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

Autor/inn/en	Forsyth, Carol M.; Graesser, Arthur C.; Pavlik, Philip, Jr.; Cai, Zhiqiang; Butler, Heather; Halpern, Diane; Millis, Keith
Titel	Operation ARIES!: Methods, Mystery, and Mixed Models: Discourse Features Predict Affect in a Serious Game
Quelle	In: Journal of Educational Data Mining, 5 (2013) 1, S.147-189 (43 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2157-2100
Schlagwörter	Intelligent Tutoring Systems; Scientific Methodology; Science Instruction; Educational Games; Computer Games; Undergraduate Students; College Science; Student Surveys; Metacognition; Psychological Patterns; Data Analysis; Prediction; Critical Thinking; Learning; Pretests Posttests; Factor Analysis; Correlation; Student Motivation; Dialogs (Language); California + Suchen Sie Ihr Suchwort? Intelligentes Tutorsystem; Teaching of science; Science education; Natural sciences Lessons; Naturwissenschaftlicher Unterricht; Educational game; Lernspiel; Computer game; Computerspiel; Computerspiele; Schülerbefragung; Meta cognitive ability; Meta-cognition; Metakognitive Fähigkeit; Metakognition; Auswertung; Vorhersage; Kritisches Denken; Lernen; Faktorenanalyse; Korrelation; Schulische Motivation; Dialog; Dialogs; Dialogue; Dialogues; Kalifornien
Abstract	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. (As Provided).
Anmerkungen	International Working Group on Educational Data Mining. e-mail: jedm.editor@gmail.com; Web site: http://www.educationaldatamining.org/JEDM/index.php/JEDM/index
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01