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Record W4393327239 · doi:10.18269/jpmipa.v27i2.41724

Probing Students’ Research Skills: A case of the Utilizing Technology in The New Normal Era

2022· article· en· W4393327239 on OpenAlexaff
Joey Ms. Suba, Edwin D. Torres, Jon Ivee P. Hernandez, Maribeth G. Rivera, Maybell L. L. Panlaqui, Lester G. Loyola

Bibliographic record

VenueJurnal Pengajaran Matematika dan Ilmu Pengetahuan Alam · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Curriculum and Learning Methods
Canadian institutionsAssumption University
Fundersnot available
KeywordsComputer sciencePsychologyPedagogyMathematics educationMedical educationSociologyMedicine

Abstract

fetched live from OpenAlex

Online delivery systems have been a booming industry worldwide because it has become an easy and safe way of acquiring necessities in our daily lives. This trend is the most recognized case of the application of information technology in the new normal era. Information technology major students’ skills in conducting research were identified Using the results of research related to the online delivery system. A slight difference was found between the information technology education major (preservice IT teachers) and the information technology major. However, the results cemented the importance of embedding research results in the IT major curriculum.

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)0.021
metaresearch head score (Gemma)0.048
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.109

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.048
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0100.014
Scholarly communication0.0070.005
Open science0.0030.008
Research integrity0.0070.012
Insufficient payload (model declined to judge)0.0030.001

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 head0.064
GPT teacher head0.429
Teacher spread0.365 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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