Implementation of design based learning for the development of SDGs educational games
Bibliographic record
Abstract
Education on sustainable education (ESD) is gaining momentum to ensure that SDGs are met by 2030. The educational institutions have significant role in fostering ESD. However, there is lack of educational resources to be used for ESD. Particularly, teaching the concept of SDGs needs an attention grabbing and engaging approach and Design Based Learning (DBL) holds much potential. The main objective of this investigation was to describe the development of SDGs education resources i.e., SDGs educational games using DBL approach. Besides, the generic skills of the students during DBL were assessed during game development phase. The outputs of DBL were Bingo Mat game, Carrom board game and Sugoroku game. These games were validated for their effectiveness as resource for teaching and learning SDGs. The results revealed the positive impact on the generic skills of students through DBL during game development phase. Moreover, the response results of the players highlighted that carrom board game offered them the gaming experience while Bingo and Sugoroku offered them learning experience. Another important finding of this study is the need to teach SDGs from the younger age as the level of education had significantly impacted on their knowledge about SDGs. The results of this study will contribute to the domain of ESD by articulating an alternative pedagogy of integrating DBL with SDGs as invigorating educational resources and faculty development method.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".