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Record W1972781355 · doi:10.2298/zipi0902477t

Curriculum as a support to investigative approach in learning chemistry

2009· article· en· W1972781355 on OpenAlexaboutno aff
Biljana Tomašević, Dragica Trivić, Snežana Bojović

Bibliographic record

VenueZbornik Instituta za pedagoska istrazivanja · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicScience Education and Pedagogy
Canadian institutionsnot available
FundersMinistry of Education, IndiaUniversity of HullUtah State UniversityAmerican Educational Research Association
KeywordsCurriculumProcess (computing)Mathematics educationWork (physics)National curriculumOrder (exchange)ChemistryComputer scienceEngineering ethicsPedagogySociologyPsychologyEngineeringMechanical engineeringBusiness

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.662
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.050
GPT teacher head0.386
Teacher spread0.336 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2009
Admission routes1
Has abstractyes

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