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A NUMBER OF KILOMETERS MATTERS: A COMPARATIVE STUDY ON LEARNERS STUDY HABITS AND SCHOOL PERFORMANCE

2023· article· en· W4388425932 on OpenAlexaboutno aff
LEVIBOY C GEPITULAN, BERLEIN ANN E. SURTIDA

Bibliographic record

VenueInternational Journal of Research Publications · 2023
Typearticle
Languageen
FieldComputer Science
TopicEducational Methods and Media Use
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)HabitLikert scaleSimple random samplePsychologyKilometerMathematics educationSample (material)PopulationScale (ratio)DemographyGeographyDevelopmental psychologySocial psychologySociologyPhysics

Abstract

fetched live from OpenAlex

Going to school is a daily affair in a students life. The distance between home and school may be a determining factor in the school performance of the students. In this study, it aims to compare two sample groups of students: one group lives 1-2 kilometers from the school, and the other lives 3–4 kilometers from the school. The researchers utilized a sample size of 166, which represents the entire population of Binoni National High School. The participants were chosen using simple random sampling. Then the respondents were subjected to answering a questionnaire that measured the study habits of the participants and their fourth quarter ratings for school performance. The general average of the students near the school is 85.43%, while those living away from school have a general average of 85.47%. While the Likert scale shows an overall mean of 3.98 with a verbal description of agree, students from 3–4 kilometers from school have an overall mean of 4.11 with a verbal description of agree. These findings suggest that even if there is no significant difference in school performance between students who live near and far from school, However, they differ in their study habits students living away from school had a better study habit than students living near the school.

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)0.004
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 score0.063
Threshold uncertainty score0.282

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.256
GPT teacher head0.541
Teacher spread0.285 · 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
Published2023
Admission routes1
Has abstractyes

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