The Impact of Autonomy and Types of Informational Text Presentations in Game-Based Environments on Learning: Converging Multi-Channel Processes Data and Learning Outcomes
Bibliographic record
Abstract
Game-based learning environments (GBLEs) focus on enhancing learning by providing learners with various representations of information (e.g., text, diagrams, etc.) while allowing full autonomy, or control over their actions. Challenges arise as research shows that learners inaccurately use cognitive and metacognitive processes when given full autonomy. This study examined 105 undergraduates who were randomly assigned to autonomy conditions (i.e., full, partial, and no autonomy) as they interacted with scientific informational text presentations (i.e., non-player characters [NPCs], books and research articles, posters) during learning with Crystal Island, a GBLE. We assessed how learners’ eye-tracking (e.g., fixation durations on objects) and log-file (e.g., durations of activities) data reflected how learners interacted with text presentations and selected pretest-relevant items (i.e., text providing answers to questions on the pretest). Results showed that participants in the partial autonomy condition ( n = 38) demonstrated higher learning gains than those in the full autonomy condition ( n = 45). Time spent interacting with all books and research articles within Crystal Island were positively correlated with learning gains. There were significant differences in learners’ duration and fixation duration on informational text presentation interactions between conditions and within types of presentations as well as significant interactions between pretest-relevant items and types of presentations. Overall, autonomy and pretest relevancy impact the time interacting with informational text presentations which influence learning. Implications are provided for applying autonomy during game-based learning, and how this may direct future implementations of AI within GBLEs to provide implicit scaffolding via adaptively limiting learners’ autonomy as they interact with informational text.
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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 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".