Using a multi-dimensional assessment battery to screen for learning problems : an evaluation study in a sample of Canadian native Indian students
Bibliographic record
Abstract
This thesis is an analysis of test scores from a multi-dimensional assessment of Canadian Native Indian students attending an elementary school on a reserve in British Columbia. The intention of the assessment was to determine the incidence of learning problems among the students, and the special educational assistance required. The testing instruments used included the Metropolitan Reading Readiness Test; the Developmental Test of Visual-Motor Integration; the Peabody Picture Vocabulary Test; the Canadian Test of Basic Skills; and the Canadian Cognitive Abilities Test. Two tests of perceptual acuity were also administered. The single administration of the tests was designed to locate the level of achievement attained by the students and compare this attainment with age and grade placement at time of testing. The intention of the thesis was to determine the appropriateness of the battery of tests for this sample of Native Indian students. Disparities were found between placement and achievement with evidence of increasing spread in the upper grades. The average difference was approximately one year in grade 3, rising to two or more years in grades 5 and 6. The conclusion reached was that the instruments were useful in identifying the areas and extent of difference between the sample and the population. Specifically, vocabulary knowledge was low. Incidence of vision and hearing impairment was high; 40% of the students were found to have vision problems, and 21% were diagnosed as having hearing difficulty, 4 times the national average.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".