Model Innovation Capability in Learning Process of Indonesian University: Determinant Analysis of Innovation Diffusion Theory
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study examines the role of Innovation Diffusion Theory in enhancing the innovative behavior of lecturers in Indonesia’s higher education system. In the context of rapid changes due to the Indutsrial Revolution 4.0 and the implementation of the Independent Curriculum, the need for innovation in teaching methods is critical. This research focuses on Project-Based Learning (PjBL) and Problem-Based Learning (PBL) as key innovative approaches. A total of 233 lecturers participated in the study, which employed structural equation model (SEM) to analyze the relationships between variables. The model is based on adaptation of the Theory of Planned Behavior (TPB), incorporating antecedents such as Image, Compatibility, Result Demonstrability, Voluntariness, and Visibility. The findings indicate that the developed structural model meets Goodness of Fit criteria, demonstrating strong influences from Visibility and Voluntariness on behavioral intention. However, other variables showed no significant impact. This research highlights the importance of fostering a supportive environment for innovation in higher education, suggesting that enhancing lecturers' motivation to innovate requires addressing both personal and institutional factors.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| 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 | 0.000 |
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 it