use Meerkat-ED to do social network analysis and discourse analysis

Meerkat-ED is a software designed by Dr. Reihaneh Rabbany 

Meerkat-ED can be used to analyze two kinds of networks: participants’ interaction network and the network of terms they have used in their interactions.
I applied a small portion of my data in this tool, here is the process:
  • transfer your data into .txt file, if you don’t use Moodle, please refer to samplefile.txt for format. The online course I am studying was not based on Moodle, so I manually transferred a very small portion of my data into the right format
  • open the txt file in Meerkat-ED, two networks (i.e., interaction network and term network) were automatically generated

in the interaction network, you can change time frame at the bottom to demonstrate network during different duration

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Figure 1. Student interaction network

in the term network, you can click student’s name at the right top window, to show terms this student used

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Figure 2. Term network

  • you can click analyze-content analysis-topic clustering to show topic communities.

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Like KBDex, Meerkat-ED is a useful tool to analyze online collaborative learning. One strength Meerkat-ED has is you can set different text analysis methods, and different types of words, e.g., noun or verb. One thing Meerkat-ED is different form KBDex is that, you cannot choose specific words in Meerkat-ED. In addition, you should also pay attention to data clean process before you import your data into Meerkat-ED.  You might need to make noun words consistent throughout your data file. For example, I should have changed all “communities” to “community”, or “questions” to “question”. This part can be tricky.
Another creative use of Meerkat-ED and KBDex is that you can use them to visually demonstrate content or discourse analysis results. For instance, if you have coded discussions based on some coding scheme, you have got codes for each analysis unit. Then you can use Meerkat-ED and KBDex to show the networks of codes (where links represent co-occurrence of codes in the same sentence), rather than the networks of words, phrases of the discussion content itself. This might be an interesting way to demonstrate content or discourse analysis result. Widely-used ways to demonstrate content or discourse analysis results are “code and count” table, pie/bar charts, or sequential/path analysis. The function of network of terms supported by Meerkat-ED can create new visualizations for researchers.
Overall, Meerkat-ED is a solid tool to do data mining in online collaborative learning.
If you are interested in applying this tool in your reserach, please refer to more info:
Rabbany, R., Takaffoli, M., & Zaïane, O. R. (2011). Analyzing participation of students in online courses using social network analysis techniques. In Proceedings of educational data mining.
Rabbany, R., Elatia, S., Takaffoli, M., & Zaïane, O. R. (2013). Collaborative learning of students in online discussion forums: A social network analysis perspective. In Educational data mining (pp. 441-466). Springer International Publishing
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use KBDex in online collaborative learning research

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).
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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).
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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 Engineering2(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 development60(5), 903-921.