94 Using Research Priority Setting to Investigate Special Education in British Columbia
Bibliographic record
Abstract
Abstract Background In a recent school year, nearly 58,000 (10.5%) Kindergarten-Grade 12 students in British Columbia had a special needs designation, according to the BC Ministry of Education (BC MED) Student Statistics 2015/2016. Despite these considerable numbers, we know little about the educational journeys of students with special needs. To help set a research agenda in this area, we undertook a Research Priority Setting (RPS) study, which is an approach used in knowledge translation and exchange research. RPS is a relatively new approach, particularly in educational research, and is used to involve knowledge users early in the research process. It can include a diverse range of methods including surveys, workshops, and Delphi studies. As part of a larger research program to investigate the population-level educational journeys of students with special needs, we employed RPS. Objectives The purpose of this study was to explore educational leaders’ perspectives on research priorities in special education. The specific research questions were: 1. Given a pre-determined set of research areas based upon available population-level administrative data, what are educational leaders’ research priorities? 2. Which specific special education student groups (here, called designations) are educational leaders most interested in learning about through population-based research? 3. Beyond the available administrative data, what additional population-level research priorities do educational leaders identify? Design/Methods We employed a cross-sectional survey design. In December, 2017, we invited public school districts to participate in a survey we created to help us identify research priorities related to the educational journeys of students with special needs and disabilities. The specific needs the BC MED routinely tracks are presented with their respective designation codes in Table 1. The survey invited participants to rank eight specific research areas in order of perceived importance. Then, they were asked to indicate which of the 12 special needs designations were of most interest in relation to the research areas. Finally, the survey included an open-ended question inviting participants to suggest further areas for research. Quantitative results were analyzed using descriptive analyses, including frequency tables, cross-tabulations, and histograms. Qualitative data from the open-ended question was analyzed using content analysis. Results We asked a wide range of education professionals to complete this survey, including: district administrators, district learning support service providers, and school staff. In total, 71 participants volunteered to complete our survey, representing 43 of BC’s 60 public school districts. The average participant had: a master’s degree; a district administrator position; and between 20 and 29 years of educational experience. The majority of participants were experienced in special education. Overall, survey participants agreed on three specific priorities for future research on the educational journeys of students with special needs and disabilities: We then used approved BC MED data to prepare district-specific reports for each of these three topics, with province-wide data included for comparison. In each report, we break down students’ results by their BC MED special needs designation. Conclusion This study is an important step forward in our knowledge about the educational journeys of students with special needs. The results can be useful in guiding policy and program development, both here in British Columbia and beyond. Healthcare providers in pediatrics are in key roles to advocate for supports and resources for this population on their educational journeys.
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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 | 0.063 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".