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Record W4386800723 · doi:10.23977/aetp.2023.071117

A Brief Analysis of the Application of Digital Resources to Vocational Colleges

2023· article· en· W4386800723 on OpenAlexvenueno aff
Yang Junxi

Bibliographic record

VenueAdvances in Educational Technology and Psychology · 2023
Typearticle
Languageen
FieldComputer Science
TopicEducational Technology and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsProcess (computing)Vocational educationCurriculumQuality (philosophy)Computer scienceResource (disambiguation)The InternetEngineering managementTeaching methodShared resourceKnowledge managementMathematics educationEngineeringWorld Wide WebPedagogySociology

Abstract

fetched live from OpenAlex

The construction of digital teaching resources is an important means to adapt to the development trend of "Internet + education and teaching" reform, promote the development and sharing of high-quality teaching resources, and promote the comprehensive application of information technology in the process of education and teaching reform and teaching implementation, and it is also an important guarantee for strengthening the construction of digital teaching resources and network teaching. Through the exploration of digital resources, this paper aims to fully learn from the experience of advanced digital teaching resources construction at home and abroad in the process of promoting the construction of digital teaching resources, and encourage teachers to carry out teaching and research on the construction and application of digital resources. It is necessary to fully understand the needs of teachers and students, find problems and study countermeasures; It is necessary to combine the existing information technology application environment of the school to build digital teaching resources suitable for the school, and strengthen the application of digital teaching resources and improve the application ability of digital resources based on resource development, curriculum construction as the core, and the improvement of teachers' teaching ability as the guarantee.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.352
Threshold uncertainty score0.398

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.008
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.016
GPT teacher head0.371
Teacher spread0.355 · 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 designTheoretical or conceptual
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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