Increasing Teacher Retention by Improving Self-Efficacy and Classroom Management Skills in Pre-Service Teachers
Bibliographic record
Abstract
Pre-service teacher preparation programs are often ineffective in preparing new teachers to use classroom management skills and strategies. This contributes to high teacher-turnover rates for new teachers and ultimately influences principal retention and student outcomes. The crisis of teacher retention in the aftermath of the pandemic threatens U.S. global competitiveness and national security. Teacher preparation programs can address job stress and job satisfaction by better preparing teachers for the challenges of the post-pandemic classroom. The purpose of this longitudinal, mixed-methods improvement science study was to determine if embedding evidence-based classroom management skills and strategies into the instructional methods coursework programs, augmented by structured applied learning opportunities to improve the IE, would improve pre-service teachers’ self-efficacy in classroom management. The research used the Classroom Management Self Efficacy Instrument, focus groups, and written reflections. Five themes emerged from the data: student-teacher relationships, orderly classrooms, preventative measures, difficult students, and use of technology. Scores for seven practice-oriented items on the CMSEI showed strong improvement (M = 93.75%, post-intervention). Responding to the CMSEI question “I can manage a class very well,” 87.50% of students strongly agreed or agreed after the intervention. On eight items pertaining to self-efficacy on the post-survey, students reported strong efficacy (M = 85.16% agree or strongly agree). The major conclusion from this study is that embedding evidence-based classroom management skills and strategies into the instructional methods coursework, supported by structured applied learning opportunities, improves pre-service teachers’ self-efficacy and holds tremendous potential to reduce teacher attrition.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".