Two Ways to Teach: Direct Instruction and Indirect Instruction/Inquiry: Simplifying Planning Concepts for Early Career Teachers
Bibliographic record
Abstract
This paper describes two planning diagrams that support pre-service and early career teachers’ understanding of direct and indirect instructional approaches to teaching Conceptual models can support understanding of the embedded decision points that new teachers must address as they plan lessons. In this paper, we offer 2 models to support the understanding of pre-service and early career teachers with two conceptual diagrams that relate to lesson planning. One of these diagrams has been used for several years with pre-service teachers who have identified that this conceptual diagram has helped them understand planning concepts early in their planning experiences. This diagram demonstrates the phases of instruction used by experienced teachers when they plan for direct instruction. A body of prior research has been completed to demonstrate the existence of the main conceptions and relative times in the diagram as they are evident in teachers’ practice and to identify how the diagram is perceived by pre-service teachers. The second diagram has been designed as a complimentary method of helping pre-service teachers understand concepts related to planning for indirect instruction involving various forms of inquiry.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".