Statistical Discourse Analysis of Online Discussions: Informal Cognition, Social Metacognition, and Knowledge Creation

Chiu, Ming Ming; Fujita, Nobuko

doi:10.1007/978-981-287-047-6_6

Cited by 9 publications

(6 citation statements)

References 35 publications

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“…Much of the work in DCLA is focused on analysis of contributions and contributors, by mapping contributions from participants to roles, or categories of discourse to productive, explanatory-seeking discussion (Chen and Resendes 2014). Other work focuses on statistical analysis of discourse, which allows for both modeling of individual contributions, and modeling relationships among messages within an online community or network, and considers other variables such as demographics (Chiu and Fujita 2014).…”

Section: Social Learningmentioning

confidence: 99%

Learning in the wild: coding for learning and practice on Reddit

Haythornthwaite

Kumar

Gruzd

et al. 2018

Learning, Media and Technology

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Learning on and through social media is becoming a cornerstone of lifelong learning, creating places not only for accessing information, but also for finding other self-motivated learners. Such is the case for Reddit, the online news sharing site that is also a forum for asking and answering questions. We studied learning practices found in 'Ask' subreddits AskScience, Ask_Politics, AskAcademia, and AskHistorians to develop a coding schema for informal learning. This paper describes the process of evaluating and defining a workable coding schema, one that started with attention to learning processes associated with discourse, exploratory talk, and conversational dialogue, and ended with including norms and practices on Reddit and the support of communities of inquiry. Our 'learning in the wild' coding schema contributes a content analysis schema for learning through social media, and an understanding of how knowledge, ideas, and resources are shared in open, online learning forums.

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Section: Social Learningmentioning

confidence: 99%

Learning in the wild: coding for learning and practice on Reddit

Haythornthwaite

Kumar

Gruzd

et al. 2018

Learning, Media and Technology

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“…Researchers have also applied hidden Markov models to multimodal data to detect students' engagement (Andrade et al, 2016). In collaborative settings, sequential analysis, statistical discourse analysis and recurrence analysis have provided insights into the collaborative process (Andrist, Ruis, & Shaffer, 2018;Chiu & Fujita, 2014;Dindar, Alikhani, Malmberg, Järvelä, & Seppänen, 2019;Jermann & Nüssli, 2012;Lee & Tan, 2017;Malmberg, Järvelä, & Järvenoja, 2017). Many of the described methods focus on the recent history for the analysis.…”

Section: Temporal Analysis In Educationmentioning

confidence: 99%

Temporal analysis of multimodal data to predict collaborative learning outcomes

Olsen¹,

Sharma²,

Rummel³

et al. 2020

Brit J Educational Tech

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The analysis of multiple data streams is a long‐standing practice within educational research. Both multimodal data analysis and temporal analysis have been applied successfully, but in the area of collaborative learning, very few studies have investigated specific advantages of multiple modalities versus a single modality, especially combined with temporal analysis. In this paper, we investigate how both the use of multimodal data and moving from averages and counts to temporal aspects in a collaborative setting provides a better prediction of learning gains. To address these questions, we analyze multimodal data collected from 25 9–11‐year‐old dyads using a fractions intelligent tutoring system. Assessing the relation of dual gaze, tutor log, audio and dialog data to students' learning gains, we find that a combination of modalities, especially those at a smaller time scale, such as gaze and audio, provides a more accurate prediction of learning gains than models with a single modality. Our work contributes to the understanding of how analyzing multimodal data in temporal manner provides additional information around the collaborative learning process.

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“…But they have been found to be more persistent, and write lengthier posts [15] [16], and posting behavior depends on both individual characteristics of the poster and the relationship between posts. Selection of data therefore needs to reflect judgments about learning behaviour in forums, rather than just loading in all data.…”

Section: Step 2 Coding and Scoring Log File Datamentioning

confidence: 99%

Crowd-sourced learning in MOOCs

Milligan

2015

Proceedings of the Fifth International Conference on Learning Analytics and Knowledge

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This paper illustrated the promise of the combination of measurement theory and learning analytics for understanding effective MOOC learning. It reports findings from a study of whether and how MOOC log file data can assist in understanding how MOOC participants use (often) messy, chaotic forums to support complex, unpredictable, contingent learning processes. It is argued that descriptions of posting, voting and viewing behaviours do not in and of themselves provide insights about how learning is generated in MOOC forums. Rather, it is hypothesised that there is a skill involved in using forums to learn; that theory-informed descriptions of this skill illustrate how MOOC participants use forums differently as they progress from novice to expert; that the skill progression can be validated through the use of forum log file data; and that log file data can also be used to assess an individual MOOC participant's position in relation to this progression -that is, to measure an individual's skill in learning through forums and similar educational settings. These hypotheses were examined using data drawn from forums in a large MOOC run at the University of Melbourne in 2013.

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Statistical Discourse Analysis of Online Discussions: Informal Cognition, Social Metacognition, and Knowledge Creation

Cited by 9 publications

References 35 publications

Learning in the wild: coding for learning and practice on Reddit

Learning in the wild: coding for learning and practice on Reddit

Temporal analysis of multimodal data to predict collaborative learning outcomes

Crowd-sourced learning in MOOCs

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