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Record W1659170822 · doi:10.5430/jct.v4n1p145

Assessment of the Teaching Behavior of the Instructors of an Out-of-School Program

2015· article· en· W1659170822 on OpenAlexvenueno aff
Elena Rodríguez-Naveiras

Bibliographic record

VenueJournal of Curriculum and Teaching · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Practices and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsIntrapersonal communicationObservational studyInterpersonal communicationProcess (computing)Protocol (science)PsychologyMedical educationMathematics educationComputer sciencePedagogySocial psychologyMedicine

Abstract

fetched live from OpenAlex

Out-of-school programs for students with high abilities are especially relevant when their needs are not covered informal education. The evaluation of these programs is essential and it can be carried out from different evaluativeapproaches. The evaluation of the behavior of the people who implement the programs is an important aspect in theevaluation of process and it can resemble the one conducted by a teacher in formal education. This type ofassessment requires an appropriate methodology such as the observational. In this study a protocol for observation ofteaching functions has been designed for the evaluation of the behavior of the instructors of a socio-affectiveout-of-school program. The frequency of occurrence of behaviors as well as the resultant of conducts in intrapersonal,interpersonal and cooperative activities is analyzed in this study.

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)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.052
GPT teacher head0.436
Teacher spread0.385 · 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 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

Citations2
Published2015
Admission routes1
Has abstractyes

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