HyFlex Learning Ecosystem with Social Emotional Learning
Bibliographic record
Abstract
The model of the HyFlex Learning Ecosystem is with social emotional learning to enhance digital emotional intelligence. The concept is based on the integration of digital learning ecosystems. HyFlex Learning and social emotional learning this research has the objective (1) To study and synthesize the conceptual framework of The HyFlex Learning Ecosystem with Social-Emotional Learning to Enhance Digital Emotional Intelligence. (2) To develop model of The HyFlex Learning Ecosystem with Social Emotional Learning to Enhance Digital Emotional Intelligence. (3) To study the suitability of The HyFlex Learning Ecosystem with Social Emotional Learning. Research hypothesis: The suitability of the model of The HyFlex Learning Ecosystem with Social Emotional Learning is at a very high level. The participants in this research include seven experts from various institutions, all of whom are specialized in the design and development of instruction models and instruction systems. The results, which are in consistence with the expectation of the researchers, show that (1) This research can serve as a guideline for developing a flexible integrated learning ecosystem that can enhance digital emotional intelligence, consisting of a 6-step social emotional learning process, integrated with the digital learning ecosystem and HyFlex Learning. (2) the overall suitability of the development to the model of the HyFlex Learning Ecosystem with Social Emotional Learning to Enhance Digital Emotional Intelligence (Overall composition) It is at a very high level (Mean = 4.98, S.D. = 0.06, IR = 0.00, Q.D. = 0.00), and (3) Overall, In conclusion, The results of the evaluation certify the suitability of using the model of HyFlex Learning Ecosystem with Social Emotional Learning to Enhance Digital Emotional Intelligence is suitable for actual use at a very high level (Mean = 4.79, S.D. = 0.57, IQR = 0.00, Q.D. = 0.00).
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".