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Record W2793866276 · doi:10.5539/jel.v7n3p159

Assessment of Public Schools’ Out-of-School Time Academic Support Programs with Participant-Oriented Evaluation

2018· article· en· W2793866276 on OpenAlexvenueno aff
Şaban Berk

Bibliographic record

VenueJournal of Education and Learning · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicSchool Choice and Performance
Canadian institutionsnot available
FundersMarmara Üniversitesi
KeywordsPsychologyMathematics educationMedical educationProcess (computing)PedagogyMedicineComputer science

Abstract

fetched live from OpenAlex

Using the participants-oriented approach, this study evaluated public schools’ out-of-school time academic support programs, corresponding to the corrective/enrichment stage of Bloom’s Mastery Learning Model and offered outside formal education’s weekday hours and on weekends. Study participants included 50 principals, 110 teachers, 170 students attending programs, 110 students not attending programs, and 61 parents, all selected through random sampling in a survey-model study in Istanbul, Turkey. Partial findings were the following. According to principals and teachers, programs were sufficiently introduced to target groups. Satisfaction of attending students with the teaching—learning process was sufficient, and students believed program participation increased their success in regular classes. However, program functioning presented some problems. Administrators and teachers think the no-cost programs resulted in lack of interest among students. In addition, problems of materials and transportation have not been completely solved. Similarly, offered classes and lessons’ content organization fall short of expectations. In conclusion, out-of-school time academic support programs play important roles in reducing differences among learning levels based on individual characteristics in collective or formal learning. Still, student needs should be fulfilled, and programs should be maintained. Further studies should be conducted on these programs’ integration into formal education.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.110
Threshold uncertainty score0.813

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.138
GPT teacher head0.456
Teacher spread0.318 · 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.

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

Citations9
Published2018
Admission routes1
Has abstractyes

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