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A New Conceptual Framework for Measuring Online Listening in Asynchronous Discussion Forums

  • Huanyou Chai
  • Zhi Liu
  • Tianhui Hu
  • Qing Li
Chapter
  • 29 Downloads
Part of the Advances in Analytics for Learning and Teaching book series (AALT)

Abstract

Learner interactions are the key to understanding how learning occurs in asynchronous discussion forums. Most of the existing studies, however, only pay attention to the visible aspect of interaction (online speaking, i.e., creating posts); little is known about online listening, the invisible one that refers to viewing existing posts. Online listening, often used interchangeably with reading and lurking, is an active, personally driven, and inseparable component in the participation of online discussions. Its main measurement method is objective one based on log-file data, which can be subdivided into individual- and social perspectives-oriented. Two key problems that occur are the lack of concern for the quality of online listening and the unavailability of temporal information in most existing forums. To address these gaps, this chapter presents a conceptual framework from theoretical perspective that aims to comprehensively measure learners’ online listening in asynchronous discussion forums. In order to understand the invisible aspect of learner interactions, the proposed framework first develops a semantic network-based discussions grounded on assimilation theory of meaningful learning. Then a combination of extracted analytics, SNA, and content analysis is proposed to comprehensively analyze online listening from individual, social, and cognitive perspectives. We believe that this proposed framework will contribute to an in-depth insight into learner interactions in online discussions.

Keywords

Online listening Assimilation theory of meaningful learning Semantic network-based discussions Extracted analytics Social network analysis Content analysis 

Notes

Acknowledgments

This work was supported by the Research Funds from National Natural Science Foundation of China (Grant No. 61977030, 61702207, L1724007), National Key Research and Development Program of China (Grant No. 2017YFB1401303), National Social Science Fund Project of China (Grant No. 14BGL131), and Ministry of Education-China Mobile (Grant No. MCM20160401).

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Huanyou Chai
    • 1
  • Zhi Liu
    • 2
    • 3
  • Tianhui Hu
    • 1
  • Qing Li
    • 2
  1. 1.National Engineering Research Center for E-Learning, National Engineering, Laboratory for Educational Big DataCentral China Normal UniversityWuhanChina
  2. 2.National Engineering Laboratory for Educational Big Data (NELEBD)Central China Normal University (CCNU)WuhanPeople’s Republic of China
  3. 3.National Engineering Research Center for E-learning (NERCEL)Central China Normal University (CCNU)WuhanPeople’s Republic of China

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