Potential scoring and predictive bias in interim and summative writing assessments.
Bibliographic record
Abstract
Interim and summative assessments often are used to make decisions about student writing skills and needs for instruction, but the extent to which different raters and score types might introduce bias for some groups of students is largely unknown. To evaluate this possibility, we analyzed interim writing assessments and state summative test data for 2,621 students in Grades 3-11. Both teachers familiar with students and researchers unaware of students' identifying characteristics evaluated the interim assessments with analytic rubrics. Teachers assigned higher scores on the interim assessments than researchers. Female students had higher scores than males, and English learners (ELs), students eligible for free or reduced-price school lunch (FRL), and students eligible for special education (SPED) had lower scores than other students. These differences were smaller with researcher compared to teacher ratings. Across grade levels, interim assessment scores were similarly predictive of state rubric scores, scale scores, and proficiency designations across student groups. However, students identified as Hispanic, FRL, EL, or SPED had lower scale scores and a lower likelihood of reaching proficiency on the state exam. For this reason, these students' risk of unsuccessful performance on the state exam would be greater than predicted when based on interim assessment scores. These findings highlight the potential importance of masking student identities when evaluating writing to reduce scoring bias and suggest that the written composition portions of high-stakes writing examinations may be less biased against historically marginalized groups than the multiple choice portions of these exams. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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 | 0.285 | 0.559 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".