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題名 Two tales of time: Uncovering the significance of sequential patterns among contribution types in knowledge-building discourse
作者 洪煌堯
Chen, Bodong;Resendes, Monica;Chai, Ching Sing;Hong, Huang-Yao
貢獻者 教育系
關鍵詞 Temporality; learning analytics; Lag-sequential Analysis; Frequent Sequence Mining; knowledge building
日期 2017-01
上傳時間 12-Jul-2017 11:38:27 (UTC+8)
摘要 As collaborative learning is actualized through evolving dialogues, temporality inevitably matters for the analysis of collaborative learning. This study attempts to uncover sequential patterns that distinguish “productive” threads of knowledge-building discourse. A database of Grade 1–6 knowledge-building discourse was first coded for the posts’ contribution types and discussion threads’ productivity. Two distinctive temporal analysis techniques – Lag-sequential Analysis (LsA) and Frequent Sequence Mining (FSM) – were subsequently applied to detecting sequential patterns among contribution types that distinguish productive threads. The findings of LsA indicated that threads that were characterized by mere opinion-giving did not achieve much progress, while threads having more transitions among questioning, obtaining information, working with information, and theorizing were more productive. FSM further uncovered from productive threads distinguishing frequent sequences involving sustained theorizing, integrated use of evidence, and problematization of proposed theories. Based on the significance of studying temporality in collaborative learning revealed in the study, we advocate for more analytics tapping into temporality of learning.
關聯 Interactive Learning Environments, 25(2), 162-175
資料類型 article
DOI http://dx.doi.org/10.1080/10494820.2016.1276081
dc.contributor 教育系
dc.creator (作者) 洪煌堯zh-tw
dc.creator (作者) Chen, Bodong;Resendes, Monica;Chai, Ching Sing;Hong, Huang-Yaoen-US
dc.date (日期) 2017-01
dc.date.accessioned 12-Jul-2017 11:38:27 (UTC+8)-
dc.date.available 12-Jul-2017 11:38:27 (UTC+8)-
dc.date.issued (上傳時間) 12-Jul-2017 11:38:27 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/110946-
dc.description.abstract (摘要) As collaborative learning is actualized through evolving dialogues, temporality inevitably matters for the analysis of collaborative learning. This study attempts to uncover sequential patterns that distinguish “productive” threads of knowledge-building discourse. A database of Grade 1–6 knowledge-building discourse was first coded for the posts’ contribution types and discussion threads’ productivity. Two distinctive temporal analysis techniques – Lag-sequential Analysis (LsA) and Frequent Sequence Mining (FSM) – were subsequently applied to detecting sequential patterns among contribution types that distinguish productive threads. The findings of LsA indicated that threads that were characterized by mere opinion-giving did not achieve much progress, while threads having more transitions among questioning, obtaining information, working with information, and theorizing were more productive. FSM further uncovered from productive threads distinguishing frequent sequences involving sustained theorizing, integrated use of evidence, and problematization of proposed theories. Based on the significance of studying temporality in collaborative learning revealed in the study, we advocate for more analytics tapping into temporality of learning.
dc.format.extent 650474 bytes-
dc.format.mimetype application/pdf-
dc.relation (關聯) Interactive Learning Environments, 25(2), 162-175
dc.subject (關鍵詞) Temporality; learning analytics; Lag-sequential Analysis; Frequent Sequence Mining; knowledge building
dc.title (題名) Two tales of time: Uncovering the significance of sequential patterns among contribution types in knowledge-building discourse
dc.type (資料類型) article
dc.identifier.doi (DOI) 10.1080/10494820.2016.1276081
dc.doi.uri (DOI) http://dx.doi.org/10.1080/10494820.2016.1276081