Comment on “Transforming Malaysia's Higher Education: Policies and Progress”
Bibliographic record
Abstract
Investment in education is right as regarded one of the most important strategies for social and economic development in the long term. Historically, the private and social returns to schooling are higher at the primary and secondary levels compared to the tertiary level. However, as countries become more developed and wealthier, the returns to tertiary education could exceed that of primary education (Psacharopoulos and Patrinos, 2018). As Malaysia heads towards graduating from middle-income status, human capital development is a key policy the country's Twelfth Malaysia Plan 2021–2025 (12MP, see Malaysia, 2021). For tertiary education, the goal is to improve the quality of universities. In this regard, Tham and Chong (2023) provide a timely assessment of the quality and policy challenges to improve the quality of higher education in Malaysia. A key policy challenge highlighted by Tham and Chong is how to measure the quality of higher education. As they rightly point out, the official performance indicators used such as use of selective international university rankings are clearly problematic given that the paradox of high rankings juxtaposed with under-employment of university graduates. Furthermore, they also opine that the implementation of quality assurance and accreditation systems have not ensured the quality of teaching in universities. In reflecting the challenges facing higher education in Malaysia, it is worth examining some of the key institutional and political factors as well as constraints that have shaped the trajectory of the sector in Malaysia. These include the impacts of affirmative action policies (the New Economic Policy [NEP], and its successor variants) in both student intake and faculty hiring in public universities. Ethnic (Bumiputra vs. non-Bumiputra and regional [Peninsular vs. Sabah and Sarawak]) dimensions continue to be emphasized in the 12MP. In addition, the effects of the use of Bahasa Malaysia (Malay language) as the medium of instruction at all levels of education continue to be debated. In the past, this language requirement in teaching had also limited opportunities for hiring foreign academics. The liberalization of higher education in the 1980s was partly undertaken in response to such policies. Furthermore, the subsequent internationalization of higher education created more space for the role of market forces which mitigate some of these policies. Although the emergence and development of private higher education have invigorated higher education in Malaysia, it has created a dualistic system that raises issues related in inequality. The issues of quality and inequality are intertwined. More studies are needed on the quality gap between public and private higher education in Malaysia and the extent to which it has exacerbated inequality. This has also implications of social cohesion. Needless to say, the quality of higher education in Malaysia greatly depends on the quality of primary and secondary students. The performance of Malaysian students (15 years old) in the Programme for International Student Assessment (PISA) tests indicate that their performance is lower than expected given its income level (World Bank, 2019). Any assessment of the quality of higher education outputs will need to consider the quality of the student intake (ex-ante). Thus, the transformation of higher education will require reforms of primary and secondary education as a necessary pre-condition. Finally, any assessment of the quality of higher education needs to consider whether universities are producing graduates and research that meet industry needs. The phenomenon of unemployment and underemployment amongst domestic graduates suggests this is a serious problem. In upgrading the pool of skilled workers in Malaysia, the Malaysian government should also re-examine the potential benefit of facilitating the absorption of skilled foreign students educated in Malaysian universities. This would be part of a required re-orientation of the country's labor policies which are currently in favor of importing relatively low-skilled workers. Such policies also need to address the long-term structural changes in the Malaysian economy in terms of the rise of services, the relative decline of the manufacturing sector, global value chain participation, and the digital transformation. These and other issues need to be addressed for another reason—tackling the brain drain problem that involves the emigration of skilled Malaysian workers to more developed economies such as Singapore, Australia, Canada, the United Kingdom, and the United States.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".