MétaCan
Menu
Back to cohort
Record W2066345250 · doi:10.5539/ies.v4n1p212

Management of Technology (MoT) undergraduate program in Malaysia: A Perspective

2011· article· en· W2066345250 on OpenAlexvenueno aff
Alina Shamsuddin, Nor Hazana Abdullah, Eta Wahab

Bibliographic record

VenueInternational Education Studies · 2011
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumPerspective (graphical)Higher educationInstitutionBusinessEngineering managementSociologyPolitical sciencePublic relationsEngineeringPedagogyComputer scienceSocial science

Abstract

fetched live from OpenAlex

Management of Technology (MoT) Education is growing both in numbers and importance. There are more than 200 universities in the world that are offering MoT programs. However, these universities have taken different approaches with respect to the names and designs of the programs. In Malaysia, some of the programs are known as Technology Management, Production & Operation and Industrial Management. The curriculum structures and contents of the programs also vary. For more effective MoT education in Malaysia, it is necessary to consider both the national agenda and business requirements for more practical MoT education approach. This study described two MoT undergraduate programs in one public institution in Malaysia to fill in the literature gap on MoT education in Malaysia. It adapted the IAMOT MoT Credo as a framework for comparing the structure and contents of the programs. The findings concluded that, although the two programs have met the IAMOT Credo’s requirements, they have distinctive features. These distinguishing characteristics reflected the Malaysian’s human resource needs.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.318
Teacher spread0.295 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueInternational Education StudiesSame topicEngineering Education and Curriculum DevelopmentFrench-language works237,207