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Record W3114853063 · doi:10.5539/ies.v14n1p76

Educational Research Training in Teacher Training Programs: The Views of Future Teachers

2020· article· en· W3114853063 on OpenAlexvenueno aff
Haylen Perinés

Bibliographic record

VenueInternational Education Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Teacher Training
Canadian institutionsnot available
FundersAgencia Nacional de Investigación y Desarrollo
KeywordsTraining (meteorology)Focus groupQualitative researchPsychologyMedical educationTeacher educationPedagogyEducational researchMultimethodologyVisibilityFaculty developmentMathematics educationProfessional developmentSociologyMedicine

Abstract

fetched live from OpenAlex

The objectives of this study were to explore teacher student’s views on the research training they receive and know their suggestions for improving it. This was a qualitative study conducted on students in the teacher training programs of a Chilean public university who were distributed into nine focus groups. The findings showed that the students have a rather critical view about the training they receive, particularly due to the lack of continuity of research-related courses and their limited participation in research activities. Regarding how to improve their training, the students suggest providing one research-related course each year, having researchers as teachers, and giving greater visibility to the educational research produced within their university. The study concludes that it is important to promote participation in educational research and that university teacher educators and the entire university community must adopt a broad-ranging view on this subject.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.044
metaresearch head score (Gemma)0.038
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.235

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0440.038
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0140.012
Scholarly communication0.0130.007
Open science0.0020.006
Research integrity0.0040.009
Insufficient payload (model declined to judge)0.0040.001

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 head0.624
GPT teacher head0.566
Teacher spread0.058 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations17
Published2020
Admission routes1
Has abstractyes

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