Effect of Preservice Classroom Management Training on Attitudes and Skills for Teaching Children With Emotional and Behavioral Problems: A Randomized Control Trial
Bibliographic record
Abstract
Childhood emotional and behavioral problems are prevalent in elementary classroom settings, making it imperative that high-quality, efficacious training be available to support teachers in managing disruptive and distressed child behaviors. Our study used a randomized control design to examine the impact of 36 hours of preservice education targeted at improving the attitudes of teachers toward children with emotional and behavioral difficulties, and developing their skills in using proactive and preventative strategies to address anticipated behavioral challenges. Eighty-two preservice teachers were randomly assigned to an elective course on management of emotional and behavioral problems (50 teachers) or to an alternate elective of their choice (32 teachers). Results highlight the positive influence of targeted preservice instruction; specifically, there were medium to large posttest effect size differences between preservice teachers who received this elective as compared with those who did not on measures of teachers’ use of psychological pressure (e.g., teacher disappointment and shaming; d = 0.76), their positive emotions ( d = 0.69), negative reactions ( d = 1.05), and their use of proactive strategies ( d = 1.43 and 1.59), inadequate strategies ( d = 0.73), and reactive strategies ( d = 1.01) in response to challenging child behaviors in simulated classrooms. No significant intervention-related differences were noted in preservice teacher self-efficacy, endorsement of rules and control, warmth and support, or negative beliefs. Overall, results provide promising evidence that preservice training can effectively affect the immediate attitudes and skills of teachers for supporting children with emotional and behavioral problems in a regular classroom context.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".