Learning optimization: Video learning media creation training using the Canva platform for kindergarten teachers
Bibliographic record
Abstract
The development of information and communication technology (ICT) has had a significant impact on education, including early childhood education (PAUD). In today’s digital era, technology-based learning media such as video are effective in capturing children’s attention and helping them understand material in a fun, visual, and accessible way. However, the use of technology at the PAUD level still faces challenges, particularly limited infrastructure and teachers’ low digital skills in areas such as graphic design and video editing. This community service activity aimed to improve PAUD teachers’ competence in utilizing technology through training on creating video-based learning media using the Canva platform. The main problem identified was the limited use of digital media in the learning process, largely due to a lack of training and low digital literacy among teachers. A five-day intensive training was conducted with 10 teachers from Hangtuah Hamadi Kindergarten and Kartika VI-2 Persit Entrop Kindergarten in Jayapura City. The training materials included an introduction to Canva Edu, video creation practice, presentations, and classroom implementation. Evaluation results showed the training was effective, demonstrated by active participation and the production of videos suited to early childhood characteristics. This activity fostered teacher innovation and contributed to the digital transformation of early childhood education.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".