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Record W2802632093

STUDY ON EFFECTS OF INTERNET USAGE ON PRIMARY EDUCATION AND PRIMARY TEACHERS

2018· article· en· W2802632093 on OpenAlexaboutno aff
Rajeshkumar Modi, Dr.Nidhi Goel

Bibliographic record

VenueJournal of Emerging Technologies and Innovative Research · 2018
Typearticle
Languageen
FieldComputer Science
TopicMobile Learning in Education
Canadian institutionsnot available
Fundersnot available
KeywordsRestructuringThe InternetGlobalizationProcess (computing)Work (physics)HypermediaTeacher educationPrimary educationProfessional developmentPublic relationsPedagogyMathematics educationPolitical scienceComputer sciencePsychologyBusinessEngineeringMultimediaWorld Wide WebFinance
DOInot available

Abstract

fetched live from OpenAlex

Review of related studies promotes a greater understanding of the problem and its crucial aspects and ensures the avoidance of unnecessary duplication. It is an indispensable part of any research project. It is also an important prerequisite to actual planning and then execution of any research work. Without the help of hypermedia and mass communication we never think up about the globalization and restructuring educational pedagogy to ameliorate existing teacher education. In countries throughout the world new methods and means are used to improve in teacher education programme. In developed countries like Canada, USA, Australia, Europe are being paid to the role of computer technology in the process of teacher education. There are several profitable and non-profitable professional organizations dedicated to the improvement of teacher education programme through computer based technology.

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)0.001
metaresearch head score (Gemma)0.009
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.045
GPT teacher head0.392
Teacher spread0.347 · 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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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Same venueJournal of Emerging Technologies and Innovative ResearchSame topicMobile Learning in EducationFrench-language works237,207