Can the WRAT-4 Math Computation Subtest Predict Final Grade in College-Level Science
Bibliographic record
Abstract
Seventy-one, freshman through senior undergraduate college students 28 males and 43 females, M age =22.34 yr., SD = 4.20 in 5 different science classes were administered the Wide Range Achievement Test Fourth Edition (WRAT-4) Math Computation Subtest. Predictive validity coefficients were calculated relative to the criterion of the final class grade. The validity coefficient for the pre-course WRAT score was statistically significant. The WRAT-4 Math subtest can be used by instructors to examine performance on specific items to judge the appropriateness of a student’s placement in either entry-level or advanced science courses. However, high school grades are also a good predictor of completing the college curriculum and should be used along with math computation skills scores. Also motivation to complete college level science courses and socioeconomic status may be covariates in predicting college science final grade and eventual graduation from college.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".