Peningkatan Hasil Belajar Matematika Melalui Pendekatan Teaching at The Right Level Berbantuan Papan Musi
Bibliographic record
Abstract
This research aims to improve student learning outcomes in Mathematics subjects using the TaRL (Teaching at The Right Level) approach assisted by Papan Musi media. This approach is an approach based on the level of students' abilities. This learning considers the needs and characteristics of each student. The goal is to create fun learning so that all students can achieve the expected learning goals. The subjects of this study are 32 students (15 male students and 17 female students) of class V. This classroom action research is reflective and collaborative. The implementation is carried out for two cycles. The results of this study show that the application of the TaRl (Teaching at The Right Level) approach assisted by a prayer board can improve the learning outcomes of Mathematics students in grade V of elementary school. This can be seen from the increase in the percentage of the average value of the observation results in cycle 1, namely the completeness value of 51% and the average value of 73%. Of the 32 students, 17 students have achieved KKM scores. There was also a very good increase in cycle 2 with a percentage of 85% and the average score in this cycle reached 87.5%. There were 28 students who improved their learning outcomes. So, using the Teaching at The Right Level learning approach with the help of the musi board media in Mathematics learning has an impact on improving the learning outcomes of grade V students of SDN 1 Mataram.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".