Making Sense of Sensemaking: Understanding How K–12 Teachers and Coaches React to Visual Analytics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With the spread of learning analytics (LA) dashboards in K--12 schools, educators are increasingly expected to make sense of data to inform instruction. However, numerous features of school settings, such as specialized vantage points of educators, may lead to different ways of looking at data. This observation motivates the need to carefully observe and account for the ways data sensemaking occurs, and how it may differ across K--12 professional roles. Our mixed-methods study reports on interviews and think-aloud sessions with middle-school mathematics teachers and instructional coaches from four districts in the United States. By exposing educators to an LA dashboard, we map their varied reactions to visual data and reveal prevalent sensemaking patterns. We find that emotional, analytical, and intentional responses inform educators’ sensemaking and that different roles at the school afford unique vantage points toward data. Based on these findings, we offer a typology for representing sensemaking in a K--12 school context and reflect on how to expand visual LA process models.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it