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Partner Keystrokes Can Predict Attentional States during Chat-Based Conversations

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Title: Partner Keystrokes Can Predict Attentional States during Chat-Based Conversations
Language: English
Authors: Kuvar, Vishal, Flynn, Lauren, Allen, Laura, Mills, Caitlin
Source: International Educational Data Mining Society. 2023.
Availability: International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/
Peer Reviewed: Y
Page Count: 7
Publication Date: 2023
Document Type: Speeches/Meeting Papers
Reports - Research
Descriptors: Computer Mediated Communication, Trust (Psychology), Socialization, Keyboarding (Data Entry), Prediction, Correlation, Task Analysis, Cognitive Processes, Interpersonal Communication, Data Analysis, Cooperative Learning, Attention Control, Computer Software, Foreign Countries
Geographic Terms: United States, United Kingdom
Abstract: Computer-mediated social learning contexts have become increasingly popular over the last few years; yet existing models of students' cognitive-affective states have been slower to adopt dyadic interaction data for predictions. Here, we explore the possibility of capitalizing on the inherently social component of collaborative learning by using keystroke log data to make predictions across conversational partners (i.e., using person A's data to make prediction about if person B is mind wandering). Log files from 33 dyads (total N = 66) were used to examine: (a) how mind wandering (defined here as task-unrelated thought) during computer-mediated conversations is related to critical outcomes of the conversation (trust, likability, agreement); (b) if task-unrelated thought can be predicted by the keystrokes of one's partner; and (c) how much data is needed to make predictions by testing various window-sizes of data preceding task-unrelated thought reports. Results indicated a negative relationship between task-unrelated thought and perceptions of the conversation, suggesting that attention is an important factor during computer mediated chat conversations. Finally, in line with our hypothesis, results from mixed effects models showed that one's level of task-unrelated thought was predicted by the keystroke patterns of their conversational partner, but only using small window sizes (5s worth of data). [For the complete proceedings, see ED630829.]
Abstractor: As Provided
Entry Date: 2023
Accession Number: ED630875
Database: ERIC
FullText Text:
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  Data: Computer-mediated social learning contexts have become increasingly popular over the last few years; yet existing models of students' cognitive-affective states have been slower to adopt dyadic interaction data for predictions. Here, we explore the possibility of capitalizing on the inherently social component of collaborative learning by using keystroke log data to make predictions across conversational partners (i.e., using person A's data to make prediction about if person B is mind wandering). Log files from 33 dyads (total N = 66) were used to examine: (a) how mind wandering (defined here as task-unrelated thought) during computer-mediated conversations is related to critical outcomes of the conversation (trust, likability, agreement); (b) if task-unrelated thought can be predicted by the keystrokes of one's partner; and (c) how much data is needed to make predictions by testing various window-sizes of data preceding task-unrelated thought reports. Results indicated a negative relationship between task-unrelated thought and perceptions of the conversation, suggesting that attention is an important factor during computer mediated chat conversations. Finally, in line with our hypothesis, results from mixed effects models showed that one's level of task-unrelated thought was predicted by the keystroke patterns of their conversational partner, but only using small window sizes (5s worth of data). [For the complete proceedings, see ED630829.]
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      – Text: English
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        PageCount: 7
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      – SubjectFull: Computer Mediated Communication
        Type: general
      – SubjectFull: Trust (Psychology)
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      – SubjectFull: Socialization
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      – SubjectFull: Keyboarding (Data Entry)
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      – SubjectFull: Cooperative Learning
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      – SubjectFull: Attention Control
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      – TitleFull: Partner Keystrokes Can Predict Attentional States during Chat-Based Conversations
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