- Title
- Real-time cognitive measures for enhanced human performance
- Creator
- Seyderhelm, Andrew James Alexander
- Relation
- University of Newcastle Research Higher Degree Thesis
- Resource Type
- thesis
- Date
- 2024
- Description
- Research Doctorate - Doctor of Philosophy (PhD)
- Description
- Serious games and simulation training is an ever-growing field with increasing adoption across a wide range of applications and industries. These approaches aim to create educational experiences that are engaging and challenging and enhance learning or training outcomes. Typically, these types of games have had a one-size-fits-all approach where each player receives the same experience. Within this approach designers of these serious games strive to balance gameplay elements with learning requirements to create engaging, motivating, challenging and immersive experiences. This balance is typically understood by iterative testing, yet this approach may not be suitable for all players. The game-play elements or learning content may be too difficult for some or too easy for others. In response to this dichotomy, the balance of fun and learning, much research has been undertaken exploring adaptive serious games. These are serious games that attempt to adapt to the needs of players in real-time, attempting to strike the appropriate level of challenge – neither too hard nor too easy. This approach is supported by flow theory, which proposes that achieving the right balance of challenge may lead to a flow state that is ideal for deep engagement with the task. Adaptive serious games frequently rely on user performance metrics or player affect (e.g. boredom, frustration, or excitement) when adapting a games difficulty to achieve this balance. However, users may maintain task performance, or a favourable affective state, while experiencing increasing levels of cognitive load. Striking the correct level of cognitive load is critical to the learning process, as too high cognitive load may result in overwhelming the player, and too low may result in boredom or disengagement. This study aimed to develop a new adaptive serious game system that combined performance measures with cognitive load to dynamically adjust the gameplay and learning content to meet the needs of the player (student) and the learning outcomes. Simultaneously, this research seeks to apply appropriate learning and psychological theories during design and development to achieve a system that was robust, easy to implement, cost-effective, reliable, and enhanced serious game outcomes. There has been limited research on adaptive serious games that incorporate measures of cognitive load, and none that combine cognitive load and performance measures to adapt multiple game and learning elements. This study has contributed to the literature on serious games through the development of a cognitive adaptive serious games framework and a cognitive and performance based dynamic difficulty adjustment system applicable in serious games and simulation training. The next stage was to identify a reliable, effective, easy to use and implement method of measuring cognitive load. Several approaches were considered, and the final method selected was a form of secondary task built directly into the serious game, using a typical game controller. This method was based on the International Organisation for Standardization’s (ISO) detection response task (DRT), and the method developed for this study was termed the virtual detection response task (virDRT). Experiment 1 was developed to assess how performance and cognitive load were affected by different tasks, challenges and aesthetic conditions in a driving-task serious game. The Cognitive Effect Driving Game was used to validate the virDRT method. Participants (n = 31) played the serious game comparing performance while the virDRT was active versus inactive in a within-subject design. This experiment provided detailed metrics of performance and cognitive load and a deeper understanding of how different tasks, challenges and aesthetic factors affected players. These results can be used by future developers seeking to design effective adaption in serious games. The results from Experiment 1 were used to design the cognitive load and performance dynamic difficulty adjustment system. This system takes performance and cognitive load data and uses it to adjust a serious game in real-time. Detailed outputs were obtained that can support debriefing and assessment and help learning providers assess whether a serious game is effective and what areas need improvement. Experiment 2 used the Surveillance Training Serious Game. Participants (n = 52) played either an adaptive version of the game or a version with only linear difficulty. For this experiment, an updated version of the virDRT was created that increased the resolution of the cognitive load measurement by a factor of five. The results from Experiment 2 showed that the design successfully lowered cognitive load and adjusted the game difficulty for a more balanced experience. This research also provides a novel concept of mastery applicable to serious games: serious game mastery is achieved when threshold performance is achieved with low cognitive load. This research provides significant insights for serious games development, particularly when incorporating DDA, and demonstrates a new version of a DDA system incorporating a combination of cognitive load and performance-based adaption.
- Subject
- dynamic difficulty adjustment; simulation training; serious games; cognitive load; training performance; adaptive training; working memory
- Identifier
- http://hdl.handle.net/1959.13/1510945
- Identifier
- uon:56460
- Rights
- Copyright 2024 Andrew James Alexander Seyderhelm
- Language
- eng
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| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | ATTACHMENT01 | Thesis | 9 MB | Adobe Acrobat PDF | View Details Download | ||
| View Details Download | ATTACHMENT02 | Abstract | 277 KB | Adobe Acrobat PDF | View Details Download |