Open educational resources in Malaysia
Bibliographic record
Abstract
Open educational resources (OER) are a relatively new phenomenon in the Malaysian higher education (HE) sector. Although there have been “lone rangers” strongly advocating the use of OER in the country, many HE institutions, including Wawasan Open University, Open University of Malaysia and Asia e University, are yet to make use and reuse of OER a mainstream practice. There also seems to be reticence over making content freely available to the nation or the region, as well as an absence of policy directions. Notwithstanding, some of these institutions, urged on by individual staff, are taking a serious look at adopting an institutional policy on OER and digital resources. A prime example of this new movement is the OER-based, self-directed open and distance learning course material developed by Wawasan Open University as a pilot project leading to an institutional policy on the use and reuse of OER. Under a grant from the International Development Research Centre of Canada through an umbrella study on Openness and Quality in Asian Distance Education, a team of collaborators from various Asian countries developed an extensive survey instrument to identify the Asian landscape of digital resources and OER. In Malaysia, the instrument was officially made available to 15 public, private not-for-profit and private for-profit HE institutions. A total of 43 valid responses were received from individuals who are using digital resources/OER, as well as institutional authorities who commented on the institutional stand on OER. This report summarises the findings from the survey responses gathered from Malaysia and provides an overview of the Malaysian HE landscape with respect to digital resources and OER use.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".