Curriculum as a support to investigative approach in learning chemistry
Bibliographic record
Abstract
One of the main reasons for low achievement of our students in international tests is the lack of functional, applicable knowledge. Formation of such knowledge demands changing the usual way of implementation of instruction (transfer of ready-made knowledge) to learning through performing simple research and practical work. Considering the fact that instruction, as an organised process, takes place in frameworks determined in advance, which are arranged and regulated on the national level by curricula, it is assumed that this kind of approach must originate precisely from curricula, which is not the case in our educational practice. The goal of this paper was to determine the way in which this kind of approach in instruction and learning of chemistry can be supported by the curriculum, in order for it to become a part of regular teaching practice on the national level. The paper presents how different structural components of curricula from eight different educational systems (four European countries, one Asian country, two American federal states and one Canadian province) are used to promote and support the importance of research work in instruction and learning of chemistry. The curricula from Slovenia, England, Denmark, Malta, Singapore, North Carolina, Utah and Ontario were analyzed in order to determine the kind of information they offer within structural components and accordingly, the way in which each component promotes research approach to learning chemistry, how it guides the teacher in planning such activities in the classroom, organization and performing instruction, monitoring and evaluating students' achievements.
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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 | 0.001 | 0.007 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".