Using App Inventor as Tool for Creating Mathematics Applications for Mobile Devices with Android OS
Bibliographic record
Abstract
People are programming on their personal computers since the 1980s, but today's mobile applications are making computing as “personal” as never before. Never in the history of the use of technology in education has there been a technology so widely available to citizens as mobile technology. According to this trend, we are moving into a new era of consuming information via mobile computing, one that promises greater variety in applications, highly improved usability, and networking. Our consuming information culture gives us all sorts of opportunities for entertainment, even learning using high-tech devices, which are considered to be black boxes to most of us, because only few people can create applications. Even though, more and more students are interested in developing their own mobile applications. In this context, we present our experience with App Inventor - a user friendly platform and drag-and-drop block language to create Android apps. The present paper is an intro to the creation process for an applied mathematics app, started 4 years ago, corresponding to the requirements of our classroom consuming information culture.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.015 |
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".