This volume brings together research on how gameplay data in serious games may be turned into valuable analytics or actionable intelligence for performance measurement, assessment, and improvement. Chapter authors use empirical research methodologies, including existing, experimental, and emerging conceptual frameworks, from various fields, such as: computer science software engineering educational data mining statistics information visualization. Serious games is an emerging field where the games are created using sound learning theories and instructional design principles to maximize learning and training success. But how would stakeholders know what play-learners have done in the game environment, and if the actions performance brings about learning? Could they be playing the game for fun, really learning with evidence of performance improvement, or simply gaming the system, i.e., finding loopholes to fake that they are making progress? This volume endeavors to answer these questions.Klappentext:
This first-of-its-kind reference moves the concepts and methodologies of game analytics and learning analytics into the emerging field of serious games. The book calls for more validated and standardized research into serious games analytics, backing up this demand with ways that player data can be transformed into information of value to the academic and serious games sectors. Featured methodologies derive from diverse disciplines, from computer science and data visualization to learning science and statistics. And the volume's second half highlights new frontiers for serious games in medical education and patient care, psychological profile generation, and learning support.
Included in the coverage:
Serious Games Analytics gives educators, instructional designers, and researchers in educational technology a fuller grasp of the knowledge learners access in serious game play, and data and ideas leading to the next wave of serious games.
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