The Impact of Merdeka Belajar Kampus Merdeka (Emancipated Learning) and Motivation on Students’ Learning Outcomes in Higher Education in Indonesia
Bibliographic record
Abstract
The implementation of Merdeka Belajar Kampus Merdeka (MBKM/Emancipated Learning) with a form of learning activity that is more involved in community life and a hybrid learning model is very likely to influence student learning outcomes. This study explores the impact of implementing the MBKM program, which emphasizes a learning approach integrated with community life and adopts a hybrid learning model. The objectives of this study are to analyze the determinants of students’ learning outcomes in the context of MBKM through hybrid learning and assess factors that affect student learning outcomes in the same learning model. The methodology used was a quantitative survey design. These research steps involved questionnaires that had been proven valid and reliable for data collection. The collected data was then analyzed using descriptive and inferential statistics, with path analysis techniques to understand the relationship between the variables studied. The results reveal that 1) The learning model has a significant influence on student learning outcomes; 2) Learning motivation has a significant effect on learning outcomes; 3) MBKM structure contributes significantly to learning outcomes; 4) The learning model has a significant effect on learning motivation, and 5) MBKM structure affects student learning motivation significantly. This research provides important insights into the effectiveness of emancipated learning (MBKM) and hybrid learning in higher education in Indonesia.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".