Gamification of Formative Feedback in Language Arts and Mathematics Classrooms
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
The use of computer games in education has been increasing in popularity during the past decade. Game-based learning environments are designed to teach specific knowledge content and skill-based learning outcomes using game elements. One main reason for using game-based learning environments is to increase student motivation and engagement while teaching learning outcomes. Many of the game-based learning environments are designed so that students will reach maximum flow, which is defined as students being so completely immersed in that game that they do not notice that they are learning. These learning environments have been shown to improve many behaviour and cognitive learning outcomes. While game-based learning has many benefits, some educational researchers have indicated that it is often very costly to develop a complex game-based assessment to teach a few learning outcomes. Hence, in some cases it is more beneficial to approach the use of computer games in education using gamification.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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