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Record W2758230080

Motivating High School Students to Persist in Science through Teacher-Scientist Partnerships

2014· article· en· W2758230080 on OpenAlexfundno aff
Berkel Williams

Bibliographic record

VenueTSpace · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicScience Education and Pedagogy
Canadian institutionsnot available
FundersOffice of International Science and EngineeringUniversity of Toronto
KeywordsMathematics educationScience educationPedagogySociologyEngineering ethicsPsychologyEngineering
DOInot available

Abstract

fetched live from OpenAlex

The improvement of science education has been the focus of numerous studies in the past, however few education researchers have questioned both secondary school teachers and practicing scientist themselves to determine ways in which they could collaborate to make the learning of science a more authentic experience for students. Toward this end, this qualitative study was conducted using semi-structured interviews with secondary school teachers and scientists who were involved in partnerships. During the interviews the following primary research question was addressed: How do secondary school science teachers and practising scientist collaborate to motivate and encourage students to remain engaged in science and/or to pursue careers in science? The data collected was then analyzed to identify common themes. The findings indicate that there are several reasons why students remain engaged in science including types of science-related experiences, family influences, and personal characteristics. Conversely, fear of failure, misconceptions about the nature of science, and time investment were some of the reasons given for the failure of students to persist in science. In addition, although scientists and teachers collaborate in a variety of ways; the availability of resources, teacher apathy, institutional culture and organizational structures act as barriers/hindrances to the formation of these partnerships.

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)0.005
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.168
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.139
GPT teacher head0.486
Teacher spread0.347 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

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