Effective Collaboration between Teachers and Parents: Discovering how to unlock Literacy and Language Abilities in students with Down Syndrome
Bibliographic record
Abstract
This research project investigates how a classroom teacher and a parent view and implement literacy/language programming for students with Down syndrome (DS). Through in-depth interviews with both participants, this qualitative research study examines the perspectives and practices of an active teacher and parent. The findings are presented as two case studies. A cross-case is also presented that connects to the findings to relevant literature. The case studies examine the participants’ perspectives on literacy and language development; the implementation of a literacy and language development program; what effective collaboration between teachers and parents looks like; and what can be done moving into the future. The teacher and parent participant provide valuable insights into what effective collaboration can look like and how it can positively affect the literacy/language development of students with DS. There is a need for further development of both the programming for students with DS, as well as the communication that takes place between home and school/parents and teachers. By examining the perspectives of a teacher and a parent, in connection to the literacy and language development of students with DS, the importance of communication and collaboration between these two groups is apparent.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".