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Record W2942482723

Crossing Cultural Borders in Science: Impact of Community Based Learning

2018· article· en· W2942482723 on OpenAlexaff
Virendra Mohan Verma, Latika Raisinghani

Bibliographic record

Venue2019 Conference of the Canadian Society for the Study of Education · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsExperiential learningMicronesianScience learningPedagogySpace (punctuation)SociologyValue (mathematics)Science educationMathematics educationPsychologyAnthropology
DOInot available

Abstract

fetched live from OpenAlex

This case study investigated impact of community-based experiential learning (CBEL) on first year undergraduate students learning of science in Kosrae, Micronesia. The investigation involved pre- and post- CBEL reflections of individual students as well as focus group interviews with select students, which were analyzed qualitatively. The results of the study include: 1) CBEL experiences created a space for students to overcome language barriers 2) Students’ acknowledged CBEL as a platform to be encultured into the culture of science 3) Students identified CBEL experience as empowering and valuable at the personal and communal fronts. These findings suggest that CBEL proved to be an effective strategy which allowed Micronesian students to cross cultural borders and thereby, promoted their learning of science at the individual and communal level as well as promoted their sense of civic responsibility. The insights gained from this study will inform future iterations of the course. These may also inform teaching practices of other science educators in Micronesia, and in wider international contexts that value promoting cross-cultural communal learning of science.

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)0.001
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.202
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.002
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 head0.407
Teacher spread0.326 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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