Creating Data-Driven Reteaching Groups

Teaching something does not guarantee that students learn the material. As student-learning data become available, data-driven educators reflect on those results and consider if, when, and how they should adjust instruction.
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About this Micro-credential

Key Method

When considering reteaching, one must determine when, how, and with whom reteaching should occur. A key decision is whether reteaching should occur as a whole group, in small groups, or individually. This step in data-driven instruction comes before determining content will actually be retaught. Using analyzed data, the instructor contemplates student performance, decides whether standards should be retaught, and if so, proposes whether the reteaching should happen with the whole group, with small-groups of students, or with individuals.

Method Components

There are five primary components to creating data-driven reteaching groups:

1. Conduct a high-level analysis of the student performance data.

At a minimum, the educator calculates the average student performance on each standard. Additionally, he or she should consider any limitations to the validity of the inference they make about average student achievement, given the data.

2. Based on that analysis, identify any standards that should likely be retaught with the whole group.

The educator uses the data to provide rationale for which standards, if any, should be retaught to the whole class.

3. Based on the data, identify small groups for reteaching.

The educator uses the data to provide rationale for which standards should be retaught to small groups of students and decide who should be in each group and on which standards they should focus. It is important to describe the process used to arrive at the conclusions.

4. Based on the data, identify students who should be retaught individually.

The educator uses the data to provide rationale for selecting particular students for whom individualized reteaching is likely to be most effective given the time-intensive nature of this intervention.

5. Identify other data that could improve reteaching decisions.

Data-literate educators see assessment data as only part of a spectrum of data that can be used to make instructional decisions. Other data could include attendance data, data on student motivation, behavioral data, etc.

Research & Resources

Supporting Research

  • Mason, D., and Good, T. 1993. “Effects of Two-Group and Whole-Class Teaching on Regrouped Elementary Students’ Mathematical Achievement.” American Educational Research Journal, 30, 328–360.

Resources

  • Bambrick-Santoyo, Paul. 2010. Driven by Data: A Practical Guide to Improve Instruction. Jossey-Bass, San Francisco.
    Bambrick-Santoyo, Paul. 2012. Leverage Leadership: A Practical Guide to Building Exceptional Schools. Jossey-Bass, San Francisco.
  • Good, T., and Brophy, J. 2008. Looking in Classrooms, 10th Edition. Pearson, New York.

Submission Requirements

Submission Guidelines & Evaluation Criteria

To earn the micro-credential for Creating Data-Driven Reteaching Groups, use the dataset at http://bit.ly/1J8JsIX to complete the steps outlined below. Please assume that each of the items from the associated assessment is similar in structure, content, and rigor. This assumption allows you to complete the activity without having a test in hand or being a subject-specific expert in the underlying content. It should be emphasized that before actually reteaching, one would want to question the initial classroom instruction and think about what could be done differently in the reteaching to better help learners.

Each component of your artifact submission will be assessed based on the rubrics that follow each question. You must earn a (3) Proficient or (4) Exemplary score on each portion of the submission in order to earn the micro-credential.

Part 1. High-level analysis

  1. How did the class as a whole perform on each of the seven math standards?
  2. Given the data, on which standards should one be least confident in the inference about average student performance? Why?

Part 2. Grouping questions

  1. Given the data, which standards, if any, should be retaught to the whole group? Why?
  2. Given the data, which standards, if any, should be retaught in small groups? Please describe who would be in each group and which standards one would prioritize when reteaching.
  3. Given the data, which students should be retaught individually? Why? On which standards should they focus?
  4. What other data, beyond what was provided, would be helpful in making the ultimate decision about how to group students to reteach these standards?

Except where otherwise noted, this work is licensed under:
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
http://creativecommons.org/licenses/by-nc-nd/4.0/

Requirements

Download to access the requirements and scoring guide for this micro-credential.
How to prepare for and earn this micro-credential - in a downloadable PDF document

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