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Record W2135944495 · doi:10.20308/ejpe.v5i1.59

Pre-Service Teachers' Perceptions of Teaching STSE-Based High School Physics: Implications for Post-Secondary Studies.

2014· article· en· W2135944495 on OpenAlexaffabout
Katarin MacLeod

Bibliographic record

VenueEuropean Journal Of Physics Education · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicDiverse Educational Innovations Studies
Canadian institutionsSt. Francis Xavier University
Fundersnot available
KeywordsCurriculumContext (archaeology)Mathematics educationPerceptionTeacher educationScience educationPedagogyPhysics educationSubject (documents)PsychologyComputer science

Abstract

fetched live from OpenAlex

Science, Technology, Society and Environment (STSE) education has received attention in educational research, policy, and science curricula development, yet less advancement has been made in moving theory into practice. There are many examples of STSE-based teaching in science at the elementary and secondary levels, yet little has focused specifically on the area of physics education. This research examined pre-service physics teachers’ views and perceptions, challenges and tensions which influenced their adoption of the required STSE educational expectations found within secondary school physics curriculum of Ontario, Canada, in the context of a pre-service physics education course. The researcher employed an interpretive case study design. The pre-service physics teachers’ evolution of perceptions and attitudes demonstrate growth in the areas of curricula understanding and implementation issues, potential student concerns, and general fit of the subject within the context of a student’s learning journey. This study contributes to our understanding of the challenges faced when teaching physics through an STSE lens, provides implications for teacher education and physics education at the undergraduate level.

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 score0.942
Threshold uncertainty score0.302

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 studies0.0000.000
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 head0.038
GPT teacher head0.300
Teacher spread0.262 · 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".

Quick stats

Citations3
Published2014
Admission routes2
Has abstractyes

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