Science at Play: Assessing Learner Agency in a Game-Based Virtual Inquiry Environment
Bibliographic record
Abstract
Game-based learning environments (GBLEs) rarely combine support for learner agency with rigorous, embedded assessment. This study reports on the design and pilot implementation of ALIVE (Agency for Learning in Immersive Virtual Environments), a virtual inquiry environment that integrates the Agency for Learning (AFL) and Evidence-Centered Game Design (ECGD) frameworks. Nine middle and high school students completed a scaffolded ecological investigation requiring evidence collection, hypothesis testing, and causal explanation. Data included concurrent think-aloud protocols, gameplay interaction logs, and brief end-of-session feedback questions. Triangulated analyses captured both convergence and divergence between self-reported and observed agency, revealing intentional decision-making, iterative reasoning, and adaptive strategy shifts supported by nonplayable characters, digital tools, and just-in-time feedback. Findings indicate that the AFL-ECGD integration provides a replicable approach for aligning learner autonomy with competency-based assessment in immersive contexts. While limited by the small sample and single-session exposure, the study advances theoretical accounts of agency in game-based learning and demonstrates a multisource methodology for investigating inquiry processes. Design implications include sequencing tasks to elicit intentionality and forethought, embedding adaptive scaffolds to support self-regulation and reflection, and logging process data for competency-aligned interpretation. Future work should examine scalability, longitudinal outcomes, and domain transfer.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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 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".