Apply GAP data in educational game design through difficulty tuning, hints, and feedback¶

Strategy · All strategies
Evidence · 1 claim (1 for) · 1 study (1 associational), q2 · 0 of 1 report an effect size · 1 claim rests on one study

Description¶

The chapter proposes three methods for using persistence detection to enhance learning in educational games. As the authors state, "Next, we discuss three potential methods to apply GAP data in educational game design: tuning gameplay difficulty, hints, and feedback." Difficulty tuning aims to keep players in the ZPD so they are challenged enough to exhibit persistence without excessive frustration; GAP can inform targeted hints by indicating the player's current state; and real-time GAP feedback could give players a new metric, like a badge or trophy, for self-regulating their persistence.

Design Implications¶

Context¶

Requirements¶

• GAP data points with granularity suited to the game structure; hint systems need informative data points about the player's current skills and conceptualizations

Constraints¶

• Fine-grained data points planned for NP (e.g., in-game drawing data) will likely be specific to NP's game mechanics and thus less transferable to other games; GAP feedback must avoid exposing so much about GAP calculations as to allow gaming of the system

Target Learners¶

• middle school students playing educational games

Target Learning Goals¶

• persistence and physics learning in game-based environments

Claims¶

• The usefulness of the GAP depends on players being sufficiently challenged in the game [+W]

Related Strategies¶

• Use the solver as a hint engine for intelligent tutoring of spatial reasoning in puzzle games

Examples¶

-

Key Sources¶

• Ventura, M., Shute, V., & Small, M. (2013). Assessing Persistence in Educational Games. Design Recommendations for Intelligent Tutoring Systems - Volume 2: Adaptive Instructional Management. https://eric.ed.gov/?q=Assessing+Persistence+in+Educational+Games