Mining Event-Stream Learner Profiles in an Educational Math Game
Bibliographic record
Abstract
Educational games can provide rich, interactive environments for learning, generating extensive event-stream data that can offer deep insights into student behavior and learning outcomes. However, research on mining learner profiles from event-stream play data remains limited, particularly in adaptive game-based environments. This study applies learning analytics and educational data mining (LA/EDM) methods to analyze student playstyles in My Math Academy, a game-based adaptive learning system. Using a predictive profiling approach, we employ interpretable tree-based models to identify distinct learner groups based on in-game interactions and their relationship to learning outcomes. Results reveal five learner profiles: high-volume learners, slow-paced learners, selective-cancel high-success learners, fast-paced learners, and low-duration learners. Each group exhibits unique behavioral patterns in usage, pacing, and playstyle, especially highlighting the nuanced relationship between time on task and learning. These findings reinforce the importance of adaptive learning environments that support diverse learner needs and inform instructional design tailored to individual student trajectories. By integrating educational research with industry best practices in player profiling, this study contributes to the growing body of research at the intersection of learner profiles and game-based LA/EDM. More broadly, this deeper understanding of student profiles in immersive, adaptive digital learning environments provides a foundation for more responsive, data-driven educational design-fostering richly personalized learning experiences tailored to student pacing, interaction style, and performance.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".