Teacher Reform in Indonesia: The Role of Politics and Evidence in Policy Making
Bibliographic record
Abstract
With close to three million teachers, Indonesia has one of the largest and most diverse cadres of teachers in the world. The evolving nature of its education system and the increasingly complex challenges facing individual teachers and the teaching profession as a whole are of immense importance to the nation’s future development. In 2005 the Indonesian government approved a comprehensive Teacher and Lecturer Law that radically reformed the nation’s teacher management and development process. This book explores this uniquely comprehensive reform by focusing on the nature of Indonesia’s teaching profession before and after the Teacher Law; the educational and political economy context of the law; the structures, strategies, and processes that arose from the law including a comprehensive system of teacher appraisal and salary increases which effectively doubled the income of certified teachers; the political and economic factors which distorted the reform process; its impact on teacher knowledge, skills, and motivation and student outcomes; and the (in)efficiencies derived from the reform in terms of the system’s financing and the distribution of its teachers. This book’s framework promotes an approach to teacher reforms through improving the nature of recruitment into the profession; pre-service education; induction, mentoring, and probation; formal certification; continuing professional development; teacher performance appraisal; and ongoing career development. It should therefore be of particular interest to Ministries of Education and development agencies contemplating similar comprehensive reforms. The lessons and recommendations from this analysis include the following: • The doubling of teacher income has increased the status of the teaching profession and attracted better candidates to apply to teacher training institutions. • The mere fact of certification and the consequent doubling of teacher income have not achieved the better teaching and learning that was expected. • A quality assurance framework needs to be put in place from the beginning of any reform process. • The costs of extending the certification program to all teachers are associated with significant trade-offs within the education sector. Estimates suggest that spending on teacher compensation will need to absorb a much larger share of the education budget and require reductions in spending in other areas.
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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 | 0.085 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.007 | 0.031 |
| Scholarly communication | 0.040 | 0.035 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.013 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".