Adaptive Micro-Learning Model Based on Dhamma Using Mixed Reality to Develop Students to Be Good Citizens
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
The COVID-19 pandemic forced school closures globally, leading to significant learning regression in academic performance, skills, and ethical development. This study aims to: 1) synthesize and develop an adaptive micro-learning model based on Dhamma principles using mixed reality (MR), 2) compare pre-and post-test results, and 3) assess the model’s impact on students’ good citizenship. Participants included 19 experts and 39 Grade 6 students. The methodology involved synthesizing and developing an adaptive micro-learning model, comparing pre- and post-study scores, and evaluating academic achievement and good citizenship development. The study identified seven key steps in the adaptive micro-learning model: 1) testing prior knowledge (Dhammannuta), 2) reporting prior knowledge results (Atthanyuta), 3) explaining learning objectives (Attanyuta), 4) outlining the learning path (Mattanyuta), 5) video-based learning (Kalanyuta), 6) collaborative learning via MR (Parisanyuta), and 7) peer knowledge exchange (Pukkalanyuta). The model’s effectiveness was rated highly (x̅ = 4.78, S.D. = 0.34). Students’ good citizenship scores significantly improved, increasing from a pre-test average of 15.87 points (52.90%) to a post-test average of 25.72 points (85.73%), with statistical significance at the 0.01 level.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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