Teacher Practices, Peer Dynamics, and Academic Enablers: A Pilot Study Exploring Direct and Indirect Effects Among Children at Risk for ADHD and Their Classmates
Bibliographic record
Abstract
Establishing a positive peer climate in elementary school classrooms is an important goal for educators because peer dynamics are thought to affect academic learning. Thus, it is important to (a) understand the relationship between children's peer dynamics and academic functioning, and (b) identify teacher practices that influence both peer processes and academic outcomes. In this pilot study, we explored whether specific teacher strategies that promote positive behaviors in children and positive peer dynamics influence children's better academic enablers, as well as whether they do so indirectly via improving peer sociometric ratings. Such teacher strategies may be particularly relevant for supporting children who demonstrate impairment in both social and academic domains, such as children at risk for attention-deficit/hyperactivity disorder (ADHD). Thus, we also examined whether these relationships differ for children with elevated ADHD symptoms and peer problems (i.e., target students), relative to classmates (i.e., non-target students). Participants were 194 children in the classrooms of 12 teachers (grades K-4) who participated in an open-trial pilot study of the school-based version of the Making Socially Accepting Inclusive Classrooms (MOSAIC) program. In the fall and spring of a school year, we assessed children's sociometric ratings received from peers, and academic enabler skills as rated by teachers. Throughout one academic year, we obtained assessments of teachers' use of MOSAIC strategies (observed and self-reported). Results showed that, after accounting for fall academic enablers, the teacher strategy of CARE time (involving one-on-one interaction with the student to build the teacher-student relationship) was positively associated with spring academic enablers. However, findings did not support the hypothesized indirect effect of peer sociometric ratings on the relationship between teacher strategy use and academic enablers, or the moderated indirect effect by target student status. Implications for future research and classroom interventions are discussed.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".