An Investigation of Educatorsâ Data Habit of Mind
Bibliographic record
Abstract
Educators are increasingly being asked to interact with data to facilitate studentsâ learning in the classroom. However, as an educational measurement community, we have little understanding of the factors and/or contexts that facilitate educatorsâ successful use of data. Educatorsâ use of score reports and the relationship to the intended use is integral to the concept of validity. A conceptual model, âData Habit of Mind,â is proposed to study educatorsâ understanding, interpretation and potential applications of results from large-scale assessments. The metaphor, âHabit of Mind,â was originally coined by Robert Sternberg and Dan Keating, and has been applied in the education sector to describe educatorsâ habits of inquiry when interacting with assessments. Based on an extensive review of the literature, Data Habit of Mind is defined as a combination of statistical literacy and score report interpretation. Statistical literacy is the extent to which an individual is able to describe, organize and reduce, represent, and analyze and interpret data. Score report interpretation is the extent to which an individual is able to describe, summarize, question, and propose an application for a given set of elements on a score report. The combination of these two makes up an individualâs Data Habit of Mind. \nTwenty educators were interviewed to assess their level of statistical literacy and their score report interpretation skills. A cognitive interview approach was used to capture the educatorsâ cognitive processes as they solved performance-based tasks, and protocol analysis procedures were used to encode the responses into the conceptual model. Descriptions of educatorsâ Data Habit of Mind were then generated through qualitative matrix analysis. Four groups of educators were identified based on the patterns of relationship between their statistical literacy and score report interpretation scores. Demographic factors, including teaching experience, gender and educational background were not meaningful predictors of educatorsâ Data Habit of Mind. These results add to our understanding of how educators interpret and use test results and have implications for test validation processes.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".