Signals of Struggle: Detecting Player Difficulties Using Machine Learning
Bibliographic record
Abstract
Struggle is an inevitable part of gameplay, and it’s often what makes games meaningful, rewarding, and fun. Still, some moments of difficulty spiral into frustration or confusion, and can cause players to quit entirely. Being able to detect and interpret struggle is thus essential for designing better player experiences, but identifying these moments remains challenging. We present a machine learning approach for detecting player struggle in real time using gameplay telemetry. Using three quests built in Terraria that each emphasize a different set of game mechanics – gathering, combat, or crafting – we collected data on how players interact with different systems and had them annotate where they encountered difficulty. Using this dataset, we trained Random Forest classifiers and evaluated model performance across different feature sets, window sizes, and step sizes. Our results show that such a model can successfully identify whether unseen players are experiencing struggle in the crafting quest, while the other quests proved more difficult. We also tested whether a model could classify the type of struggle as cognitive or performative, and found promising results for the crafting and combat quests. Our findings demonstrate the potential of using player telemetry to detect struggle, laying the groundwork for future adaptive systems that offer real-time, context-aware support tailored to individual player needs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".