Bibliographic record
Abstract
This paper uses a new administrative dataset of students at a large university matched to courses and instructors to analyze the importance of teacher quality at the postsecondary level.Instructors are matched to both objective and subjective characteristics of teacher quality to estimate the impact of rank, salary, and perceived effectiveness on grade, dropout and subject interest outcomes.Student fixed effects, time of day and week controls, and the fact that first year students have little information about instructors when choosing courses helps minimize selection biases.We also estimate each instructor's value added and the variance of these effects to determine the extent to which any teacher difference matters to short-term academic outcomes.The findings suggest that subjective teacher evaluations perform well in reflecting an instructor's influence on students while objective characteristics such as rank and salary do not.Whether an instructor teaches full-time or part-time, does research, has tenure, or is highly paid has no influence on a college student's grade, likelihood of dropping a course or taking more subsequent courses in the same subject.However, replacing one instructor with another ranked one standard deviation higher in perceived effectiveness increases average grades by 0.5 percentage points, decreases the likelihood of dropping a class by 1.3 percentage points and increases in the number of same-subject courses taken in second and third year by about 4 percent.The overall importance of instructor differences at the university level is smaller than that implied in earlier research at the elementary and secondary school level, but important outliers exist.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".