Bibliographic record
Abstract
This paper examines the application of adaptive gamification in digital learning environments to enable desired learning outcomes. Online learning has become more prevalent since the outbreak of the Covid-19 pandemic. However, remote learning lacks the external stimuli and face-to-face interaction essential to promote motivation, engagement, and collaboration (Walter & Woolery, 2021). There is a pressing need for a new model of education where students can learn and collaborate remotely. Even though an increasing number of literatures has shown the potential effect of gamification in promoting motivation and engagement among students, there is a lack of systematic frameworks that guide educators to apply gamified experience in their classrooms (Alhammad & Moreno, 2018). Using studies on gamification, motivation, and collaborative learning, this Major Research paper analyzes how adaptive gamification can be best applied in online learning and collaboration. Along with this MRP, a minimum viable product, Sandbox, was created. Sandbox is an online training platform that can be used to enhance collaborative learning in a wide variety of contexts and subject areas. This prototype illustrates the interplay of two core features used to enable engagement towards desired learning outcomes: (1) personalized gamified experience drawn from individual motivational affordance and (2) productive online collaboration frameworks that overcome the limitation of remote learning.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".