Analytics Validation for PBS KIDS Science Games

Principal Investigator: Greg Chung
Graduate School of Education & Information Studies (CRESST)
Participants: Rooms 3, 11 & 12 – Students ages 6-8
Keywords: gameplay meaures for Inquiry, Engagement, Affect, Content Knowledge


While many educational games strive to adapt for an optimal gaming and learning experience, there is relatively little research on creating algorithms for adaptivity This study seeks to understand the efficacy of algorithms embedded in educational science games that adapt the user experience according to the player’s actions and performance in order to deliver a superior learning experience. The study will also lead to the development of new algorithms based upon the observational data we collect in order to further the adaptive capabilities of the games. Participants will be observed playing science games (aligned with the Next Generation Science Standards), developed by PBS KIDS. Video data and clickstream data will be collected, and players will participate in 2 short interviews with researchers to understand their background knowledge of the science concepts, attitudes toward science, experience with digital games, and experience playing the games. Data will be analyzed qualitatively to understand the players’ experience of the game adaptivity, and new algorithms will be developed based upon observations of player behavior.