“Inner Engineering” for success—A complementary approach to positive education
Bibliographic record
Abstract
The movement of positive education is growing globally. Positive education aims to balance academic skills with skills of wellbeing. This study introduces the “Inner Engineering” methodology and evaluates its impact on promoting wellbeing and flourishing for college students. Based on the science of yoga, the Inner Engineering methodology comprehensively addresses four major dimensions of human experiences—physiological, cognitive, affective, and energetic experiences and offers methods and processes to optimize wellbeing in all of these dimensions. The study design involves a quasi-experimental one-group with pre- and post-course tests. Participants of the study (n= 92 students) completed both the pre- and post-course surveys. The pair-wiset-test results showed significant improvement in wellbeing (mindfulness, joy, vitality, sleep quality, and health) and flourishing in the academic setting (academic psychological capital, academic engagement, and meaningful studies) and in life (meaningful life) among students who successfully completed the course. These findings suggest that the academic curriculum may be balanced by integrating the yogic sciences of wellbeing which address a more complete spectrum of human experiences as a whole person. This, in turn, has a further effect on flourishing academically and in life. Future studies may involve a larger sample size with a comparison group or a randomized control and a longitudinal follow-up.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".