The Effect Of Learning Media Management And Self-Motivation On Science Literacy Ability
Bibliographic record
Abstract
The basis of this paper is the teacher's ability to manage the learning media used during learning because it is an important component in delivering teaching materials. Learning objectives can be effectively achieved if teachers can manage the media needs of each teaching material and use it appropriately. The post-pandemic learning process is felt to be less meaningful and tends to be boring for students, so teachers must be able to innovate in choosing and creating learning media that can motivate students to learn. This study aims to determine the benefits and effects of using "KARCIL" media in improving the science literacy of students of Madrasah Tsanawiyah. The focus of the paper lies in the use of "KARCIL" media in Natural Sciences (IPA) subjects with a quantitative approach of the field research model through interviews and observations and the distribution of questionnaires to students used as samples. The results of the study obtained were (1) There was a significant influence between the use of "KARCIL" media on science literacy skills students, amounting to 0.001 with a confidence level of 10%, (2) There was a significant influence between self-motivation and science literacy ability of 0.011 with a confidence level of 10% and (3) There is a significant influence between the value of the coefficient of determination for the Nagelkerke model of 64% and 36% influenced by other factors that are not studied. From the results of the study, it can be concluded that good management of learning media and appropriate use by teachers can improve students' literacy ability and learning achievement.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".