Published May 1, 2013
Carol M. ForsythArthur C. Graesser Philip Pavlik Jr. Zhiqiang Cai Heather Butler Diane Halpern Keith Millis
Operation ARIES! is an Intelligent Tutoring System that is designed to teach scientific methodology in a game-likeatmosphere. A fundamental goal of this serious game is to engage students during learning through naturallanguage tutorial conversations. A tight integration of cognition, discourse, motivation, and affect is desired tomeet this goal. Forty-six undergraduate students from two separate colleges in Southern California interactedwith Operation ARIES! while intermittently answering survey questions that tap specific affective andmetacognitive states related to the game-like and instructional qualities of Operation ARIES!. After performinga series of data mining explorations, we discovered two trends in the log files of cognitive-discourse events thatpredicted self-reported affective states. Students reporting positive affect tended to be more verbose duringtutorial dialogues with the artificial agents. Conversely, students who reported negative emotions tended toproduce lower quality conversational contributions with the agents. These findings support a valence-intensitytheory of emotions and also the claim that cognitive-discourse features can predict emotional states over andabove other game features embodied in ARIES.
How to Cite
Forsyth, C., Graesser, A., Pavlik Jr., P., Cai, Z., Butler, H., Halpern, D., & Millis, K. (2013). Operation ARIES!: Methods, Mystery, and Mixed Models: Discourse Features Predict Affect in a Serious Game. JEDM | Journal of Educational Data Mining, 5(1), 147-189. Retrieved from https://jedm.educationaldatamining.org/index.php/JEDM/article/view/33
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