Pengembangan bahan ajar hypermedia berbasis kelora dengan model quantum teaching siswa kelas IV SD di Kecamatan Pecangaan Kabupaten Jepara
Bibliographic record
Abstract
The purpose of this research is to analyze the needs of teaching materials, to design teaching materials, to analyze the effectiveness of the needs of teaching materials. The research method uses R&D. The resulting product is in the form of kelora-based hypermedia teaching materials with a quantum teaching model. The research and development steps refer to Borg and Gall. Data obtained from interviews, questionnaires, and tests. Data were analyzed by accumulating the number of scores. The effectiveness of learning material data was analyzed by normality test, homogeneity test, gain test, t test at a significance level of 0.05 using SPSS. The results of research and development are hypermedia teaching materials based on Jepara local wisdom with a quantum teaching model. This product proved to be feasible because the total scores obtained by the validators of teaching materials, materials and practices were 90.63; 90.27, and 90.25 “very feasible” criteria. The results of the responses from students were 88.89% and the responses of teachers in three elementary schools with an average of 88.24% with the product category "very feasible". The use of this product is effective in improving student learning outcomes. The average percentage of the pretest in the control and experimental classes was almost the same, namely 67.29 and 64.71. After being given treatment, the average posttest of the control and experimental classes increased to 75.53 and 89.41. The results of the t test obtained t value = 6.045 with a significance level of 0.000 < = 0.05.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".