Teachers’ Attitude towards Minimum Competency Assessment at Sultan Agung Senior High School in Pematangsiantar, Indonesia
Bibliographic record
Abstract
In order to replace all students in Indonesia, the minimum competency assessment is administered in 2021. The evaluation includes literacy, literacy and financial literacy. This study seeks to examine the attitude of teachers to the minimum assessment of competence or known as the minimum competence assessment (AKM). A descriptive qualitative method with a statistical method was used in this research. There were 34 teachers at Sultan Agung Senior High School in Pematangsiantar, Indonesia (SMA Sultan Agung). The participants therefore received questionnaires. Questionnaire statements distributed through Google form. The delivery of questionnaires via Google's Covid-19 form, which prevented the scientist from conducting face-to-face research with its participants. There were 12 items on the questionnaire given. There are 4 question items for each component. Overall, the results of the teachers' research attitudes towards the assessment of minimum skills achieved a maximum score of 60 and a minimum score of 12. After the data are analyzed, more teachers agree that in the implementation of the AKM they are looking for the issues themselves. There were 18 teachers (48.6%) in the group who agreed on the statement, which was a sharp contrast to those teachers who disagreed, i.e. (2.7 percent). The teachers therefore really want to know about AKM. With numerous references to AKM on the Internet, it helps teachers to practice AKM.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".