Please use this identifier to cite or link to this item: http://hdl.handle.net/10497/21289
Title: 
Authors: 
Supervisor: 
Tan, Seng Chee
Chen, Wenli
Issue Date: 
2019
Abstract: 
Instructors adopting a dialogic approach to teaching and learning (Reznitskaya & Gregory, 2013) tend to design learning environments where there is shared control over classroom talks for collaborative meaning making and dialogic inquiry among learners. In this thesis, the dialogic pedagogical approach follows Scardamalia and Bereiter’s model of knowledge building (2003), which leverages a learner’s natural curiosity in questioning and inquiry. In such an environment, learners engage in social collaborative inquiries to contribute and advance communal knowledge. However, working to improve one’s ideas requires considerable support (Scardamalia & Bereiter, 2014). For example, during the initial phase of the inquiry, learners could raise many competing ideas. Recognising and identifying promising ideas is an approach to support such processes and is also an important component of expertise and creativity (Bereiter & Scardamalia, 1993; Gardner 1994). Technological tools, such as online forums (e.g., Knowledge Forum), can provide conducive platforms to the sharing of ideas, but learners still encounter potential difficulties in identifying promising ideas that would be relevant, communally interesting, and of impact to the community. The goal of this research is to identify and analyse promising ideas that can sustain idea improvement in knowledge building discourse, and investigate the effect of these ideas on the collective advancement of communal knowledge. This thesis encapsulates the development and implementation of a methodology called Idea Identification and Analysis (I2A), which uses network analysis, temporal analytics, and machine learning techniques to define the attributes of promising ideas, recognise various idea types, and determine the effect of such ideas within online knowledge building discourse. The methodology is guided by an Idea Pipeline framework that is modelled after a classic innovation process. Two studies were conducted in this thesis to search for promising ideas and to determine the feasibility and scalability of the I2A methodology. The first study was exploratory in nature and its results determined the attributes of idea promisingness using the betweenness centrality (BC) network measure. A classification system was developed to determine idea types in discourse based on the recognition of BC trends. A temporal analysis was applied to identify promising ideas in discourse and the findings were qualitatively validated. Building upon the first study, the second study further improved the I2A methodology using text mining, new visualisation, and clustering techniques. In addition to confirming the presence and nature of promising ideas from the first study, learners’ input in discourse were examined to determine the mobility of ideas in discourse and various idea types could be automatically recognised. These processes also helped to confirm and uncover new promising ideas in discourse. In essence, this thesis contributes to the field of Computer-Supported Collaborative Learning (CSCL) with the development of a framework and methodology that will guide and assist future research and development in closing the knowledge gap in idea analysis within knowledge building discourse.
URI: 
Call Number: 
LB1062 Lee
File Permission: 
Restricted
File Availability: 
With file
Appears in Collections:Doctor of Philosophy (Ph.D.)

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