Educational Game “Mengenal Indonesia” as a Medium to Introduce Indonesia to the Kids
Bibliographic record
Abstract
Educational game is a game especially for the kid, that contains educational materials designed in an interactive game that aims to improve intelligence as well as providing knowledge to the users. The opportunity to make android-based education game is open wide, especially the game that exploit local content. In this research, we will develop the education game that has the main goal to introduce Indonesia to the learners. Games that will be built is android-based game that explore the knowledge or specific fact about certain region or provinces in Indonesia, so it will called as “Mengenal Indonesia”. This game is expected can act as a learning medium to introduce about Indonesia to the student, in interesting way. The game also be equipped with quiz about specific fact on the region that selected by the player. The game is built through ADDIE model as an approach. ADDIE (Analysis, Design, Development, Implementation, and Evaluation) is an approach that common use in design a learning content. The tools that used to build the game is FlashDevelop and using ActionScript 3.0 as programming language. After the game built, we conduct the survey to test the respond of the user. The result show that most of the user feel “Mengenal Indonesia” can help them improving knowledge about Indonesia. Therefore, it is necessary to build the game that has the content the knowledge about Indonesia. This paper will discuss the approach, design and making of educational game that contains information about Indonesia area. This content is selected because currently there is no educational game with a similar content available in the market and this content can be act as additional resource on the Social Sciences subject at elementary school level. The game, we call as “Mengenal Indonesia”, previous name is “know your country”, focusing to introduce some unique aspect about specific region in Indonesia. The source of knowledge we take from the book “RIPUT” (Ringkasan Pengetahuan Umum), that contains common knowledge about Indonesia, such as the longest river, the highest mountain, the fact about certain place in Indonesia, and many more. The game was built using ADDIE approach, that consist 5 steps: analysis, design, deployment, implementation, and evaluation. The game was built using FlashDevelop and ActionScript 3.0 as language script.
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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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".