Evaluation of a Project to Develop Learning Management Competency Using Digital Technology Among Teachers in a Bangkok School in Order to Facilitate the Learning Loss Recovery of Basic Education Level Students: Applying Kirkpatrick’s Concepts and Model
Bibliographic record
Abstract
The spread of the coronavirus (COVID-19) strongly affected educational management in Thailand. This gave rise to the problem of how to improve the quality of students of all ages through 100% online learning, be this in areas of knowledge, abilities, skills, and attitudes towards learning. Therefore, the Secretariat of the Education Council of Thailand studied the learning loss of basic education students during COVID-19 and recommended ways to solve the problem using the seven measures derived from the RECOVER Model. Based on this, the researchers devised a project and conducted it along with agency administrators and school administrators under Bangkok Metropolitan Administration, Bang Khen District. Their objective was to evaluate the development of learning management competency using digital technology among teachers in a Bangkok school in order to facilitate the learning loss recovery of basic education level students. The results revealed that participants responded strongly to the overall project process and activities (Mean = 4.71, S.D. = 0.57) with the level of knowledge developed from the relative gain score at a high level (GS = 72.12) and 75.00 percent had the ability to create educational Line stickers for sale in the Sticker Shop. In addition, participants’ behavior changed as a result of applying the knowledge, abilities, and skills acquired to teaching and learning in their own subjects. Consequently, the schools to which they are affiliated were able to facilitate the learning loss recovery of students extremely well through the combined participation of administrators, teachers, students, and parents.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".