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
Gamification, or the use of game elements in non-game contexts, has become a popular and increasingly accepted method of engaging learners in educational settings. However, there have been few comparisons of different kinds of courses and students, particularly in terms of discipline and content. Additionally, little work has reported on course instructor/designer perspectives. Finally, few studies on gamification have used a conceptual framework to assess the impact on student engagement. This paper reports on findings from evaluating two gamified multimedia and social media undergraduate courses over the course of one semester. Findings from applying a multidimensional framework suggest that the gamification approach taken was moderately effective for students overall, with some elements being more engaging than others in general and for each course over time." Post-term questionnaires posed to the instructors/course designers revealed congruence with the student perspective and several challenges pre- and post-implementation, despite the use of established rules for gamifying curricula.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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