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

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Title: Operation ARIES!: Methods, Mystery, and Mixed Models: Discourse Features Predict Affect in a Serious Game
Language: English
Authors: Forsyth, Carol M., Graesser, Arthur C., Pavlik, Philip, Cai, Zhiqiang, Butler, Heather, Halpern, Diane, Millis, Keith
Source: Journal of Educational Data Mining. 2013 5(1):147-189.
Availability: International Educational Data Mining. e-mail: jedm.editor@gmail.com; Web site: http://jedm.educationaldatamining.org/index.php/JEDM
Peer Reviewed: Y
Page Count: 43
Publication Date: 2013
Sponsoring Agency: National Science Foundation (NSF)
Contract Number: SBE0354420
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: 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)
Geographic Terms: California
ISSN: 2157-2100
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.
Abstractor: As Provided
Entry Date: 2013
Accession Number: EJ1115374
Database: ERIC
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  Data: Operation ARIES!: Methods, Mystery, and Mixed Models: Discourse Features Predict Affect in a Serious Game
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  Data: International Educational Data Mining. e-mail: jedm.editor@gmail.com; Web site: http://jedm.educationaldatamining.org/index.php/JEDM
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  Data: <searchLink fieldCode="DE" term="%22California%22">California</searchLink>
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  Data: 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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    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 43
        StartPage: 147
    Subjects:
      – SubjectFull: Intelligent Tutoring Systems
        Type: general
      – SubjectFull: Scientific Methodology
        Type: general
      – SubjectFull: Science Instruction
        Type: general
      – SubjectFull: Educational Games
        Type: general
      – SubjectFull: Computer Games
        Type: general
      – SubjectFull: Undergraduate Students
        Type: general
      – SubjectFull: College Science
        Type: general
      – SubjectFull: Student Surveys
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      – SubjectFull: Metacognition
        Type: general
      – SubjectFull: Psychological Patterns
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      – SubjectFull: Data Analysis
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      – SubjectFull: Prediction
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      – SubjectFull: Learning
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      – SubjectFull: Pretests Posttests
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      – SubjectFull: Factor Analysis
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      – SubjectFull: Correlation
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      – SubjectFull: Student Motivation
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      – SubjectFull: Dialogs (Language)
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      – SubjectFull: California
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      – TitleFull: Operation ARIES!: Methods, Mystery, and Mixed Models: Discourse Features Predict Affect in a Serious Game
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