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Record W4247751216 · doi:10.23977/aetp.2019.31001

"Internet +" Path to Improve the Quality of Ideological and Political Theory Courses in Higher Vocational Colleges

2019· article· en· W4247751216 on OpenAlexvenueno aff
Xiaolei Zhang

Bibliographic record

VenueAdvances in Educational Technology and Psychology · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicIdeological and Political Education
Canadian institutionsnot available
Fundersnot available
KeywordsIdeologyVocational educationQuality (philosophy)The InternetCurriculumPoliticsPosition (finance)Political philosophySociologyCourse (navigation)Mathematics educationPedagogyPublic relationsPolitical scienceEngineering ethicsPsychologyEngineeringComputer scienceBusinessEpistemologyLaw

Abstract

fetched live from OpenAlex

The improvement of the quality of ideological and political theory courses in higher vocational colleges under the background of "Internet +" is one of the important ways to achieve the goal of high-quality talents training. The ideological and political theory course (hereinafter referred to as the ideological and political course) in higher vocational colleges is the main channel and main position for the systematic ideological and political education of higher vocational students. Through the investigation of 27 enterprises and 7 colleges, the research team found the following reform directions and countermeasures for the ideological and political course: 1. Adopt three kinds of information methods to mobilize students' interest in learning. 2. Strengthen the training in secondary school. 3. Explore new teaching models. 4. Compiling the ideological and moral cultivation and legal foundation course guidance as a supporting material to improve the effectiveness. 5. Adopt three practical teaching methods. 6. Make full use of the network to obtain course-related materials. 7. Divide the tasks in the form of modules to further improve the curriculum construction materials.

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)0.001
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.200
Threshold uncertainty score0.627

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.034
GPT teacher head0.435
Teacher spread0.401 · 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
Published2019
Admission routes1
Has abstractyes

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