Practice Activity: Examining Your Monitoring Data
Lead data dialogues


Leading a data dialogue effectively requires a focus, data, guiding questions, and an understanding of the collaborative inquiry process. Data-driven dialogue assists teams in making shared meaning of data, in surfacing multiple perspectives, in separating data from inference, and in making data-driven decisions. Though the data is key to the dialogue, the process of collaborative inquiry drives the results. When leading the data dialogue, you will find the Seven Norms of Collaborative Work, developed by Laura Lipton and Bruce Welman, to be helpful to the process. Lipton and Wellman also advise “allowing adequate time to explore assumptions, predictions, questions, and observations before offering explanations or solutions. In doing so, groups not only reach sounder conclusions but also build their capacity to inquire and learn together.”

Good data-driven dialogue leads to data-driven decisions. If you have organized ongoing data dialogues among stakeholders, it is much more likely that they will feel ownership for the data-decisions you collectively make.

It is important that group leaders develop skills in managing, modeling, mediating, and monitoring effective group process and that groups develop strong collaborative norms. The following guidelines will assist the facilitator in having a productive dialogue.


  • Choose a focus for the discussion.
    .For example
    .What does the data tell us about our student’s performance on drawing inferences?
  • Choose a format for the data that is easy to read.  If teachers are bringing their own classroom data, decide on a common format.
  • Set ground rules for the discussion and enforce them.
  • Model collaborative group norms.
  • Provide adequate time for dialogue.  Otherwise, you will have difficulty reaching common understanding.
  • Keep the focus on improvement, not on blame.
  • Guard against early conclusions of why the data look like they do. Instead, focus your discussion on identifying the questions the data raise.
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