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Record W3198028548 · doi:10.5539/jel.v10n5p170

Predictors of Graduate Students’ Research Performance in the Philippine State-Run Higher Education Institution

2021· article· en· W3198028548 on OpenAlexvenueno aff
Venessa S. Casanova

Bibliographic record

VenueJournal of Education and Learning · 2021
Typearticle
Languageen
FieldMedicine
TopicArtificial Intelligence in Healthcare and Education
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyMedical educationGraduate studentsHigher educationCoping (psychology)Affect (linguistics)PedagogyPolitical scienceMedicineClinical psychology

Abstract

fetched live from OpenAlex

This descriptive study determined the predictors of research performance of the graduate students in Occidental Mindoro State College, San Jose, Occidental Mindoro, Philippines. This research specifically looked into the graduate students’ level of research performance, attitude towards research, challenges encountered, and the strategies employed to cope with the research challenges. A total of 41 completely enumerated students enrolled in Methods of Research and Thesis Writing during the second semester of Academic Year 2018-2019 served as respondents of the study. The study found that the graduate students’ level of research performance is high, specifically in writing the statement of the problem, hypothesis, significance of the study, and definition of terms. They have a positive attitude towards research in terms of usefulness and predispositions. They have negative research anxiety. Challenges encountered include insufficient funds, developing interest, inability to select researchable topics, and limited related literature. Coping strategies employed were frequent consultations with the adviser, seeking help from other competent faculty and students, using technology, and giving material appreciation. Attitude and challenges encountered were found to be predictors of the graduate student’s research performance. A positive attitude towards research and the moderate challenges encountered could affect the graduate student’s research performance.

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)0.002
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 score0.173
Threshold uncertainty score0.325

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.290
GPT teacher head0.509
Teacher spread0.218 · 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

Citations10
Published2021
Admission routes1
Has abstractyes

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