The Impact of Professional Development on Student Achievement As Measured by Math and Science Curriculum-based Assessments
Bibliographic record
Abstract
The purpose of this study was to explore the impact of teacher professional development on student achievement measured by scores on curriculum-based assessments, CBAs. The participants in the study included 260 3rd, 4th, and 5th grade math and science teachers. Teacher participation in professional development courses was collected for curriculum, instruction, differentiation, assessment, technology integration, and continuous improvement credit types. Achievement data for 8,454 students was used: 2,883 in 3rd grade, 2,752 in 4th grade, and 2,819 in 5th grade. The dependent variable of student achievement was dichotomized at the median: half of the student participants scored above the median and half of the students scored at and below the median. A series of logistic regression models were fit to the data that included examining all main effects and interaction terms among all variables to determine the best fitting model. The results of this study indicate that for 4th grade science, teacher professional development participation in curriculum, instruction, and differentiation credit strands increased the chances for students to score above the district median on CBAs. The larger number of professional development hours in a variety of credit strands had a negative impact on student achievement in 4th grade science. In 5th grade science, the students whose teacher spent more hours in professional learning for continuous improvement had an increased likelihood of scoring above the district median on CBAs.
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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 | 0.005 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".