Administration and Scoring Errors of Graduate Students Learning the WISC-IV
Bibliographic record
Abstract
A total of 19 graduate students enrolled in a graduate course conducted 6 consecutive administrations of the Wechsler Intelligence Scale for Children, 4th edition (WISC-IV, Canadian version). Test protocols were examined to obtain data describing the frequency of examiner errors, including administration and scoring errors. Results identified 511 errors on 94% of protocols with a mean of 4.48 errors per protocol. The most common errors were identified on the Vocabulary, Similarities, and Comprehension subtests, which comprised 80% of all errors. A repeated-measures ANOVA (analysis of variance) was not significant across six administrations, F(5, 90) = 1.609, p = .166, eta 2 = .082, although there was a trend in the data for a reduced number of errors with successive administrations. Results were consistent with other studies that have determined graduate student administration and scoring errors do not improve with repeated administrations. Implications and recommendations to reduce administration and scoring errors among graduate students were discussed.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".