TALK TO LEARN: THE IMPACT OF INCORPORATING DIFFERENT TYPES OF TALK IN A LANGUAGE IMPAIRED-INTENSIVE SUPPORT PROGRAMMING CLASSROOM
Bibliographic record
Abstract
TALK TO LEARN: THE IMPACT OF INCORPORATING DIFFERENT TYPES OF TALK IN A LANGUAGE IMPAIRED-INTENSIVE SUPPORT PROGRAMMING CLASSROOMDoctor of Education 2024 Katharine Piotrowski Department of Social Justice Education University of Toronto Abstract Grounded in Lev Vygotsky’s theory of child development as influenced by social interaction, this study investigated the connection between exploratory and presentation talk and learning in the Language Impairment-Intensive Support Programming (LI-ISP) elementary classroom in Ontario during the 2021-2022 school year. Specifically, this study investigated the impact of the TALK To LEARN Program, a program I designed to focus on talk-centered tasks, on the oral communication and presentation skills of students with language impairment. It also aimed to identify any further areas of growth that may have resulted from the students’ participation in the TALK To LEARN Program. Qualitative data was collected in the form of researcher observation notes, student presentations and reflection tasks as well as parent and teacher interviews. Findings of the study revealed positive changes in oral communication, increased overall student engagement in school, increased demonstration of independence and responsibility as well as increased confidence for the students who participated in the TALK To LEARN Program. Based on these findings, the TALK To LEARN Program supports the research highlighting the importance of empowering students with language impairment to optimize their growth both socially and academically through developing their communication skills and building on the power of talk.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".