From product to process data: Game mechanics for science learning
Bibliographic record
Abstract
Game-based learning environments (GBLEs) supplement classroom instruction so students can demonstrate their scientific reasoning abilities and increase knowledge, providing a platform that promotes interest and engagement in science. The goal of this study was to examine the effectiveness of game mechanics for science learning. This study identifies how two types of game mechanics—learning and assessment mechanics—are used by high school participants (N = 137) as they learn about microbiology with Crystal Island, a game-based learning environment for science education. Participants’ learning outcomes were evaluated in two ways: learning gains, which assessed participants’ domain knowledge acquisition, and game completion, which assessed participants’ ability to successfully demonstrate scientific reasoning abilities. Results from this study showed that game completion is not related to learning gains. However, as participants engaged with increasingly more assessment mechanics, learning gains decreased. Further, profiles of learners were extracted to better understand the learning process that best supports greater learning outcomes. Results showed that learners who engaged in less recurrent transitions across assessment mechanics were more likely to successfully demonstrate scientific reasoning abilities. Implications for the design of games which provide scaffolding based on process data of learners’ game mechanic use are provided.
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.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".