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Multimodal learning analytics for game‐based learning.

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Title: Multimodal learning analytics for game‐based learning.
Authors: Emerson, Andrew ajemerso@ncsu.edu, Cloude, Elizabeth B., Azevedo, Roger, Lester, James
Source: British Journal of Educational Technology. Sep2020, Vol. 51 Issue 5, p1505-1526. 22p. 3 Diagrams, 6 Charts.
Subject Terms: *Classroom environment, *Educational games, *Effective teaching, *Student engagement, *Educational technology, *Learning analytics, Facial expression, Eye tracking
Abstract: A distinctive feature of game‐based learning environments is their capacity to create learning experiences that are both effective and engaging. Recent advances in sensor‐based technologies such as facial expression analysis and gaze tracking have introduced the opportunity to leverage multimodal data streams for learning analytics. Learning analytics informed by multimodal data captured during students' interactions with game‐based learning environments hold significant promise for developing a deeper understanding of game‐based learning, designing game‐based learning environments to detect maladaptive behaviors and informing adaptive scaffolding to support individualized learning. This paper introduces a multimodal learning analytics approach that incorporates student gameplay, eye tracking and facial expression data to predict student posttest performance and interest after interacting with a game‐based learning environment, Crystal Island. We investigated the degree to which separate and combined modalities (ie, gameplay, facial expressions of emotions and eye gaze) captured from students (n = 65) were predictive of student posttest performance and interest after interacting with Crystal Island. Results indicate that when predicting student posttest performance and interest, models utilizing multimodal data either perform equally well or outperform models utilizing unimodal data. We discuss the synergistic effects of combining modalities for predicting both student interest and posttest performance. The findings suggest that multimodal learning analytics can accurately predict students' posttest performance and interest during game‐based learning and hold significant potential for guiding real‐time adaptive scaffolding. [ABSTRACT FROM AUTHOR]
Copyright of British Journal of Educational Technology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: Multimodal learning analytics for game‐based learning.
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  Data: <searchLink fieldCode="AR" term="%22Emerson%2C+Andrew%22">Emerson, Andrew</searchLink><i> ajemerso@ncsu.edu</i><br /><searchLink fieldCode="AR" term="%22Cloude%2C+Elizabeth+B%2E%22">Cloude, Elizabeth B.</searchLink><br /><searchLink fieldCode="AR" term="%22Azevedo%2C+Roger%22">Azevedo, Roger</searchLink><br /><searchLink fieldCode="AR" term="%22Lester%2C+James%22">Lester, James</searchLink>
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  Data: <searchLink fieldCode="JN" term="%22British+Journal+of+Educational+Technology%22">British Journal of Educational Technology</searchLink>. Sep2020, Vol. 51 Issue 5, p1505-1526. 22p. 3 Diagrams, 6 Charts.
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  Data: *<searchLink fieldCode="DE" term="%22Classroom+environment%22">Classroom environment</searchLink><br />*<searchLink fieldCode="DE" term="%22Educational+games%22">Educational games</searchLink><br />*<searchLink fieldCode="DE" term="%22Effective+teaching%22">Effective teaching</searchLink><br />*<searchLink fieldCode="DE" term="%22Student+engagement%22">Student engagement</searchLink><br />*<searchLink fieldCode="DE" term="%22Educational+technology%22">Educational technology</searchLink><br />*<searchLink fieldCode="DE" term="%22Learning+analytics%22">Learning analytics</searchLink><br /><searchLink fieldCode="DE" term="%22Facial+expression%22">Facial expression</searchLink><br /><searchLink fieldCode="DE" term="%22Eye+tracking%22">Eye tracking</searchLink>
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  Data: A distinctive feature of game‐based learning environments is their capacity to create learning experiences that are both effective and engaging. Recent advances in sensor‐based technologies such as facial expression analysis and gaze tracking have introduced the opportunity to leverage multimodal data streams for learning analytics. Learning analytics informed by multimodal data captured during students' interactions with game‐based learning environments hold significant promise for developing a deeper understanding of game‐based learning, designing game‐based learning environments to detect maladaptive behaviors and informing adaptive scaffolding to support individualized learning. This paper introduces a multimodal learning analytics approach that incorporates student gameplay, eye tracking and facial expression data to predict student posttest performance and interest after interacting with a game‐based learning environment, Crystal Island. We investigated the degree to which separate and combined modalities (ie, gameplay, facial expressions of emotions and eye gaze) captured from students (n = 65) were predictive of student posttest performance and interest after interacting with Crystal Island. Results indicate that when predicting student posttest performance and interest, models utilizing multimodal data either perform equally well or outperform models utilizing unimodal data. We discuss the synergistic effects of combining modalities for predicting both student interest and posttest performance. The findings suggest that multimodal learning analytics can accurately predict students' posttest performance and interest during game‐based learning and hold significant potential for guiding real‐time adaptive scaffolding. [ABSTRACT FROM AUTHOR]
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  Label:
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  Data: <i>Copyright of British Journal of Educational Technology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Sep2020
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