Intelligent tutoring design alternatives in a serious game

Document Type

Conference Proceeding

Publication Date

6-14-2019

Department

Department of Cognitive and Learning Sciences

Abstract

The adaptive learning potential in video games can be increased by incorporating intelligent tutoring system (ITS) approaches. However, very few studies have examined the ITS capability in a game. Across a four-year research project, we implemented a number of intelligent tutoring design alternatives and integrated them into the serious 3D video game. The game, Heuristica, was designed to improve critical thinking by exposing players to different cognitive biases through game play, and then adaptively giving players opportunities to practice avoiding or mitigating each bias. We describe the intelligent tutoring system, and how we structured the learner’s environment, provided learners with tailored feedback, supported spaced and massed practice, and provided opportunities for in-game learner reflection. We describe the design and functionality of the Student Modeler and interface features that support student feedback and interaction. We evaluate the tradeoffs made in the student model design and the impact they had on the game experience, focusing on the open student model summary screen, mixed-initiative opportunities and algorithms for selection of learning opportunities. This paper contributes to the intelligent tutoring literature by describing one of the first intelligent tutoring systems embedded in a 3D game for training and provides lessons learned from implementations in a single game.

Publisher's Statement

© Springer Nature Switzerland AG 2019. Publisher’s version of record: https://doi.org/10.1007/978-3-030-22341-0_13

Publication Title

International conference on human-computer interaction

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