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Record W4386078570 · doi:10.59120/drj.v12i4.32

Printed Self-Learning Module Distribution and Completion Preferences of Grade 7-12 Students of Tagugpo National High School in Davao Oriental, Philippines: A Conjoint Analysis

2021· article· en· W4386078570 on OpenAlexaboutno aff
Bronson Dapa, Gemma M. Valdez

Bibliographic record

VenueDavao Research Journal · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Vocational Training
Canadian institutionsnot available
Fundersnot available
KeywordsConjoint analysisPreferenceSummative assessmentDescriptive statisticsMathematics educationPsychologyScale (ratio)Quarter (Canadian coin)Distribution (mathematics)MathematicsGeographyStatisticsFormative assessment

Abstract

fetched live from OpenAlex

This study was conducted to determine the printed self-learning module distribution and completion preferences of the students of Tagugpo National High School, Tagugpo, Lupon, Davao Oriental, Philippines. It is descriptive survey research wherein one-hundred eighty-four randomly selected student respondents are shown various choices or hypothetical profiles and asked to evaluate these profiles based on their preferences. To determine the overall preference of these students for printed self-learning module distribution and completion, conjoint analysis was done. The analysis revealed that students from Tagugpo National High School expressed a preference for the modules to be printed in booklet form, distributed within their respective barangays on Mondays, and collected at the conclusion of each quarter. They also preferred to be given only four subjects per week with a t wo-hour duration each and accomplish only the activities found in the modules with no summative tests. This study recommends that students may be given three options on how they want their modules to be distributed and accomplished. It also suggests that further study on self-learning module preferences may be done on a wider scale, including assessment revision and a link between module preferences and student learning outcomes and dropout intentions.

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)0.004
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 score0.032
Threshold uncertainty score0.555

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.174
GPT teacher head0.481
Teacher spread0.307 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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