Teachers’ Attitude towards Minimum Competency Assessment at Sultan Agung Senior High School in Pematangsiantar, Indonesia
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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