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Record W2121452694 · doi:10.5539/ass.v10n4p195

The Direction of Generic Skills Courses at National University of Malaysia (UKM) towards Fulfilling Malaysian Qualifications Framework

2014· article· en· W2121452694 on OpenAlexvenueno aff
Nazri Muslim, Nasruddin Yunos

Bibliographic record

VenueAsian Social Science · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Islamic Studies
Canadian institutionsnot available
FundersUniversiti Kebangsaan Malaysia
KeywordsTeamworkHuman capitalCompetition (biology)Soft skillsPeople skillsPosition (finance)Vocational educationUnemploymentSkills managementPublic relationsBusinessMarketingManagementPolitical sciencePsychologyPedagogyEconomic growthEconomics

Abstract

fetched live from OpenAlex

One of the key issues of the country is poor command of certain skills among university graduates from Public Higher Education Institute (PHEI). This matter has increased the rate of unemployment among graduates. This is because the industry as one of the key sectors of the economy generators needs human capital that masters various skills. The industry is facing global competition that requires them to participate and compete. Hence, to ensure that their position remains strong and relevant, human capital that masters skills such as communication skills, decision making, teamwork and others are urgently needed. Hence the emphasis on the aspects of these skills should be incorporated in the national education system. This article discusses the direction of generic skills courses at the National University of Malaysia in fulfilling the needs of Malaysian qualifications framework. This study is performed through analysis methodology on relevant documents in the matter. The study found that the National University of Malaysia has already implemented adoption of generic skills in courses offered, in fulfilling Malaysian qualifications framework. This will produce a student that is equipped with generic skills and further meets the needs of the job market.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.826
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.003
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.023
GPT teacher head0.324
Teacher spread0.300 · 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 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

Citations3
Published2014
Admission routes1
Has abstractyes

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