Multimedia Mobile Learning Application for Children’s Education: The Development of MFolktales
Bibliographic record
Abstract
Children learn from what they see and hear. One of the attractive applications is animation story and these children are exposed to various types of animation story. However, not all animation stories presented are suitable for children’s education in terms of exaggeration elements applied in animation. Furthermore, the existence of mobile application does not emphasize the touch gesture that is suitable for children’s age. Hence, there is a lack of mobile learning applications with education-oriented environment for children’s education. Therefore, there is a need for research to develop a well-designed mobile application with suitable exaggeration elements together with good story plots and socio-cultural values to educate as well entertain children. This paper discusses the design and development of Malay folktales mobile application called MFolktales based on a local Malay folktale story. MFolktales is an Android-based application and it was developed based on the validated conceptual model as well as analyzed and defined design principles and requirements. This paper presents the development process of MFolktales application. The development life cycle was adopted from ADDIE Instructional Design (ID) model, taking into consideration the animation development process of pre-production, production, and post-production. Overall, there are five phases involved in the development life cycle: analysis, design, development, implementation and evaluation. The application was tested to strengthen its functionality and usability. The result shows that MFolktales application is ready to be tested to real users and ready to be commercialized.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".