Journal article Open Access

Operation ARIES!: Methods, Mystery, and Mixed Models: Discourse Features Predict Affect in a Serious Game

Forsyth, Carol M.; Graesser, Arthur C.; Pavlik, Philip, Jr; Cai, Zhiqiang; Butler, Heather; Halpern, Diane; Millis, Keith

Operation ARIES! is an Intelligent Tutoring System that is designed to teach scientific methodology in a game-like atmosphere. A fundamental goal of this serious game is to engage students during learning through natural language tutorial conversations. A tight integration of cognition, discourse, motivation, and affect is desired to meet this goal. Forty-six undergraduate students from two separate colleges in Southern California interacted with Operation ARIES! while intermittently answering survey questions that tap specific affective and metacognitive states related to the game-like and instructional qualities of Operation ARIES!. After performing a series of data mining explorations, we discovered two trends in the log files of cognitive-discourse events that predicted self-reported affective states. Students reporting positive affect tended to be more verbose during tutorial dialogues with the artificial agents. Conversely, students who reported negative emotions tended to produce lower quality conversational contributions with the agents. These findings support a valence-intensity theory of emotions and also the claim that cognitive-discourse features can predict emotional states over and above other game features embodied in ARIES.

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