A Case Study of Curriculum Unpacking Practices of a Kindergarten Teacher
Bibliographic record
Abstract
Curriculum unpacking, defined as the process of interpreting the intended curriculum into classroom instruction, is important in the overall success of the school curriculum. As a critical process that serves as a bridge between the intended curriculum and classroom instruction, however, there is a paucity of research about it. Hence, this study aimed to describe the curriculum unpacking practices of a teacher. It entailed a qualitative research design specifically a case study to look closely into the single context of a purposively selected kindergarten teacher in a public school. The main data gathering techniques used were key informant interview and document review. The data obtained were subjected to thematic analysis. The result of the study revealed that the participant follows a generally linear process in unpacking the curriculum as noted in the compliance to the minimum standards of the intended curriculum, main consideration of the learner while translating the intended curriculum into instruction as mandated in the law, and alignment of the curriculum and instructional components. However, qualitative probes uncovered possible errors such as misinterpretation of the developmentally appropriate principle espoused by the intended curriculum and discrepancy between the curriculum standards and instructional activities. The implications in practice are discussed in the study.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".