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Record W3187961734 · doi:10.3968/12161

The Market-Oriented Education Approach Brings Challenges to the Practice: Based learning Approach in Pre-school Higher Education

2021· article· en· W3187961734 on OpenAlexvenueno aff
Jindao Wang, Weisha Wang, Wei Xiao, Siting Liang, Yang Dong, Haoyuan Zheng, LI Fan-lin

Bibliographic record

VenueCanadian social science · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education and Employability
Canadian institutionsnot available
Fundersnot available
KeywordsEmployabilityCurriculumGovernment (linguistics)Higher educationWork (physics)Process (computing)Quality (philosophy)PedagogyMedical educationEngineering ethicsSociologyPolitical scienceEngineeringComputer scienceMedicine

Abstract

fetched live from OpenAlex

Universities in Chinaare encouraged by the Chinese government to adopt a practice-based learning approach in the Higher Education sector. Such emphasis exerts great impacts on the traditional practice-basedpre-schoolHigher Education and has alsoimposed some challenges ontoacademics in Universities. The market-oriented approach requires Universities to emphasize employability skills that meet the demands of delivering a modernized and international pre-schooleducation. Universities need to adapt their curriculum design to embed practical training on employability skills into the curriculum.Building on our existing knowledge, the pre-school curriculum is aiming to work with pre-school practitioners. Bridging the gap between University teachingandpractical skills required by the pre-school sector, this process facilitates practice-based learning for students and improves research for academics. Piloting practice-based learning allows Universities to focus on enhancing employability skills and helping Universities adapt their curriculum to achieve the desirable employability skills they want their graduates to have. As a result, the practice-based learning approach informs Universities’ curriculum design and quality standards so that graduates develop employable attributes.

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)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.946
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0040.001
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 head0.026
GPT teacher head0.337
Teacher spread0.310 · 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.

Study designNot applicable
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
Published2021
Admission routes1
Has abstractyes

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