A Japanese research & software team developed an analytical software called KBDeX
, to visualize network structures of discourse based on two-mode words*discourse
units. This software is basically a discourse analysis tool, based on the relation between words and discourse units. For a researcher in the collaborative online learning research field, you can select words and demonstrate the relations of words, based on your interest. This can help demonstrate students’ knowledge construction/creation. It demonstrates words/discourses relations in the network format; in addition, it can demonstrate the temporal development process of networks. This is a big strength of this discourse analysis tool. Yet it should be noted that it is not primarily based on the relations between students; rather, it is based on the co-occurence of words in discourse units. Although, student interaction network can be demonstrated, in terms of the common words they have used in the discourse.
Here I want to demonstrate a demo from a part of my dissertation data. I would probably consider to use this tool in my dissertation.
- First, I dragged a portion of online discussion data from the data I collected for my dissertation and transferred them to the format as shown by KBDex dataset. In my data, each discourse unit represents a comment.
- I added one feature to assign students to two different groups (see figure 1); a group of students got involved within a discussion in the thread.
- Then I ran the data of group 2 in the main windows (see figure 2).
Figure 1. Groups
KBDex platform has four windows: (1) The discourse viewer which shows an overview of the discourse and selected word (top left window), (2) the network structure of students (top right window), (3) the network structure of discourse units (bottom left window), and (4) the network structure of selected words (bottom right window).
Figure 2. Main windows
An important note I have gained from this trail is that it is very important to consider what words you choose from the student discussion content. Words can represent students’ inquiry process; but it cannot represent the whole inquiry process. We, as researchers, might carry our understanding of the inquiry process based on the choice of words; yet, it cannot fully represent students’ cognitive inquiry process. For example, from the bottom left window of figure 2, we can see that some comments are isolated with the core cluster, which means that there are no common words within these comments. But, of course, students made inquiry in these comments, they just did not use the words we have chosen. Therefore, it’s important to pay attention to the word choosing process, and to describe why you choose some words other than other words. And it’s important to acknowledge the inquiry process within isolated discourse units.
Something to consider before you start KBDex analysis:
- clean your data, make sure the words you chose are consistent throughout the dataset
- consider how to use the group function, it does not necessarily need to be a traditional group as we talk in education. Like in my study, a group is assigned to students who got involved in interacting with each other in a discussion thread. The discussion thread is divided into several groups depending on the interaction
- time in your data, since KBDex can demonstrate temporality of networks, namely the evolution of networks
Finally, like Meerkat-ED, the function of network of selected words provided by KBDex can be used to demonstrate content/discourse analysis result.
If you are interested in using KBDex in your online collaborative learning research, here are some seminal work done by the research and software develop team:
Matsuzaw, Y., Oshima, J., Oshima, R., Niihara, Y., & Sakai, S. (2011). KBDeX: A platform for exploring discourse in collaborative learning. Procedia-Social and Behavioral Sciences, 26, 198-207.
Matsuzawa, Y., Oshima, J., Oshima, R., & Sakai, S. (2012). Learners’ use of SNA-based discourse analysis as a self-assessment tool for collaboration.International Journal of Organisational Design and Engineering, 2(4), 362-379.
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, 60(5), 903-921.