Feedback with Children: Exploring a Framework for Providing Psychoeducational Assessment Results in Schools
Bibliographic record
Abstract
While assessment of learning difficulties has been the subject of substantial research in school psychology, there has been almost no parallel literature describing techniques for communicating results of assessments, particularly to the young children we see in schools. This is unfortunate, given that sharing assessment results with children, especially in a way that is compelling and positive, is a difficult task, exacerbated by limited time and often, uncertainty about what to say. Therefore, over the course of one year, I set out to explore the Brain Building Feedback Framework by Dr. Liz Angoff (2021), a newly published approach for giving assessment feedback to children. I interviewed a small group of school psychologists and children about their experiences of participating in feedback sessions using this specific framework, drawn from elementary schools of the large Ontario school board where I work. The aim was to understand what happened in these sessions that helped children with learning difficulties understand their assessment results. Through reflexive thematic analysis, three main themes (and three sub-themes) which point to the helpful aspects of the feedback experience were developed. The main themes include a sense of collaboration between psychologist and child, which creates the frame for the what, how, and when of communication in feedback, that results in potentially transformative conceptions for both children and psychologists. The implications of these findings can inspire (1) insights into why giving feedback directly to children is important and (2) ideas about how we might effectively do so in school psychology practice.
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 | 0.092 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.017 | 0.047 |
| Scholarly communication | 0.021 | 0.019 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.007 | 0.009 |
| 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".