The Personal Program Plan (PPP) Reviewed: A Saskatchewan Perspective
Bibliographic record
Abstract
Every country and corresponding educational systems have unique ways of administering and delivering services for students deemed to have special educational needs.Unlike our American neighbors, Canada relies on individual provinces and territories to assume their own legislative authority and develop appropriate laws, policies and programs to educate all of its students.Anchored in our Charter of Rights and Freedoms each jurisdiction develops educational systems that are inclusive in nature and consistent with the right of all of its citizens to access publically funded schools.This paper outlines one school division's attempt to pilot an innovative new Personal Program Plan (PPP) design in a province with a rich history of addressing the special needs of its students.A Masters of Education student in the Educational Psychology and Special Education Department of the University of Saskatchewan's College of Education decided to focus on her home school division's pilot project.Heather Hayes utilized a semi structured questionnaire to determine how six Educational Support Teachers (ESTs) responded to this new PPP template during the 2010-2011 school year.The template employed an Understanding by Design (UbD) format and utilized Performance Tasks (PTs) to demonstrate students' understandings.Recommendations for refinement and revision emerged from the study.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".