Development of Learning by E-Learning System: A Case of Mahamakut Buddhist University, Mahavajiralongkorn Rajaviyalaya Campus
Bibliographic record
Abstract
This research aimed to develop learning by e-learning system in Mahamakut Buddhist University, Mahavajiralongkorn Rajaviyalaya Campus. The method used in this study was Participatory Action Research that consisted of two cycles of planning, practice, observation, and reflection during two semesters in the academic year 2020. Twenty-one teachers and forty students were voluntarily involved with the desired development and participated in this research. The three expectations from the development outcomes were: 1) the improvement under the identified indicators, 2) the researcher, the research participants, and the campus learned from practice, and 3) knowledge gained from practice will benefit continuous improvement in the future. The research findings illustrated three following aspects. Firstly, in both Cycles 1 and 2, the means of post-practice evaluations were higher than the means of pre-practice evaluations in the following programs; e-learning system development, meditation practice learning development, and teacher's skill enhancement for creating online media. Secondly, the researcher, the research participants, and the campus learned the following common aspects: an awareness of the importance of participation, being an all-the-time learner, and transcribing lessons from practice which was previously often neglected. Finally, the knowledge gained correlates with Kurt Lewin's Force-Field Analysis which consists of the following concepts: 1) Expected change, 2) Driving factors for change, 3) Resistance to change and 4) Overcoming resistance. Each component defines a set of thoughts and beliefs that Mahamakut Buddhist University, Mahavajiralongkorn Rajaviyalaya Campus, will implement as a basis for reviewing and strengthening an additional set of ideas and beliefs.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".