Bibliographic record
Abstract
Using assessment data is a complex process (Wayman & Jimerson, 2014) that requires teachers have necessary skills to access, understand, analyze, and utilize student and school-level data to increase student learning. Educators need intensive professional development to acquire expertise to use continuous assessment data effectively in response to individual learning needs (Campbell & Levin, 2009; Fullan & al, 2006). Schools overlook opportunities to integrate data from assessments into instructional application in classrooms (Means & al, 2009). This presentation introduces the Data Literacy Learning Model (DLLM) piloted in this qualitative study. The DLLM was developed to build educator capacity to translate data into instructional practice that positively impacted student learning. The DLLM was revised in a K-5 school in collaboration with teachers and administration working with student, class, and school data to provide timely, continuous, and tiered responses to each child’s ongoing learning needs in early literacy. The collaborative, job-embedded, professional learning focused on educators using skills in the DLLM to transfer assessment data to instructional practice. The DLLM contributed to an increased used of assessment data and confidence using data to target student learning needs and was unanimously accepted as a strong model to develop data literacy for educators.
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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 | 0.029 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".