Learning the Structures of Online Asynchronous Conversations

Wang, Chaokun; Lin, Heran; Wang, Weiping; Cai, Zhipeng; Wang, Jianmin

doi:10.1007/978-3-319-55753-3_2

Cited by 10 publications

(6 citation statements)

References 18 publications

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“…In-person, two people may communicate directly through turn-taking. Through social media platforms, though, conversations are often asynchronous (Chen et al, 2017), as users can respond to messages with a substantial temporal delay.…”

Section: Collaborative Learning On Social Mediamentioning

confidence: 99%

How Do Teachers Collaborate in Informal Professional Learning Activities? An Epistemic Network Analysis

Fütterer,

Omarchevska,

Rosenberg

et al. 2024

J Sci Educ Technol

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Teachers turn to many sources for support and professional learning, including social media-based communities that have shown promise to help teachers access resources and facilitate productive exchanges. Although such online communities show promise, questions about their quality for providing a suitable learning environment remain insufficiently answered. In this study, we examine how teachers’ engagement on Twitter (now known as “X”) may adhere to characteristics of high-quality professional development (PD) activities. In that, we employ advanced conversational analysis techniques that extend the primarily descriptive methods used in prior research. Specifically, we collected data from three Twitter communities related to Advanced Placement Biology (N = 2,040 tweets, N = 93 teachers). Qualitative two-cycle content analyses derived both tweet content and sentiment. Using epistemic network analyses, we examined the collaborative structures to examine how participation patterns can identify characteristics of high-quality online PD. Results indicate that some teachers use Twitter with a content focus and coherent to their individual contexts and prior knowledge. Notably, differences in collaboration and participation patterns by teachers’ overall activity level hint at the existence of an online community of practice. More active teachers communicated more about how their individual contexts relate to instruction, whereas less active teachers exhibited more targeted engagement, for instance, related to sharing teaching resources and organizing learning opportunities. Overall, this study illustrates how Twitter may provide a meaningful learning environment to teachers so that it can serve as a relevant avenue for their professional learning.

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Section: Collaborative Learning On Social Mediamentioning

confidence: 99%

How Do Teachers Collaborate in Informal Professional Learning Activities? An Epistemic Network Analysis

Fütterer,

Omarchevska,

Rosenberg

et al. 2024

J Sci Educ Technol

View full text Add to dashboard Cite

show abstract

“…In-person, two people may communicate directly through turn-taking. Through social media platforms (like Twitter), though, conversations are often asynchronous (Chen et al, 2017), as users can respond to messages with a substantial temporal delay.…”

Section: Collaborative Learning On Social Mediamentioning

confidence: 99%

How do Teachers Collaborate in Informal Professional Learning Activities? An Epistemic Network Analysis

Fischer¹,

Omarchevska²,

Fütterer³

et al. 2022

Preprint

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Teachers turn to many sources for support and professional learning, including social media-based communities that have shown promise to help teachers access resources and facilitate productive exchanges. Although such online communities show promise, questions about their quality for providing a suitable learning environment remain insufficiently answered. This study examines how teachers’ engagement on Twitter may adhere to characteristics of high-quality professional development activities. In that, we employ advanced conversational analysis techniques that extend the primarily descriptive methods used in prior research. Specifically, we collected data from three Twitter communities related to Advanced Placement Biology (N = 2,040 tweets, N = 93 teachers). Qualitive two-cycle content analyses derived both tweet content and sentiment. Using epistemic network analyses, we examined the collaborative structures to examine how participation patterns can identify characteristics of high-quality teacher professional development. Results indicate that some teachers use Twitter with a content focus and coherent to their individual contexts and prior knowledge. Notably, differences in collaboration and participation patterns by teachers’ overall activity level hint at the existence of an online community of practice. More active teachers communicated more about how their individual contexts relate to instruction, whereas less active teachers exhibited more targeted engagement, for instance, related to sharing teaching resources and organizing learning opportunities. Overall, this study illustrates how Twitter may provide a meaningful learning

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“…(1) two-step approaches and (2) end-to-end methods. The two-step methods (Elsner and Charniak, 2008, 2010, 2011Chen et al, 2017;Jiang et al, 2018;Kummerfeld et al, 2019) firstly retrieve the relations between two messages, e.g., "reply-to" relations (Guo et al, 2018;, and then adopt a clustering algorithm to construct individual sessions. The end-to-end models (Tan et al, 2019;Yu and Joty, 2020), instead, perform the disentanglement operation in an end-to-end manner, where the context information of detached sessions will be exploited to classify a message to a session.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised Conversation Disentanglement through Co-Training

Liu¹,

Shi²,

Zhu³

2021

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

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Conversation disentanglement aims to separate intermingled messages into detached sessions, which is a fundamental task in understanding multi-party conversations. Existing work on conversation disentanglement relies heavily upon human-annotated datasets, which are expensive to obtain in practice. In this work, we explore to train a conversation disentanglement model without referencing any human annotations. Our method is built upon a deep co-training algorithm, which consists of two neural networks: a messagepair classifier and a session classifier. The former is responsible for retrieving local relations between two messages while the latter categorizes a message to a session by capturing context-aware information. Both networks are initialized respectively with pseudo data built from an unannotated corpus. During the deep co-training process, we use the session classifier as a reinforcement learning component to learn a session assigning policy by maximizing the local rewards given by the messagepair classifier. For the message-pair classifier, we enrich its training data by retrieving message pairs with high confidence from the disentangled sessions predicted by the session classifier. Experimental results on the large Movie Dialogue Dataset demonstrate that our proposed approach achieves competitive performance compared to the previous supervised methods. Further experiments show that the predicted disentangled conversations can promote the performance on the downstream task of multi-party response selection.

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Learning the Structures of Online Asynchronous Conversations

Cited by 10 publications

References 18 publications

How Do Teachers Collaborate in Informal Professional Learning Activities? An Epistemic Network Analysis

How Do Teachers Collaborate in Informal Professional Learning Activities? An Epistemic Network Analysis

How do Teachers Collaborate in Informal Professional Learning Activities? An Epistemic Network Analysis

Unsupervised Conversation Disentanglement through Co-Training

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