Learning to Consult and Collaborate in the High School: A Two-Year Study of Perceptions from University Student Team Members
Bibliographic record
Abstract
Collaboration in the schools is an important intervention for providing services to students and staff members. The majority of studies regarding collaborative consultation between school psychologists and teachers have been conducted at the elementary and middle school levels. Further, little has been written about teaching collaborative consultation at the university level to preservice educators.This article describes a two-year project designed to teach collaboration at the university level to two groups of high school teacher interns and school psychology students enrolled in separate courses. Teacher trainees identified problems within their classrooms with which they needed assistance and school psychology students collaborated with them to find appropriate interventions. The process was examined at the end of each spring semester for two years through focus groups led by the two university professors. Analysis of the group interactions indicated similarities and differences between the two student groups. First-year students spent a great deal of time and energy establishing a relationship with their partners and supporting this positive interaction, however, specific, practical interventions were neglected. Students in the second-year group were better able to collaborate on the implementation of actual interventions. This was attributed to additional structure and accountability measures added to the courses. Recommendations for future collaboration training experiences are outlined by the authors.
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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".