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Record W2321060177 · doi:10.5539/jel.v5n2p109

The Impact of the 5E Teaching Model on Changes in Neuroscience, Drug Addiction, and Research Methods Knowledge of Science Teachers Attending California’s ARISE Professional Development Workshops

2016· article· en· W2321060177 on OpenAlexvenueno aff
Rosa D. Manzo, Linda Whent, Lauren Liets, Adela de la Torre, Rosa Gomez-Camacho

Bibliographic record

VenueJournal of Education and Learning · 2016
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience, Education and Cognitive Function
Canadian institutionsnot available
FundersNational Institutes of Health
KeywordsAddictionPsychologyTest (biology)Mathematics educationProfessional developmentMedical educationPedagogyMedicineNeuroscienceBiology

Abstract

fetched live from OpenAlex

<p>This study examined how science teachers’ knowledge of research methods, neuroscience and drug addiction changed through their participation in a 5-day summer science institute. The data for this study evolved from a four-year NIH funded science education project called Addiction Research and Investigation for Science Educators (ARISE). Findings were based on pre- and post-test evaluation data from three annual cohorts in June 2010, 2011 and 2012. Researchers found significant improvement in teacher knowledge overall and on all subscales. Teachers with lower pre-test scores showed the greatest gain in post-test scores. What made this in-service unique was that the 5E pedagogical model was used to teach the teachers and demonstrate 5E instruction in the science classroom. Through the use of the 5E teaching method, we found that teachers in our cohorts with the least skill had higher rates of gain. A strategy that has been used extensively to teach science to children, this model moves away from didactic methods of in-service pedagogy. These findings suggest that the 5E model could be an effective way to teach teachers as well as students, particularly new and or less skilled teachers, who often tend to have high numbers of English Learner (EL) students in their classes.</p>

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)0.008
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.844
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.093
GPT teacher head0.464
Teacher spread0.371 · 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

Citations6
Published2016
Admission routes1
Has abstractyes

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