clarke, John Zimmerman, Amy Ogan

TL;DR

ClassInSight, a tool that visualizes three levels of teachers’ discussion data and structures reflection, is presented and guidelines for these conversational support tools to support personalized PD in professions beyond teaching where conversation and interaction are important are discussed.

Abstract

Teaching is one of many professions for which personalized feedback and reflection can help improve dialogue and discussion between the professional and those they serve. However, professional development (PD) is often impersonal as human observation is labor-intensive. Data-driven PD tools in teaching are of growing interest, but open questions about how professionals engage with their data in practice remain. In this paper, we present ClassInSight, a tool that visualizes three levels of teachers' discussion data and structures reflection. Through 22 reflection sessions and interviews with 5 high school science teachers, we found themes related to dissonance, contextualization, and sustainability in how teachers engaged with their data in the tool and in how their professional vision, the use of professional expertise to interpret events, shifted over time. We discuss guidelines for these conversational support tools to support personalized PD in professions beyond teaching where conversation and interaction are important.

ClassInSight: Designing Conversation Support Tools to Visualize Classroom Discussion for Personalized Teacher Professional Development

TL;DR

ClassInSight, a tool that visualizes three levels of teachers’ discussion data and structures reflection, is presented and guidelines for these conversational support tools to support personalized PD in professions beyond teaching where conversation and interaction are important are discussed.

Abstract

Teaching is one of many professions for which personalized feedback and reflection can help improve dialogue and discussion between the professional and those they serve. However, professional development (PD) is often impersonal as human observation is labor-intensive. Data-driven PD tools in teaching are of growing interest, but open questions about how professionals engage with their data in practice remain. In this paper, we present ClassInSight, a tool that visualizes three levels of teachers' discussion data and structures reflection. Through 22 reflection sessions and interviews with 5 high school science teachers, we found themes related to dissonance, contextualization, and sustainability in how teachers engaged with their data in the tool and in how their professional vision, the use of professional expertise to interpret events, shifted over time. We discuss guidelines for these conversational support tools to support personalized PD in professions beyond teaching where conversation and interaction are important.
Paper Structure (46 sections, 3 figures, 1 table)

This paper contains 46 sections, 3 figures, 1 table.

Figures (3)

  • Figure 1: An overview of the ClassInSight interface: a) The legend with talk codes, color coding, and description, b) The miniview showing the Turn-Taking visualization as a tree visualization, c) The Transcript visualization shows the full transcript of dialogue, d) the schema that guides reflection during collaborative reflection sessions.
  • Figure 2: The Talk Ratio visualization, which shows the overall classroom discourse in terms of talk category. The vertical line separates the percentage of teacher talk versus student talk. Below the visualization is a breakdown of each talk move by percentage. Clicking on any talk move shows excerpts from the transcript if available.
  • Figure 3: The Turn-Taking visualization shows the rhythm or cadence of the discussion with teacher talk on the left and student talk on the right. Each bar represents a piece of dialogue and is proportional with the length of dialogue. The colors of the bars represent the applicable talk to that specific piece of dialogue. A legend on the left displays the code labels and colors.