Bibliographic record
Abstract
In the evolving landscape of digital technologies, gamified learning has been increasingly recognized for its potential in the educational realm, particularly within the domain of environmental education. By intertwining game design principles with educational objectives, new pathways for disseminating and enriching environmental education have been established. Despite its promise, existing gamification strategies in environmental education have been observed to exhibit limitations, notably in neglecting the temporal granularity variations in group behavior of students and adopting a singular perspective on learning behaviors. This study is chiefly anchored on two focal points: task recommendation within the gamification of environmental education and game progression adjustments tethered to adaptive learning outcomes. Through the integration of the attention mechanism and bidirectional long-short-term memory (Bi-LSTM) neural networks, predictions related to students’ dynamic preferences in gamified learning behaviors have been refined. Consequently, more tailored game task recommendations have been made. Furthermore, the dynamics of adjusting game progression contingent on students’ learning outcomes have been extensively analyzed. The insights garnered from this investigation provide critical theoretical foundations and pragmatic instruments for the nuanced employment of gamification within the environmental education context.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.904 | 0.857 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".