If You Build It Will They Come? Teacher Use of Student Performance Data on a Web-Based Tool
Bibliographic record
Abstract
The past decade has seen increased testing of students and the concomitant proliferation of computer-based systems to store, manage, analyze, and report the data that comes from these tests. The research to date on teacher use of these data has mostly been qualitative and has mostly focused on the conditions that are necessary (but not necessarily sufficient) for effective use of data by teachers. Absent from the research base in this area is objective information on how much and in what ways teachers actually use student test data, even when supposed precursors of teacher data use are in place. This paper addresses this knowledge gap by analyzing usage data generated when teachers in one mid-size urban district log onto the web-based, district-provided data deliver and analytic tool. Based on information contained in the universe of web logs from the 2008-2009 and 2009-2010 school years, I find relatively low levels of teacher interaction with pages on the web tool that contain student test information that could potentially inform practice. I also find no evidence that teacher usage of web-based student data is related student achievement, but there is reason to believe these estimates are downwardly biased.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".