Teacher Readiness Factors that Influence the Implementation of the Merdeka Curriculum in Elementary Schools
Bibliographic record
Abstract
The Merdeka (Independent) Curriculum is a crucial element for the sustainability of education in Indonesia. Teachers need to have significant readiness to ensure implementation runs optimally. However, many teachers still require clarification and help to understand and need help integrating the Merdeka Curriculum with existing conditions. The objective of this study is to examine the key elements that affect teacher preparedness and how they impact the implementation of the Merdeka Curriculum in elementary schools. This research employs a quantitative methodology with an ex post facto design. Purposive sampling was used to choose a population and sample of elementary school teachers in Jakarta, Indonesia. The sample size consisted of 122 teachers. Data collection uses a questionnaire to obtain data related to the variables in this research. The data analysis employed structural equation modeling (SEM) with the SMART-PLS 3.0 software tools. The research findings indicate that a significance value of 0.000 (p < 0.05) suggests that teacher preparedness characteristics play a crucial role in positively and significantly impacting the implementation of the Merdeka Curriculum in Elementary Schools. This study emphasizes the significance of teacher preparedness in multiple dimensions, such as a profound comprehension of the Merdeka Curriculum, the capacity to incorporate it with current circumstances, and sufficient backing from the school environment and community.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".