ECI 519: Special Topics in DL&T: Learning Analytics
Learning Analytics was one of my most difficult, yet most rewarding classes. In the course, we measured, collected and analyzed both quantitative and qualitative data using to find trends and make decisions. We learned that the key to a successful Learning Analytics cycle was to close the loop of the cycle. We collect data from the learning environment; we analyze the data, but the key is to act on that data, to make interventions and recommendations. The type of data analyzed determined the appropriate tool for use.
I used Tableau in analyzing the quantitative data released from the North Carolina End of Grade (EOG) Assessment Report, focusing on the elementary schools in the county where I previously taught for several years. You can click on the tabs at the top to see graphs of different topics.
This is the narrative for my Tableau Project.
One of the qualitative studies we did was a social network analysis, analyzing the Twitter hashtag, NCEd. We analyzed the relationships among those tweeting, identifying cliques and outliers, as well as those who were the central nodes, meaning those with the most activity, whether it be in-degree centrality (receiving the communication), out-degree centrality (communicating out to others), or both. The tool used for this analysis was Gephi, which is not the ideal tool to use, but what would run on my Mac. Students with PCs used NodeXL, which is much more user-friendly and has more capabilities for analysis.