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
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
Figure 2. Term network
- you can click analyze-content analysis-topic clustering to show topic communities.
, 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