Developing an Instrument and Assessing SDGs Implementation in Indonesian Higher Education
Bibliographic record
Abstract
This study aims to develop instruments to measure the implementation of the Sustainability Development Goals (SDGs) in higher education institutions in Indonesia, focusing on the 17 United Nation's SDGs agendas.Further, the developed instrument was used to evaluate the implementation of SDGs in higher education institutions.Considering the resource limitations in Indonesian higher education institutions, the study aims to identify and prioritize SDGs agendas that are effective and suitable for implementation.Hence, higher education institutions in Indonesia can gradually enhance the implementation of SDGs agendas by prioritizing the most efficient and suitable ones.This study used a quantitative research approach.The sample included 118 private higher education institutions in Indonesia.This study utilized various analysis techniques including exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) to assess the validity of the measurement items.The SDGs implementation was evaluated by statistical process control (SPC) analysis and illustrated with Pareto diagrams.The effectiveness of SDGs agendas was examined using an Importance-Performance Map Analysis matrix.The findings demonstrated that the SDGs measurement item accurately evaluated the implementation of SDGs.The overall level of SDGs implementation was determined to be moderate, indicating potential areas for improvement in higher education settings.The study identified 2 the high priority agendas, 13 medium priority agendas and 2 low priority agendas that require immediate attention and improvement.These findings contribute to the existing knowledge on sustainable development and offer valuable insights for policymakers and higher education institutions in Indonesia.The study also emphasizes the importance of standardized sustainability reports to enhance transparency and accountability in the higher education sector.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".