Exploring Organization of Subject Curriculum for Improvement of Coherence between General Guidelines and Subject Curriculum
Bibliographic record
Abstract
Objectives The purpose of this study is to propose the organization of subject curriculum to support a learner-centered curriculum design and practice for competency development, while ensuring coherence between general guidelines and subject curriculum. Methods Analyzing the teaching, learning, and assessment guidelines of the 2022 revised curriculum framework, we derived the key points that should be considered in the design of teachers' teaching and learning activities. In light of this, documents on the Canada Ontario curriculum (Language, Mathematics, SSHG, Science and Technology) were analyzed. Based on this, we analyzed and summarized the organization of subject curriculum to help design teaching and learning. Results The 2022 revised curriculum framework emphasized ‘connection within and between subjects, connection with life, learning about inquiry methods, active participation of students, and assessment as learning’. The subject curriculum of Ontario provides support for connection within and across subjects, application of learning content in real-world contexts, guidance on procedural knowledge and thinking processes, posing essential questions related to learning content, and supporting students' reflection on learning through curriculum expectations, strands, concepts and big ideas, processes, assessment and evaluation of student achievement, and optional teacher supports. Conclusions Based on these results, it was confirmed that it is possible to enhance coherence between general guidelines and subject curriculum and support teachers’ curriculum design and implementation through the establishment of a curriculum document framework. In the national curriculum development, it was suggested that there is a need for a curriculum design that systematizes content elements within the categories of process and skills, presents performance standards, and demonstrates the connection of knowledge within and across subjects according to the characteristics of the curriculum.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".