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Record W2142723890 · doi:10.5539/ies.v2n3p47

A Qualitative Study of Postgraduate Students’ Learning Experiences in Malaysia

2009· article· en· W2142723890 on OpenAlexvenueno aff
Sarjit Kaur, Gurnam Kaur Sidhu

Bibliographic record

VenueInternational Education Studies · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicEvaluation of Teaching Practices
Canadian institutionsnot available
Fundersnot available
KeywordsCourseworkQualitative researchPsychologyDiversity (politics)Medical educationNarrativePublic universityPedagogyHigher educationMathematics educationSociologyMedicinePolitical scienceSocial science

Abstract

fetched live from OpenAlex

In Malaysia, postgraduate coursework and research training have expanded significantly in attracting both domestic and international students from Southeast Asia and the Middle East. The task of evaluating the student learning experience in postgraduate education can point out to researchers and university educators various mismatches that would not be immediately known otherwise. In this study, 83 MA and MEd students in two public universities in Malaysia submitted written narratives to discuss their postgraduate learning experiences. Of this total, 10% of the respondents (12 postgraduate students) also volunteered to be interviewed. The findings of this qualitative study showed that the following dimensions impacted on students’ learning experiences: knowledge, values and contacts acquired, professional and personal values acquired and specific learning problems encountered. The implications of the results of this study suggest that public universities in Malaysia can take proactive steps to celebrate learner diversity when addressing students’ difficulties in their continuous effort to further enhance support and facilities for their postgraduate students.

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)0.011
metaresearch head score (Gemma)0.015
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0080.006
Scholarly communication0.0030.003
Open science0.0010.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.306
GPT teacher head0.634
Teacher spread0.328 · 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

Citations40
Published2009
Admission routes1
Has abstractyes

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