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Record W2937788601

Learning Study in Teacher Education: Pre-service Teachers’ Experiences in Planning Lessons with Variation Theory

2018· article· en· W2937788601 on OpenAlexaffabout
Miechie Leo, Yuen Sze Michelle Tan, Douglas Adler

Bibliographic record

Venue2019 Conference of the Canadian Society for the Study of Education · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicScience Education and Pedagogy
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPhenomenographyMathematics educationPedagogyCurriculumTeacher educationVariation (astronomy)Professional developmentPlan (archaeology)Grounded theoryPsychologyQualitative researchSociology
DOInot available

Abstract

fetched live from OpenAlex

This paper reports on pre-service teachers’ experiences in planning science lessons which were guided by a learning theory, variation theory. Specifically, how the teachers identified the objects of learning for their lessons, as well as the critical aspects corresponding to these objects of learning, were focused on; this aspect of lesson planning was reported to be challenging for teachers. A total of 27 science pre-service teachers from a Western Canada university participated in a professional development approach -- Learning Study -- that was implemented as part of a course in their initial teacher educaiton programme. With the purpose for teachers to bridge theory and practice, they learned about variation theory and collaborated in groups of four to five to plan science lessons. Phenomenography methodology was employed to capture the variation in the teachers’ experiences, resulting in the construction of three categories of description to illustrate the pre-service teachers’ complex experiences of learning to design and organize their science lessons. Categories include how the teachers analysed science curricula to develop coherency in lesson planning, reflected on personal learning and teaching experiences to inform pedagogy, and analysed external resources to develop knowledge of students. Implications will be discussed.

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)0.003
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.373
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.081
GPT teacher head0.391
Teacher spread0.310 · 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 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
Published2018
Admission routes2
Has abstractyes

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