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Record W2588783430 · doi:10.11575/prism/28688

What Does it Mean to Teach Biology Well? A Hermeneutic Inquiry

2015· dissertation· en· W2588783430 on OpenAlexaboutno aff
Sharon Pelech

Bibliographic record

VenuePRISM (University of Calgary) · 2015
Typedissertation
Languageen
FieldSocial Sciences
TopicScience Education and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsMathematics educationEpistemologyPsychologyPhilosophy

Abstract

fetched live from OpenAlex

This study is an interpretive inquiry into how students and teachers understand the question, “What does it mean to teach biology well?” It explores the participants’ experience of the biology classroom and how they navigate between the expectations of external factors, such as diploma examinations, post-secondary requirements, and the demands of the Alberta Education Program of Studies’ expectations of teaching through an inquiry-based focus. The study explores the tensions that the students and teachers experience as they navigate between the expectations for post-secondary education and the time constraints of covering the curriculum. Through a hermeneutic frame, some of the experiences and assumptions about understandings are explored in depth. The findings discuss teachers’ sense of conflict between wanting students to have an opportunity to explore the process and complexity of science with their sense of issues with time, expectations to cover an expansive curriculum and the expectations to prepare students for post-secondary science. This study unpacks these experiences through researching the historical arguments that teachers have inherited, exploring the continual stress of running out of time, and exploring how relationships was seen as the most essential component of teaching biology.

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 categoriesInsufficient 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: none
Teacher disagreement score0.566
Threshold uncertainty score0.999

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.051
GPT teacher head0.355
Teacher spread0.304 · 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

Citations4
Published2015
Admission routes1
Has abstractyes

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