Computer games that are used for the purpose of learning, training, and instruction are often referred to as serious games. The last decade has shown that serious games can contribute considerably to learning effectiveness and motivation of students. To further enhance effectiveness game analytics are a major focus as it is beneficial to know how a student is performing in a game. One can distinguish several reasons why this is attractive. The recording of implicit measurements of students during playing, can help to characterize the proficiency of players, e.g. by distinguishing conscientious, goal-directed players from chaotic, explorative players. It can also be an instrument in improving the game, e.g. where confusion arises and, most importantly, it can be used to dynamically improve (adapt) the game to the game performance of the player, in order to enhance learning.
There are many in-game analytics and many game analytics systems, however, too often these game analytics are based on shallow interaction data and their success in predicting learning is limited or even unknown. In our view, game analytic measures are often too data-driven and system-driven, and not enough theory-driven meaning that the analytics are not based on cognitive task analysis nor based on analysis of the cognitive processes and cognitive problems that players encounter during progressing and navigating through a serious game. Furthermore, systematic knowledge on the dimensions of adaptation is lacking.
Adaptivity in serious games consists of two main steps. The first step assesses the status of the player by means of game-behavior analytics and includes creation of a user (player) model and the second step involves using this information to provide the actual adaptivity to support the player.
Within this Research Topic we will focus on the following sub-areas:
- On-line assessment, preferably unobtrusively, of game performance that is relevant for criterion behavior, e.g. knowledge or skill acquisition. Development of player (user) models is an important component.
- Case studies into cognitive task analysis and methodology to identify the bottle-necks in information processing during game play.
- Adaptation in strict sense: in what ways, in which dimensions, can we adapt dynamically the content of the game environment?
- Conceptual, theoretical analysis: description of the concepts involved would be useful for researchers. What are the differences and similarities between on-line and off-line adaptation, personalisation, customization, dynamic adaptation, adaptivity and adaptability, etc?
Contributions to the topics indicated above can come from different disciplines: psychology, educational science, cognitive science, AI and computing science. The type of contributions may vary from theoretical descriptive study, review-like work, computing-oriented user-centered system designs to empirical studies.
Computer games that are used for the purpose of learning, training, and instruction are often referred to as serious games. The last decade has shown that serious games can contribute considerably to learning effectiveness and motivation of students. To further enhance effectiveness game analytics are a major focus as it is beneficial to know how a student is performing in a game. One can distinguish several reasons why this is attractive. The recording of implicit measurements of students during playing, can help to characterize the proficiency of players, e.g. by distinguishing conscientious, goal-directed players from chaotic, explorative players. It can also be an instrument in improving the game, e.g. where confusion arises and, most importantly, it can be used to dynamically improve (adapt) the game to the game performance of the player, in order to enhance learning.
There are many in-game analytics and many game analytics systems, however, too often these game analytics are based on shallow interaction data and their success in predicting learning is limited or even unknown. In our view, game analytic measures are often too data-driven and system-driven, and not enough theory-driven meaning that the analytics are not based on cognitive task analysis nor based on analysis of the cognitive processes and cognitive problems that players encounter during progressing and navigating through a serious game. Furthermore, systematic knowledge on the dimensions of adaptation is lacking.
Adaptivity in serious games consists of two main steps. The first step assesses the status of the player by means of game-behavior analytics and includes creation of a user (player) model and the second step involves using this information to provide the actual adaptivity to support the player.
Within this Research Topic we will focus on the following sub-areas:
- On-line assessment, preferably unobtrusively, of game performance that is relevant for criterion behavior, e.g. knowledge or skill acquisition. Development of player (user) models is an important component.
- Case studies into cognitive task analysis and methodology to identify the bottle-necks in information processing during game play.
- Adaptation in strict sense: in what ways, in which dimensions, can we adapt dynamically the content of the game environment?
- Conceptual, theoretical analysis: description of the concepts involved would be useful for researchers. What are the differences and similarities between on-line and off-line adaptation, personalisation, customization, dynamic adaptation, adaptivity and adaptability, etc?
Contributions to the topics indicated above can come from different disciplines: psychology, educational science, cognitive science, AI and computing science. The type of contributions may vary from theoretical descriptive study, review-like work, computing-oriented user-centered system designs to empirical studies.
