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Record W4384915304 · doi:10.23977/aetp.2023.070711

Computer Information Teaching Reform Based on Internet of Things

2023· article· en· W4384915304 on OpenAlexvenueno aff

Bibliographic record

VenueAdvances in Educational Technology and Psychology · 2023
Typearticle
Languageen
FieldComputer Science
TopicEducational Technology and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsVocational educationSpecialtyCurriculumProcess (computing)The InternetQuality (philosophy)Computer technologyEngineering managementSocial needsInformation technologyTeaching methodComputer sciencePublic relationsEngineeringSociologyMultimediaPedagogyPolitical sciencePsychologyEconomic growthWorld Wide WebEconomicsHealth care

Abstract

fetched live from OpenAlex

With the rapid development of modern communication technology, the major of computer application has attracted much attention, and now it has become a popular major in secondary vocational schools. Under the background of the new era, the computer application specialty is facing a broader development space, and the demand of society for computer application professionals is getting higher and higher. Secondary vocational schools must strengthen the investigation of the industry and the market, compare the social needs with the current situation of talent cultivation, education and teaching methods, constantly optimize computer teaching, improve teaching objectives and teaching plans, and improve the quality of talent cultivation. This paper analyzes the influence of Internet of Things technology on computer application specialty, analyzes the reform strategy of computer application specialty's practice curriculum from the aspects of practice curriculum system reform, practice teaching environment reform and assessment method reform, and puts forward the specific process of integrated teaching.

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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.672
Threshold uncertainty score0.412

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
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 head0.015
GPT teacher head0.360
Teacher spread0.345 · 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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