The Development and Implementation of Alternative Teacher Preparation Programs in Maryland: A Cross-Case Study of Montgomery and Prince George's County Public Schools.
Bibliographic record
Abstract
Alternative teacher certification programs have become a prominent policy option to address teacher quality issues. Despite recent headway in the analysis of alternative teacher preparation graduates and programs (e.g., Weschler, et al., 2009; Boyd, et al., 2007), the literature has been plagued by incomplete program descriptions, limited research on policies, and the contextual factors that influence program development and implementation. The purpose of this study is to address the gaps in the literature by considering how two Maryland school districts translated state alternative teacher preparation policies into programs between 1999 and 2008. Using a cross-case study of the two school districts, this research seeks to understand how local, state, and national factors influence the development and implementation of alternative teacher preparation programs and policies. Since the current literature base does not contain an integrated framework to systematically describe alternative preparation programs and concurrently consider the influence of the multiple levels of the policy context, I created two orienting frameworks. This study contains two district case studies that consider the development and implementation of alternative teacher preparation policies and programs within each district and then a cross-case analysis that examines the patterns of development and implementation of policies and programs across districts. This study finds that (1) the No Child Left Behind policy, Maryland's alternative preparation policy requirements, and each district's experience within the teacher labor market influenced the prevalence and development of programs in each district; (2) the districts' approaches to and work with providers reflected the debate and division in the national teacher education debate and the perceived "quality" of types of alternative preparation; (3) the majority of program training components, program theories of action, and implementation adjustments were not shaped by districts factors, but through Maryland's alternative preparation policy requirements and the individual provider; and (4) the contextual conditions of the districts' situation within the broader policy environment set each district up to pay closer attention to aspects of program development and implementation over others. The study closes by proposing refinements to the study's conceptual frameworks and discusses the use of contextualized teacher education research to consider teacher education program quality.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".