Emotions in online collaboration have been largely neglected or considered as a static phenomenon. Therefore, the present study aims to explore dynamics of emotional reactions in online collaboration of Communities of Learners (CoL) based on the Emotions as Social Information (EASI) Theory. In addition, relationships between emotional reactions and the learning outcomes of CoL are investigated. Data of 11 CoL with a total of 87 learners are collected in an online training programme. Qualitative content analysis was used to explore dynamics of emotional reactions and quantitative analysis was used to investigate relationships. Results showed that various positive and negative emotional reactions could be identified in the data. Further, by using a newly developed method displaying Emotional Reaction Threads (ERT) it was shown that emotional reactions unfold dynamically during online collaboration. Beyond that, results revealed relationships between the learning outcomes [(i) participation, (ii) exam grade] of CoL and positive [(i) p < .01] and negative emotional reactions [(i) p < .01, (ii) p < .05]. Based on the results of the qualitative content analysis explanations for the identified relationships are provided. Instructors of the online training programme can positively influence the learning outcomes of the CoL through expressing positive emotional reactions during online collaboration.
机构:
Cent China Normal Univ, Natl Engn Res Ctr Educ Big Data, Wuhan, Hubei, Peoples R China
Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R ChinaCent China Normal Univ, Natl Engn Res Ctr Educ Big Data, Wuhan, Hubei, Peoples R China
Liu, Zhi
Gao, Ya
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Cent China Normal Univ, Natl Engn Res Ctr Educ Big Data, Wuhan, Hubei, Peoples R ChinaCent China Normal Univ, Natl Engn Res Ctr Educ Big Data, Wuhan, Hubei, Peoples R China
Gao, Ya
Yang, Yuqin
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Cent China Normal Univ, Fac Artificial Intelligence Educ, Sch Educ Informat Technol, Wuhan, Hubei, Peoples R ChinaCent China Normal Univ, Natl Engn Res Ctr Educ Big Data, Wuhan, Hubei, Peoples R China
Yang, Yuqin
Kong, Xi
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Cent China Normal Univ, Natl Engn Res Ctr Educ Big Data, Wuhan, Hubei, Peoples R ChinaCent China Normal Univ, Natl Engn Res Ctr Educ Big Data, Wuhan, Hubei, Peoples R China
Kong, Xi
Zhao, Liang
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Cent China Normal Univ, Natl Engn Res Ctr Educ Big Data, Wuhan, Hubei, Peoples R China
Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R ChinaCent China Normal Univ, Natl Engn Res Ctr Educ Big Data, Wuhan, Hubei, Peoples R China
机构:
Kent State Univ, Educ Technol, Res Ctr Educ Technol, Kent, OH 44242 USAKent State Univ, Educ Technol, Res Ctr Educ Technol, Kent, OH 44242 USA
Gandolfi, Enrico
Ferdig, Richard E.
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Kent State Univ, Learning Technol, Kent, OH 44242 USA
Kent State Univ, Educ Technol, Kent, OH 44242 USAKent State Univ, Educ Technol, Res Ctr Educ Technol, Kent, OH 44242 USA
Ferdig, Richard E.
Soyturk, Ilker
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Kent State Univ, 800 Hilltop Dr, Kent, OH 44240 USAKent State Univ, Educ Technol, Res Ctr Educ Technol, Kent, OH 44242 USA
机构:
Beijing Language & Culture Univ, Beijing Adv Innovat Ctr Language Resources, Beijing, Peoples R ChinaBeijing Language & Culture Univ, Beijing Adv Innovat Ctr Language Resources, Beijing, Peoples R China
Li Xiaoran
2023 IEEE INTERNATIONAL CONFERENCE ON ADVANCED LEARNING TECHNOLOGIES, ICALT,
2023,
: 139
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