Exploring the Knowledge, Understanding, and Preparedness to Teach Students with Fetal Alcohol Spectrum Disorder (FASD) by Preservice Teachers in Ontario
Bibliographic record
Abstract
Fetal Alcohol Spectrum Disorder (FASD) is one of the most prevalent exceptionalities in Canadian classrooms, and one of the most challenging for teachers to support. Children affected by FASD exhibit a range of symptoms and various degrees of impairment, requiring multi-faceted supports. In teacher education programs, preservice teachers receive training through a combination of coursework and placements. Although courses offered to preservice teachers reflecting special education may be compulsory, explicit instruction on specific exceptionalities is limited. As studies have been completed in the past on teacher knowledge to support students with exceptionalities, there is a lack of research completed in Ontario reflecting how teachers are prepared to support students with FASD. If teachers are not prepared to teach students with FASD within the classroom, children with FASD may not receive the support they require. The objective of this study was to examine preservice teachers’ knowledge and understanding of FASD and their preparedness to teach these students. Both quantitative and qualitative data was collected through a 52-item online questionnaire to examine experiences supporting exceptionalities and FASD, knowledge of the condition, Teacher Sense of Efficacy (TSES), and preparedness to support students with FASD. Participants of this study (N = 80) were preservice teachers at a teacher education program accredited by the Ontario College of Teachers (OCT). It was observed that preservice teachers felt that they were not adequately trained to support students with FASD, and they did not discuss FASD as a topic of interest within their preservice teacher education programs. Experiences with exceptionalities was variable, and specific knowledge of needs of students with FASD was limited. Correlational analyses indicated that preservice teachers had low confidence of knowledge which was attributed to lack of awareness of the condition, lack of experiences supporting students with FASD, and lack of discussion of FASD and challenges faced by students with the condition. These findings suggest that FASD is a topic that should be further emphasized in preservice teacher education programs and challenges faced by students with the condition and strategies to support them must be discussed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".