The Relationship Between Teachers’ Inclusion-Related Knowledge, Skills, and Attitudes and Student Outcomes: A Review of Recent Literature
Bibliographic record
Abstract
In this article, we report on a scoping review of empirical literature addressing the relationship between teachers’ inclusion-related knowledge, skills, and attitudes and student outcomes. Using six common electronic databases for education (ProQuest, JSTOR, SAGE Journals Online, ScienceDirect, PsycINFO, PsycARTICLES), we searched for peer-reviewed, English-language publications between 1 January 2008 and 1 January 2018 (a 10-year period). A total of 25 articles met the search criteria for this scoping review and were consequently subject to a more detailed examination. This more detailed examination focused upon five student variables (intellectual disabilities [IDs], learning disabilities [LDs], autism spectrum disorder [ASD], other mental health disorders, and giftedness) and five possible student outcomes (social well-being, functional or life skills, academic achievement, post-secondary transitions, and exceptionality-related outcomes). These 25 articles include eight systematic or scoping reviews, two that report on experimental studies, nine that report on quasi-experimental studies, and six that report on correlational-descriptive research studies. A summary account of these 25 articles is offered, as is a list of related implications, for both practice and inquiry. Some of the most significant implications are related to the limited body of evidence related to a number of variables; these include teachers’ attitudes and resultant student outcomes, giftedness, and post-secondary transitions. Additionally, these implications also offer cautionary considerations related to teachers delivering mental health-related programs and utilizing technology-related interventions for students with ASD.
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 | 0.018 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.025 | 0.029 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".