Upaya penguatan kompetensi guru TPQ se-Kecamatan Mijen Kabupaten Demak melalui pelatihan evaluasi pembelajaran
Bibliographic record
Abstract
The rapid development of Al-Qur'an Education Parks (TPQ) today is both a blessing and a challenge. The blessing is that many TPQ institutions will produce a Qur'anic generation. However, the challenge is how these institutions can produce a Qur'anic generation if they are not balanced with improvements in quality, both in terms of institutional management and teacher quality. The objective of this activity is to enhance the competencies of TPQ teachers in managing learning evaluations. The method used is Participatory Action Research (PAR) following Vincent II’s 9-step process: team formation, goal formulation, stakeholder identification, needs assessment and analysis, priority setting, preparation, implementation and monitoring, review and evaluation, and determination of new needs and objectives. The target of this activity is for TPQ teachers in Mijen Sub-district to be able to create effective learning evaluation administration. The results of the community service indicate that TPQ teachers can appreciate the importance of learning evaluation and are capable of creating standardized assessments using student achievement records. The implementation of this competency enhancement activity received a positive response from TPQ teachers, allowing for the development of future activities that can be more varied.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".