The curriculum planning and implementation for mindfulness education and diversified humanism based on big data
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
In modern education, balancing academic achievement and personal development is crucial. Traditional methods often focus more on academics, neglecting personal and social skills. To address this, the study combines mindfulness education with humanistic theory to improve curriculum design. Mindfulness education emphasizes attention control, self-awareness, and emotional management. Humanistic theory focuses on student-centered learning, creativity, and self-fulfillment. This study uses quantitative research to compare different teaching methods. The research findings show that traditional teaching methods, while effective in improving students' academic performance, have limited impact on the development of their personal and social skills. In contrast, the optimized curriculum not only significantly increases students' classroom participation and interest in learning but also greatly enhances their self-confidence, sense of responsibility, teamwork, and creativity. Particularly in terms of focus and empathy, students made more noticeable progress. This indicates that the optimized curriculum helps improve students' self-awareness, emotional management, and collaborative skills. Overall, the study highlights the potential of comprehensive curriculum planning in promoting students' holistic development and emphasizes the importance of student-centered teaching approaches in cultivating learners with more integrated qualities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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