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Examining the topical coverage of genetics and biological evolution in Canadian secondary science curricula: A comparative content analysis

2021· article· en· W3170881461 on OpenAlexaffabout
Mohammad Azzam, Anton Puvirajah, Samantha Jewett, Jingrui Jiang

Bibliographic record

VenueThe FASEB Journal · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Bioinformatics, and Biomedical Research
Canadian institutionsWestern University
Fundersnot available
KeywordsCurriculumContent analysisCategorizationReading (process)Scope (computer science)NegotiationHuman geneticsPsychologyReliability (semiconductor)GeneticsComputer scienceBiologySociologyPedagogyPolitical scienceSocial scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Introduction This study was a comparative content analysis of the Canadian secondary science curriculum documents with the overarching purpose of identifying the scope and sequence of topical coverage in genetics and biological evolution. Methods The units of analysis in this study, which were comprised of all the statements pertaining to genetics and biological evolution in the 45 secondary science curriculum documents in Canada, were identified through manual reading of the documents. Furthermore, using deductive category development techniques, we categorized the statements using a categorization scheme developed by Zorek and Raehl (2013). This scheme allowed us to categorize identified statements according to their applicability and accountability to our research objectives. Inter‐rater reliability was assessed using intraclass correlation coefficients. Discrepancies between raters were negotiated until consensus was reached. Next, we employed inductive reasoning techniques to identify codes and generate common themes and categories relevant to the teaching and learning of both genetics and biological evolution. Results Of the 45 secondary science curriculum documents we analyzed, 28 contained statements that were relevant to genetics and biological evolution. Good to excellent degrees of reliability were found between rater‐pairs. Final categorization, following negotiations, resulted in a total of 509 applicable statements across all curriculum documents, of which 198 (38.90%) were accountable and 311 (61.10%) were non‐accountable. More than two‐thirds of the accountable statements were relevant to genetics (68.18%). Most topics in both genetics and biological evolution are taught in Grade 12, accounting for 58.21% and 49.18% of all genetics‐related and biological evolution‐related accountable statements, respectively. A total of 16 major themes emerged from the inductive analysis of the 135 genetics‐related accountable statements. These themes were organized into four broad categories: (1) Genetic Material (five themes); (2) Mutations (three themes); (3) Inheritance (six themes); (4) Reproductive Technology (two themes). In addition, a total of 12 major themes emerged from the inductive analysis of the 63‐biological evolution‐related accountable statements. These themes were organized into three broad categories: (1) Theory of Evolution (five themes); (2) Evolutionary Mechanisms (four themes); (3) Evolutionary Principles (three themes). Conclusion Analyses of the curriculum documents indicated that, depending on jurisdiction, biological evolution is largely taught in either Grade 11 or Grade 12, while genetics is largely taught in Grade 12. Significance Our findings denote that biological evolution is taught before and, in some jurisdictions, possibly in tandem with genetics. Accordingly, further investigation is warranted to examine exactly how these topics are taught, perhaps through qualitative research methods involving interviews with teachers nationwide. At present, nevertheless, policy and decision‐makers in many Canadian jurisdictions are advised to revise their curricula in the direction of the literature's recommendations.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.590
Threshold uncertainty score0.524

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.067
GPT teacher head0.307
Teacher spread0.240 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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".

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Citations0
Published2021
Admission routes2
Has abstractyes

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