Prediction Analysis of Literacy Numeracy and Technology Adaptation Abilities of Students Who Participate in Teaching Campuses Using the KNN Algorithm
Bibliographic record
Abstract
Literacy and numeracy skills, technological adaptation carried out by students during the Ministry of Education and Culture's campus teaching program are influenced by the limited competencies possessed by students which are not in accordance with the study program and learning can be influenced by location, network and distance to school, influencing independent campus program activities which are less effective. and efficient in teaching. So the learning and teaching process when students are on site inspection is primarily and foremost when students carry out observations at the target school. Literacy is the process of training students in the knowledge of reading techniques. Numeracy is the process of training students in knowledge of counting techniques and technological adaptation which plays a very important role in influencing digital literacy and numerization. Students and teachers still experience difficulties in the field of hardware technology and many still have low knowledge in carrying out and implementing technological adaptation in schools. with location, network and distance for schools in remote areas. Higher education greatly influences the teaching competence of students who take part in campus teaching programs. So students carry out literacy, numerization and technology adaptation programs according to their study program. Assist the campus in analyzing the campus teaching competency of the Ministry of Education, Culture and Research and Technology's campus teaching program using the K-Nearst Neighbor algorithm. By predicting the level of teaching competency, students in the campus teaching program can know the teaching competency abilities of students who take part in the campus teaching program . Based on testing using 35 test data, the value K = 3 predicts the level of teaching quality and competency so that the system accuracy is 75%, proven by testing the Python programming language system.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".