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Record W4385664343 · doi:10.1080/00219266.2023.2244975

Fictional placemaking creating meaningful contexts for causal reasoning in secondary school biology education

2023· article· en· W4385664343 on OpenAlexaff
Emmeline E. Hoogland, M.H.J. Ummels

Bibliographic record

VenueJournal of Biological Education · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicScience Education and Pedagogy
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPlacemakingContext (archaeology)Mathematics educationMeaningful learningScience educationClass (philosophy)PedagogySociologyPsychologyEpistemologyArchitectureVisual artsUrban design

Abstract

fetched live from OpenAlex

In secondary science education, students often do not feel engaged with the scientific concepts that are taught, which hinders conceptual learning. This lack of engagement can be overcome by fictional placemaking. Therefore, the purpose of our design-based research is to explore how the creation and use of fictional places lead to meaningful contexts providing opportunities for the learning of biology. Four design principles were formulated from theories on: (1) pedagogy of place; (2) communities of practice; (3) imaginative teaching; and (4) story-based learning. Based on these principles a lesson series was designed in which students were challenged to create societies living in domes on Mars as a fictional place. This lesson series was conducted in a ninth-grade class (28 students) at pre-university level. It was evaluated on the contribution of the design principles to create a meaningful context for the learning of biology. This research focuses on causal reasoning, which is a key competency in biology. The analysis of artefacts of group work showed evidence that students expressed different types of causal reasoning. Reflection on each of the design principles made clear how fictional placemaking provides opportunities for the development of students’ causal reasoning.

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)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.358
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.090
GPT teacher head0.463
Teacher spread0.373 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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