Identification of Students with Academic Difficnlties: Implications for Research and Practice
Bibliographic record
Abstract
Many students with academic difficulties are not identified by schools as needing assistance. The traditional learning disabilities category appears to be less and less suitable given recent develop-ments in special education that favor inclusion. Thus it is necessary to rethink procedures to identify students needing support and the implications of this for research and practice. This re-search was designed to examine the relationship between three procedures to identify students with academic difficulties in Quebec schools: teacher ratings, curriculum-based achievement tests, and identification of LD (learning disabilities) by the school. Results reveal that agreement between school identification and teacher rating is low. This agreement increased with subjects who had the lowest achievement test results. On the other hand, school identification and achievement scores are moderately associated. Finally, although achievement scores and teacher ratings are significantly correlated, only 50 % of students with the lowest achievement test results are recognized by their teachers as having academic difficulties. The article concludes that further research is necessary to conceptualize in a new way the identification of students who need academic support. Plusieurs élèves éprouvant des difficultés scolaires ne sont pas reconnus par l’école comme ayant
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 | 0.188 | 0.291 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".