The Impact of Using Articulate Storyline Media on Elementary School Students' Social Studies Interest and Learning Outcomes
Bibliographic record
Abstract
This research aimed to determine whether there was an influence from the use of Articulate storyline media on the interest and learning outcomes of social studies for class V students at SDN 01 Klopoduwur, Blora Regency. This type of research is quantitative, that was experimental research using a one group pretest-posttest research design where an experiment in its implementation only involves one class as the experimental class. The independent variable in this research is Articulate storyline media, while the dependent variable is students' interests and learning outcomes. The sample used was 30 class V students. The data collection techniques used are observation, questionnaires and tests in the form of multiple choices whose validity has been tested. The data analysis used is descriptive analysis techniques and inferential analysis techniques using simple linear regression tests. The results of descriptive statistical analysis showed that the average student pretest score is 57.73 and has increased after being given treatment with an average score of 84.53. The results of the inferential statistical analysis of Articulate storyline media on interest in learning by testing showed a significance value of 0.015 <0.05. Meanwhile, the significance value of Articulate storyline media on learning outcomes is 0.000 <0.05. It concluded that the Articulate storyline media has a significant influence on interest and learning outcomes in material on the characteristics of Indonesia's geographical location for class V students at SDN 01 Klopoduwur, Blora Regency. Keywords: Articulate storyline, Learning Interest, Learning Outcomes
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".