1.1 Traditional Assessments
Traditional assessment methods face significant limitations when attempting to capture comprehensive individual insights during gameplay (Georgiadis, van Lankveld, Bahreini, & Westera, 2019). In the context of dynamic and immersive gaming experiences, conventional approaches, such as standardized tests and questionnaires, fail to adequately grasp the intricacies of players’ cognitive, emotional, and behavioral responses. These methods often lack the granularity required to understand players’ problem-solving abilities, decision-making patterns, emotional states, and subject comprehension within the dynamic gaming environment. Moreover, the rigidity and standardized nature of traditional assessments may lead to oversimplified and generalized conclusions, overlooking the nuanced and context-dependent aspects of players’ interactions with the game. As games continue to evolve as powerful platforms for education, skill development, and entertainment, the constraints of traditional assessment techniques hinder the accurate measurement and interpretation of players’ true capabilities and preferences during active gameplay (Akram et al., 2018; Kenwright, 2017). To overcome these limitations, novel approaches, such as stealth assessment techniques, are being explored to seamlessly embed unobtrusive data collection methods within gaming mechanics, promising to unlock a more comprehensive and accurate portrait of the individual during gameplay (V. Shute et al., 2021).
1.2 Chapter’s Contribution
The key contributions of this chapter lie in introducing and advocating for stealth assessments during active gameplay, addressing the limitations of traditional assessment methods in capturing comprehensive individual insights. It explores the seamless integration of assessment tools within gaming mechanics to collect unobtrusive data on players’ interactions, decision-making patterns, and emotional responses. The chapter also highlights diverse data collection methods, including gamepad and keyboard data, player’s voice analysis, and video camera data, enabling the extraction of valuable biometric insights. Leveraging these techniques, researchers gain a deeper understanding of players’ problem-solving abilities, emotional states, subject comprehension, and adaptive learning patterns during gameplay. The chapter emphasizes the role of data analytics in processing collected data, employing machine learning algorithms, and visualizing patterns for enhanced interpretation. Ethical considerations surrounding data privacy, informed consent, and potential biases are also addressed. Furthermore, the chapter sheds light on how stealth assessments can revolutionize game development by enhancing educational games, personalizing gaming experiences, and finding real-world applications beyond the gaming industry. Overall, this chapter establishes stealth assessments as a powerful approach to unveil a multifaceted portrait of the individual during active gameplay, propelling the field of gaming and assessment research forward.