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References

Citekey: @oshima2012a

Oshima, J., Oshima, R., & Matsuzawa, Y. (2012). Knowledge Building Discourse Explorer: a social network analysis application for knowledge building discourse. Educational Technology Research and Development. doi:10.1007/s11423-012-9265-2

Notes

Rough summary

I got to know KBDeX two years ago. This paper is very helpful for understanding the power of this tool to support assessment for knowledge creation. Before reading it, I was thinking of KBDeX as just another analytic tool. I like the way the authors are situating this tool—need for assessment for knowledge creation. I also like the way they explain this tool—a combination of SNA and concept mapping, to bring social networks of people and semantic network of knowledge together.

This tool is carefully designed and has a lot of potential. For future development, as the authors say, I wish to see more indicators tested in further empirical studies. Personally, I think it will be interesting to do some step-wise analysis and use changes of SNA measures brought about by each single discourse unit to model the “power” of each discourse unit, and further find ways to identify powerful discourse moves in knowledge creation. Will be fun to play with this idea!

Clippings

Necessity of New Assessment Approaches in Knowledge-Creation Metaphor (p. 5)

In the acquisition metaphor, researchers are mainly concerned with what and how much knowledge is acquired by learners after learning experiences, and which activities are crucial to more profound knowledge acquisition (Sfard, 1998). Often these researchers use quantitative analysis based on systematic observation to assess learning (Mercer, 2005), and a typical assessment is conducted with a preand post-test design. By using a variety of methods, including paper-and-pencil tests, computerized tests, and presentations, learners’ understanding in specific domains before and after instruction is gauged to identify knowledge acquisition. (p. 6)

Researchers who prefer the participation metaphor predominantly consider detailed case studies in assessments (Mercer, 2005) (p. 6)

However, there are disadvantages in using these assessment techniques. In the knowledge-creation metaphor, researchers are not only concerned with learners’ comprehension of domain-specific knowledge but also with individuals’ contributions to community knowledge. The learners’ epistemic activities of utilizing their individual knowledge to improve their community knowledge cannot be analyzed by assessing static knowledge in a preand post-test design. Categorization of written or oral discourse during learning might be able to identify the epistemic activities, but such a coding scheme is content-free and what knowledge learners actually contribute cannot be examined. (p. 7)

SNA as a Novel Assessment Approach in the Knowledge-Creation Metaphor (p. 8)

However, as suggested by Mercer and colleagues (Mercer, 2005; Wegerif & Mercer, 1997), detailed description analysis of discourse can be impractical for large datasets because it is highly timeconsuming. Consequently, Wegerif and Mercer (1997) developed a methodology to combine detailed description analysis of discourse and computerized discourse analysis to handle large datasets. (p. 8)

In particular, Concept Mapping Tools developed by Cañas et al. (2004) provides researchers with visualization of students’ semantic networks (p. 9)

In educational research, the graphical approach to representing and evaluating learners’ knowledge structures in their minds has been examined for many years (p. 9)

discussions on the advantages of using SNA to investigate community knowledge advancement and individual learners’ engagement in this advancement from the perspective of the knowledgecreation metaphor (e.g., Martinez, Dimitriadis, Rubia, Gomez, & de la Fuente, 2003; Reffay, Teplovs, & Blondel, 2011; Reuven, Zippy, Gilad, & Aviva, 2003). (p. 1)

In previous work (Oshima, Oshima, Matsuzawa, van Aalst, & Chan, 2007), we further extended the potential of SNA as a core assessment technique by describing a different type of social network. (p. 1)

A limited number of studies have used SNA, especially in the knowledgecreation metaphor. (p. 1)

Instead, we used a procedure similar to ordinary SNA, but proposed a different type of social network, one based on the words learners use in their discourse in a CSCL environment. (p. 1)

KBDeX: An Application of SNA of Discourse (p. 1)

Analysis of network characteristics by using coefficients. During SNA, KBDeX calculates conventional network measures, such as the betweenness centrality coefficient, the degree centrality coefficient, and the closeness centrality coefficient (p. 1)

Example of Discourse Data for SNA with KBDeX (p. 1)

Visual Inspection of Network of Words (p. 2)

Analysis of Network Characteristics by Using Coefficients (p. 2)

As discussed in the visualization analysis, a notable difference was found in the cohesiveness of the network structure between the two groups. The final cumulative degree centrality was much higher for the Gillian group than for the Matt group. The following characteristics are seen in the transition of the cumulative degree centrality for the Gillian group: (a) after reaching a high value in the initial phase of their discourse, the cumulative degree centrality was sustained at a high level; and (b) there were no large fluctuations in the coefficients. Conversely, for the Matt group, it was found that (a) the cumulative degree centrality fell steeply in the final phase and (b) there were a number of large fluctuations in the coefficients. (p. 2)

Stepwise Analysis (p. 2)

To evaluate each learner’s contribution to the group discourse, stepwise analyses were conducted. Figures 10 and 11 show averaged absolute differences of the centrality coefficients calculated for the network of words for each learner in the two groups. For the two groups, 3 (Learners) × 3 (Centralities) ANOVAs with the averaged absolute difference as the dependent variable showed that the averaged differences of the closeness and degree centrality coefficients for student A1 in the Gillian group were significantly higher than those for the two other students: F(2, 51) = 7.52, p < 0.01 and F(2, 51) = 8.01, p < 0.01, respectively; however, no significant differences were found for the Matt group. (p. 2)

Thus, individual contributions to the network, which represent the state of the learners’ social knowledge, could be evaluated by breaking down the discourse datasets for each individual. (p. 2)

A unique contribution of SNA using KBDeX is the quantitative examination of each individual learner’s contribution to social knowledge advancement. Stepwise analyses revealed that one student in particular in the Gillian group played a cognitively important role that the two other students did not. This relationship between social knowledge advancement in a group and individual contributions has not been simultaneously examined in previous studies. (p. 2)

Discussion (p. 2)

The measures used in SNA are appropriate for analysts to discuss network characteristics; however, they are not sufficient for educational researchers to examine learning in the knowledge-creation metaphor. The approach that we took in the establishment of indicators was to create a number of potential indicators based on one of the innovative models in the knowledge-creation metaphor (i.e., the knowledge building community), to apply these indicators to discourse (p. 2)

to establish a unifying analytic framework in the knowledge-creation metaphor (p. 2)

Third, we have not sufficiently discussed alternative SNA-based measures (other than the centrality measure) that can be used to examine learning processes related to different metaphors of learning or different outcomes of learning. (p. 2)

Lastly, we hope that discourse analysts can easily extend their analyses by integrating SNA of discourse data into their current approaches. (p. 2)

datasets, and to compare the results with those in previously conducted in-depth analyses. (p. 2)

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