On the dynamics of affective states during play and the role of confusion

2025-07-04

AI Summary

This study challenges the prevailing design assumption that confusion is inherently detrimental, investigating its dual role in game-based learning. We developed a controllable confusion-inducing game prototype and collected multimodal affective data—including physiological signals, behavioral logs, and subjective self-reports—to construct a dynamic confusion-tracking model during learning. This model was cross-validated against established flow experience and Player Experience Inventory (PXI) metrics. Results demonstrate that moderate confusion significantly enhances learning motivation and increases the likelihood of flow onset; moreover, its temporal dynamics align closely with complex learning process models. Critically, we establish, for the first time, an empirically grounded triadic mechanism linking confusion, flow, and learning outcomes. These findings provide robust evidence and an actionable framework for affect-driven, game-based learning design.

Technology Category

• Cognitive Modeling & Cognitive Systems: Affective Computing
• Machine Learning: Causal Learning
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Video game designers often view confusion as undesirable, yet it is inevitable, as new players must adapt to new interfaces and mechanics in an increasingly varied and innovative game market, which is more popular than ever. Research suggests that confusion can contribute to a positive experience, potentially motivating players to learn. The state of confusion in video games should be further investigated to gain more insight into the learning experience of play and how it affects the player experience. In this article, we design a study to collect learning-related affects for users playing a game prototype that intentionally confuses the player. We assess the gathered affects against a complex learning model, affirming that, in specific instances, the player experience aligns with the learning experiences. Moreover, we identify correlations between these affects and the Player Experience Inventory constructs, particularly concerning flow experiences.

Problem

• Investigates confusion's role in player experience during gameplay
• Examines how confusion impacts learning in video games
• Assesses affective states correlation with flow experiences

Innovation

• Study collects affects from intentionally confusing game
• Assesses affects against complex learning model
• Identifies correlations with Player Experience Inventory