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In an attempt to predict the learning of a player during a content agnostic educational video game session, this study used a dynamic bayesian network in which participants’ game play interactions were continuously recorded. Their actions were captured and used to make real-time inferences of the learning performance using a dynamic bayesian network. The predicted learning was then correlated with the post-test scores to establish the validity of assessment. The assessment was moderately positively correlated with the post-test scores demonstrating support for its validity.
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Content Agnostic Game Engineering: Impact of Stealth Assessment and Content Order on Player Engagement
Content Agnostic Game Engineering (CAGE) architecture utilizes content agnostic mechanics to create educational games that can teach multiple contents. However, the player engagement goes down when second content is played using the same game mechanics. A content agnostic stealth assessment can aid a CAGE game in sustaining the engagement level of its players. A potentially generalizable method for this was tested using Chem-o-crypt, a CAGE game that can teach chemistry and cryptography contents. The game automatically detects frustration, flow, and boredom using the Affdex SDK from Affectiva. A randomized controlled experiment incorporating real-time game adaptation revealed that using stealth assessment can help sustain engagement in a CAGE game when playing multiple contents.
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- Award ID(s):
- 1828010
- PAR ID:
- 10346355
- Date Published:
- Journal Name:
- Proceedings of the Future Technologies Conference
- Volume:
- 360
- Page Range / eLocation ID:
- 455–470
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1007/978-3-030-89912-7_35
