‘Teacher data literacies practice’ meets ‘pedagogical documentation’: A scoping review
Bibliographic record
Abstract
Abstract In teacher education, there is a growing need for teachers to become data literate by collecting a variety of data on student learning to assess student progress and inform instruction. Research on pedagogical documentation in education, in particular early childhood education, has been undertaken to make students' learning visible by documenting multiple forms of student data. Although the notion of pedagogical documentation could be broadened in teacher data literacies practice, little is known about teacher data literacies practice in implementing pedagogical documentation. To fill this research gap, we performed a scoping review of the studies to investigate the landscape of teacher data literacies practice with pedagogical documentation published from 2000 to 2020. Our scoping review employed Arksey and O'Malley's methodological framework and identified 62 studies in our review. Our analysis provided an overview of the existing studies on teacher data literacies practice with pedagogical documentation. The implications of its findings were discussed. Context and implications Rationale for this study Despite the increased demand for teachers to make data‐driven and evidence‐based decisions in teaching, to our knowledge this is the first review of teacher data literacies in implementing pedagogical documentation. Why the new findings matter Our scoping review identifies knowledge gaps in teachers' pedagogical documentation in diverse K‐12 settings, particularly in developing countries. Implications for educational researchers and policy makers It also calls for more classroom‐based research on teacher data literacies practice in implementing pedagogical documentation and the need to further understand the relationship between teacher data literacies and pedagogical documentation. These implications are relevant for both educational researchers and teachers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.056 | 0.231 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.034 | 0.035 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".