Towards Development of an Interactive Mobile Application for Teaching The UNSDG
Bibliographic record
Abstract
In aim of a better, inclusive, accessible, and safer future, educational institutions are committed to integrating the United Nations' 17 Sustainable Development Goals (SDG) into their curriculum design and course delivery.Traditionally, a plain literary review of these goals has been adopted by educators.This tends to leave students wondering what a realistic scenario would look like, and how they would approach an urgent call to action.To encourage thrive to learn and delve into action, a gamified reflective and immersive process would be more sought by learners instead of reviewing the definition of goals and their description without any tangible practice.To do so, The York University SDG Uphold (YU-SDG-UP) app was designed to immerse students into a world of those scenarios, where their responses are recorded and graded on an impact scale.This provides an interactive approach which is certain to influence the user's understanding of the SDG, and their attitude towards a sustainable, inclusive, diverse, and equitable future.This is accomplished through developing a mobile application hosting a virtual world with a global health score, where the user interacts with a scenario-based problem-solving framework.Scenarios are presented in text-based descriptions, and followed by a multiple-choice list of actions, all of which hold a weighted impact on the user's global health score.The user is intended to explore these scenarios with the objective of claiming the best possible score through their chosen actions, embracing practical education through trial and error.This activity is anticipated to surpass the traditional means of teaching sustainable development through slideshow presentations, or at least reinforce that knowledge through a virtual decision-making experience.Students can practice their problemsolving skills under realistic conditions and constraints, while understanding the significance of their decisions towards sustainable living.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.007 |
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".