Hi Ji Wenbin,
Thank you very much for sharing these findings. I find your
presentation one of the most inspiring. I am personally interested in the network
analysis, but perhaps even more in its interpretation. Thus I see further steps in front of your work or similar projects. The data collection and display is the
first of them. Analysis and interpretation is a more challenging step. How meaningful the strong node is? What do we mean by 'strong'? Looking for people who have more connections and
discarding the other weaker nodes seems like a simplistic interpretation. But
fortunately this is not your conclusion. :)
I remember that you were asking what it all means for
learning experience? What we as educators/trainers or learners can learn from it? This would be
necessary to understand in order to prepare some recommendations based on
analysis and interpretation -- another step.
This topic is quite complex, but also very current. I
mentioned to you already connectivist learning theory which somewhat relies on
the network theory claiming for example that "nurturing and maintaining connections is needed to facilitate continual learning"(Siemens, 2004). Furthermore it states that "learning is a process of connecting specialized nodes or information sources" and that "a learner can exponentially improve their own learning by plugging into an existing network." (Connectivism, nd). Not everybody agrees with connectivist statements, or even
with its claim of being a learning theory. Nevertheless it still an interesting path to explore.
Another
current topic related to data analysis are learning analytics. Learning analytics refer to how the data collected in an educational context, produced by students, teachers, learning management system, can be interpreted to improve the learning experience and learning environment.
Learning analytics serve different purposes (Powell, MacNeil, 2012), like
1. for individual learners to reflect on their achievements and patterns of behaviour in relation to others;
2. as predictors of students requiring extra support and attention;
3. to help teachers and support staff plan supporting interventions with individuals and groups;
4. for functional groups such as course teams seeking to improve current courses or develop new curriculum offerings; and
5. for institutional administrators taking decisions on matters such as marketing and recruitment or efficiency and effectiveness measures.
There is a good introduction to learning analytics by G. Siemens (2013) available on this website: http://www.learninganalytics.net/
I will check out Pajek. Would you like to continue this discussion in CALMet Commons? Not sure you can access it from Beijing...?
Cheers,
Maja
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Connectivism (nd). Retrieved from: http://www.connectivism.ca/about.html
Powell, S., & MacNeil, S. (2012). Institutional Readiness for Analytics A Briefing Paper. CETIS Analytics Series. JISC CETIS, Retrieved from: http://publications.cetis.ac.uk/wp-content/uploads/2012/12/Institutional-Readiness-for-Analytics-Vol1-No8.pdf.
Richards, G. (2011). Measuring Engagement: Learning Analytics in Online Learning. Proceedings Electronic Kazan 2011. Retrieved from: https://www.academia.edu/779650/Measuring_Engagement_Learning_Analytics_in_Online_Learning
Siemens, G. (2002), Connectivism: Learning Theory for Digital Age. Retrieved from: http://www.elearnspace.org/Articles/connectivism.htm
Siemens, G. (2013). Structure and logic of analytics.(Video) Retrieved from: http://www.learninganalytics.net/