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Analyzing Information-Gathering Behavioral Sequences During Game-Based Learning Using Auto-recurrence Quantification Analysis

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Learning and Collaboration Technologies. Designing the Learner and Teacher Experience (HCII 2022)

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Abstract

Analyzing behavioral sequences is essential for identifying how interaction with scientific texts built into a game-based learning environment (GBLE) relates to learning. Undergraduate students (n = 82) were recruited to learn with Crystal Island, a GBLE that teaches learners about microbiology. Learners were randomly assigned to either the full agency (i.e., complete control over one’s own actions) or partial agency condition (i.e., restricted control). As participants learned with Crystal Island, log-file data captured their information-gathering behaviors, defined as their interactions with scientific information—i.e., posters, books, research articles, or talking to non-player characters.. To examine how learners deployed information-gathering behaviors, this paper used auto-Recurrence Quantification Analysis (aRQA). This analysis extracts the entropy (i.e., the number of unique sequential patterns denoting behavioral novelty) of learners’ information-gathering behaviors. Results found that learners with restricted agency demonstrated greater entropy, or more novel information-gathering behaviors than learners with full agency. Further, results suggested an interaction between agency and entropy on learning gains, where learners with greater entropy in the partial agency condition demonstrated greater learning gains. We conclude GBLEs should scaffold learners’ actions while promoting increased diversity in information-gathering behaviors to improve learning outcomes.

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Acknowledgments

This research was supported by funding from the Social Sciences and Humanities Research Council of Canada (SSHRC 895–2011-1006). Any opinions, findings, conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Social Sciences and Humanities Research Council of Canada. The authors would also like to thank members of the SMART Lab and the intelliMEDIA group at NCSU for their assistance and contributions.

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Authors and Affiliations

  1. University of Central Florida, Orlando, FL, 32816, USA

    Daryn A. Dever, Mary Jean Amon, Megan D. Wiedbusch & Roger Azevedo

  2. SoarTechnology, Inc., Orlando, FL, 32816, USA

    Elizabeth B. Cloude

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  1. Daryn A. Dever
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  3. Megan D. Wiedbusch
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Correspondence to Daryn A. Dever .

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Editors and Affiliations

  1. Department of Multimedia and Graphic Art, Cyprus University of Technology, Limassol, Cyprus

    Panayiotis Zaphiris

  2. Research Center on Interactive Media, Smart Systems and Emerging Technologies (CYENS), Cyprus University of Technology, Limassol, Cyprus

    Andri Ioannou

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Dever, D.A., Amon, M.J., Wiedbusch, M.D., Cloude, E.B., Azevedo, R. (2022). Analyzing Information-Gathering Behavioral Sequences During Game-Based Learning Using Auto-recurrence Quantification Analysis. In: Zaphiris, P., Ioannou, A. (eds) Learning and Collaboration Technologies. Designing the Learner and Teacher Experience. HCII 2022. Lecture Notes in Computer Science, vol 13328. Springer, Cham. https://doi.org/10.1007/978-3-031-05657-4_5

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  • DOI: https://doi.org/10.1007/978-3-031-05657-4_5

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