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A Measurement Model of Gestures in an Embodied Learning Environment: Accounting for Temporal Dependencies

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Title: A Measurement Model of Gestures in an Embodied Learning Environment: Accounting for Temporal Dependencies
Language: English
Authors: Andrade, Alejandro, Danish, Joshua A., Maltese, Adam V.
Source: Journal of Learning Analytics. 2017 4(3):18-45.
Availability: Society for Learning Analytics Research. 121 Pointe Marsan, Beaumont, AB T4X 0A2, Canada. Tel: +61-429-920-838; e-mail: info@solaresearch.org; Web site: http://learning-analytics.info/journals/index.php/JLA/
Peer Reviewed: Y
Page Count: 28
Publication Date: 2017
Document Type: Journal Articles
Reports - Research
Education Level: Grade 3
Descriptors: Measurement, Interaction, Models, Educational Environment, Correlation, Markov Processes, Predictor Variables, Comparative Analysis, Pretests Posttests, Bayesian Statistics, Statistical Analysis, Probability, Grade 3, Grade 4, Questionnaires, Interviews, Computer Simulation, Nonverbal Communication, Motion, Time, Multivariate Analysis, Qualitative Research
ISSN: 1929-7750
Abstract: Interactive learning environments with body-centric technologies lie at the intersection of the design of embodied learning activities and multimodal learning analytics. Sensing technologies can generate large amounts of fine-grained data automatically captured from student movements. Researchers can use these fine-grained data to create a high-resolution picture of the activity that takes place during these student-computer interactions and explore whether the sequence of movements has an effect on learning. We present a use-case modelling of temporal data in an interactive learning environment with hand gestures, and discuss some validity threats if temporal dependencies are not accounted for. In particular, we assess how, if ignored, the temporal dependencies in the measurement of hand gestures might affect the goodness of fit of the statistical model and would affect the measurement of the similarity between elicited and enacted movement. Our findings show that accounting for temporality is crucial for finding a meaningful fit to the data. In using temporal analytics, we are able to create a high-resolution picture of how sensorimotor coordination correlates with learning gains in our learning system.
Abstractor: As Provided
Number of References: 40
Entry Date: 2017
Accession Number: EJ1163718
Database: ERIC
FullText Text:
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  Data: A Measurement Model of Gestures in an Embodied Learning Environment: Accounting for Temporal Dependencies
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  Data: Society for Learning Analytics Research. 121 Pointe Marsan, Beaumont, AB T4X 0A2, Canada. Tel: +61-429-920-838; e-mail: info@solaresearch.org; Web site: http://learning-analytics.info/journals/index.php/JLA/
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  Data: Interactive learning environments with body-centric technologies lie at the intersection of the design of embodied learning activities and multimodal learning analytics. Sensing technologies can generate large amounts of fine-grained data automatically captured from student movements. Researchers can use these fine-grained data to create a high-resolution picture of the activity that takes place during these student-computer interactions and explore whether the sequence of movements has an effect on learning. We present a use-case modelling of temporal data in an interactive learning environment with hand gestures, and discuss some validity threats if temporal dependencies are not accounted for. In particular, we assess how, if ignored, the temporal dependencies in the measurement of hand gestures might affect the goodness of fit of the statistical model and would affect the measurement of the similarity between elicited and enacted movement. Our findings show that accounting for temporality is crucial for finding a meaningful fit to the data. In using temporal analytics, we are able to create a high-resolution picture of how sensorimotor coordination correlates with learning gains in our learning system.
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      – Text: English
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        PageCount: 28
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      – SubjectFull: Measurement
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      – SubjectFull: Interaction
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      – SubjectFull: Models
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      – SubjectFull: Educational Environment
        Type: general
      – SubjectFull: Correlation
        Type: general
      – SubjectFull: Markov Processes
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      – SubjectFull: Predictor Variables
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      – SubjectFull: Comparative Analysis
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      – SubjectFull: Pretests Posttests
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      – SubjectFull: Bayesian Statistics
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      – SubjectFull: Statistical Analysis
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      – SubjectFull: Nonverbal Communication
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      – SubjectFull: Motion
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      – SubjectFull: Time
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      – SubjectFull: Multivariate Analysis
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      – SubjectFull: Qualitative Research
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    Titles:
      – TitleFull: A Measurement Model of Gestures in an Embodied Learning Environment: Accounting for Temporal Dependencies
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              Y: 2017
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