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Shifting Paradigms in Secondary Science Classrooms: Teaching and Learning from an Ecological Perspective

2014· article· en· W2727944409 on OpenAlexaff
Sharon Pelech, William Pelech

Bibliographic record

VenueThe International Journal of Interdisciplinary Educational Studies · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsUniversity of CalgaryMemorial University of Newfoundland
Fundersnot available
KeywordsPerspective (graphical)Mathematics educationScience educationEcologySociologyPedagogyPsychologyComputer scienceBiologyArtificial intelligence

Abstract

fetched live from OpenAlex

This paper explores ecological learning theory and how it disrupts the present understanding of knowledge, intelligence, and the individual. In the education system, intelligence is seen as the basic capacity for competence; reasoning, as the activity that generates competence (St. Julien, 2000, p. 254). The common language here points to the Cartesian idea that knowledge is something that is outside the individual and that intelligence is an attribute within the individual that allows them to make use of this knowledge. Educational practices are built upon these assumptions that something must be done to the student to help them acquire and apply this knowledge. Ecological learning theory's fundamental understanding of intelligence breaks away from this paradigm and offers a very different understanding of cognition and intelligence. Moving the focus of education from studying about the world towards being part of the world means that a completely different way is needed to understand knowledge and learning (Davis, Sumara and Luce-Kapler, 2000). This means the definition of what learning is has burst open to incorporate many experiences and interactions compared to the traditional narrow definition of learning. From this frame, we will explore the implications for teachers and students in a secondary science classroom.

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)0.006
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.209
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.429
Teacher spread0.390 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

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