Components of computational thinking in citizen science games and its contribution to reasoning for complexity through digital game-based learning: A framework proposal
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Education has undergone many changes in teaching and learning, intensified by the significant technological developments that have responded to the fourth industrial revolution and other emergent situations. In this context, developing information and communication technologies has become vital in supporting new ways and learning models in the various educational levels to address a complicated environment where individuals must have complex and computational skills to respond to challenges. This study proposes a complex thinking framework that links citizen science and digital game-based learning to develop university students’ computational thinking skills. The results indicate that (a) it is possible to consider the sub-competencies of complex thinking in the design of a digital citizen-science game to develop computational thinking, and (b) the digital game-based learning framework for citizen science topics can potentially increase students’ engagement and teamwork in data collection and analysis while building their knowledge and computational thinking skills, and their complex thinking competency and sub-competencies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it