Supporting First Year Alternatively Certified Urban and Rural Intern Teachers
Bibliographic record
Abstract
abstract: The pathway for entering the K-12 classroom as a teacher varies compared to what was once the traditional model of teacher preparation. In this mixed-methods action research study, I explore supporting first year alternatively certified urban and rural intern teachers through a multicomponent distance induction program. The induction model in this study was based on the theoretical framework of Bandura's social learning theory and Wenger's communities of practice. The purpose of this study was to identify the extent in which a multicomponent distance learning induction program impacts first year intern teachers' sense of self-efficacy, understand their successes, their challenges, and to identify how intern teacher evaluations change. Quantitative data included results from a self-efficacy survey and the Student Teaching Assessment Instrument (STAI). Qualitative data was collected through intern teachers' blogs, cadre leader video narratives, and cadre leader STAI narrative responses. Six themes emerged including topics such as building relationships with other education professionals, receiving feedback from the cadre leader, identifying struggles and application of college coursework into the K-12 classroom. Key findings reveal interns are least efficacious in student engagement, relationships with other educators support an intern teacher emotionally and pedagogically, intern teachers struggle with work-life balance, and cadre leaders observed intern teachers as having improved their skills in student engagement, instructional practices, and classroom management. Implications to practice include a structured approach to introducing student engagement, creating a best practices library of video examples, and a pre-orientation (Super Saturday) of topics prior to stepping into the classroom with students.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".