Effect of Youtube Media Combine with Mind Mapping on The Material of Plant Tissue Structure and Function on MAN 2 Pati Student’s Knowledge
Bibliographic record
Abstract
This research is motivated by the importance of using effective learning media to increase students' knowledge. YouTube media, a combination of, is one of the media that can train imaginative power and play an active role in learning activities so as to develop students' knowledge. The purpose of the study was to determine the use, increase the quality of knowledge, and the effect of YouTube media combined mind mapping on students' knowledge of the structure and function of plant tissue. This research is a quasi-experimental research design with the matching only posttest only control group design. The population of this research is the students of class XI MAN 2 Pati totaling 189 students. The research sample was selected using a cluster random sampling technique, namely class XI IPA 1 and XI IPA 2. The research data was collected using tests and documentation. The data analysis technique used to test the hypothesis using the Mann Whitney test. The results showed that the use of youtube media combined with mind mapping in the experimental class had a good influence on students' knowledge of the structure and function of plant tissue with an average posttest value of 82.55 for the experimental class and 56.37 for the control class, indicating an influence on the quality of students' knowledge. on the structure and function of plant tissues. The results of the analysis of the hypothesis test obtained a significance value of <0.05 (0.000 <0.05) so it can be concluded that there is an influence of youtube media with a combination of mind mapping on students' knowledge of the plant tissue function and structure. Keywords : Youtube media, Mind Mapping, Plant Tissue Knowledge
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".