Modeling and the Gradual Release of Responsibility: What Does It Look Like in the Classroom?
Bibliographic record
Abstract
Recent professional development efforts in literacy have highlighted the role of the teacher as a model for students using direct instruction. Direct instruction is a lesson methodology taught to teacher candidates. We developed a schematic to represent the confluence of evidence found in the research and analysis of several lesson planning templates in order to create a visual representation of the elements of instruction that could be used to plan lessons. Previous research has demonstrated that modeling was not used frequently in classrooms. We were interested in determining if teachers were still using modeling infrequently. To investigate this, we identified three questions we would pursue through action research and mixed methods of analysis in local classrooms. These questions focused on determining the amount of time spent modeling in classrooms and the actions used after modeling to determine the extent these actions were reflected in the research literature. We found that teachers are using modeling much more frequently than was found to be the case in the previous study, but that the instructional actions following modeling are often inconsistent with research literature conceptions. Key Words: direct instruction, modeling, gradual release of responsibility, models for teaching
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".