Bibliographic record
Abstract
Even those who agree with the idea of creating a monitoring system might still need to be convinced that what students have to say should be considered valuable input in the effort to improve schools, whether it pertains to raising academic performance or to safety, security, and behavior. Some argue that students are so disinterested in surveys that they answer randomly or give the first answer that comes to mind. Others are concerned that students respond deliberately in ways intended to harm staff members they do not like. Still others are not sure that students really understand the true meaning of the questions and, therefore, that their answers are not usable. Students, however, are often the best sources of providing detailed information on what is happening in schools and may even provide realistic suggestions on how adults can intervene. Looking at the ways students’ perceptions are already being used in schools can help policymakers and educators see how they can be part of improving school climate. This issue, for example, has been debated in recent years as some states and school districts have moved to include students’ opinions on their experiences in the classroom as one component of new teacher evaluation systems. For example, the Tripod survey,1 developed by Harvard University’s Ron Ferguson, asks students how much they agree with statements such as “My teacher explains diffcult things clearly” and “Our class stays busy and doesn’t waste time.” The Tripod was used as part of the Bill and Melinda Gates Foundation’s Measures of Effective Teaching project and is being used in districts across the United States, in Canada, and in China. In a 2013 report, Hanover Research reviewed the literature on using student perception surveys in teacher evaluation and professional development: “Given the consistent findings of the research reviewed for this report, it is reasonable to conclude that student perception surveys can provide accurate measures of teacher effectiveness,” they write. “When the proper instrument, or survey, is utilized, student feedback can be more accurate than alternative, more widely- used instruments at predicting achievement gains.
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 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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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".