Please use this identifier to cite or link to this item:

Title: Two tales of time: Uncovering the significance of sequential patterns among contribution types in knowledge-building discourse
Authors: 洪煌堯
Chen, Bodong;Resendes, Monica;Chai, Ching Sing;Hong, Huang-Yao
Contributors: 教育系
Keywords: Temporality;learning analytics;Lag-sequential Analysis;Frequent Sequence Mining;knowledge building
Date: 2017-01
Issue Date: 2017-07-12 11:38:27 (UTC+8)
Abstract: 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.
Relation: Interactive Learning Environments, 25(2), 162-175
Data Type: article
DOI 連結:
Appears in Collections:[教育學系] 期刊論文

Files in This Item:

File Description SizeFormat
162-175.pdf635KbAdobe PDF362View/Open

All items in 學術集成 are protected by copyright, with all rights reserved.

社群 sharing