Towards Custom Quest Tutorials: Identifying and Addressing Players' Cognitive and Performative Struggles in Games
Bibliographic record
Abstract
Challenge is fundamental to creating enjoyable games and maintaining player engagement.However, excessive difficulty can frustrate players and hinder their experience.Therefore, understanding and addressing why players struggle-whether cognitively (understanding objectives) or performatively (executing actions)-can improve player engagement and enhance game design.This research is a work in progress aimed at analyzing gameplay data and player feedback from quests designed to test movement, combat, and crafting mechanics, with the goal of constructing a dataset to provide insights into players' struggles.Using machine learning, we aim to develop a model that detects player struggles in real-time, identifying key factors contributing to these challenges.This model will enable custom quest tutorial interventions triggered when a player experiences difficulties, offering tailored support for gameplay.By providing an approach to identify player struggles, this work can help game designers and developers create personalized experiences.This paper outlines the current state of our research and invites feedback from the community.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".