Teplovs, Fujita, & Vatrapu: Generating predictive models of learner community dynamics through the visualization of networks based on social network analysis and latent semantic analysis.
Communicating the results of learning analytics is critical to ensuring that analytics have an impact in an organization. The growing popularity of visualization theorists such as Edward Tufte and Stephen Few indicate the attention now being paid to how ideas, stats, and visualizations are used to communicate important concepts. Visualization is a growing discipline (and one that should be it's own course within SCIS!), and our brief treatment of it here is only a surface overview. We won't be going into detail about particular visualization techniques or about different approaches to communicating stats. Instead, as the readings and videos this week demonstrate, we will look at the power of a visual communication.
Demo: Stunning data visualization in the Allosphere [Video]. (2009). Retrieved from https://www.ted.com/talks/joann_kuchera_morin_stunning_data_visualization_in_the_allosphere
Hans Rosling: The joy of stats (OIULearn) [Video]. (2011). Retrieved from https://www.gapminder.org/videos/the-joy-of-stats/
Teplovs, C., Fujita, N., & Vatrapu, R. (2011 March). Generating predictive models of learner community dynamics through the visualization of networks based on social network analysis and latent semantic analysis [Video]. Presented at the first International Conference on Learning Analytics and Knowledge, Banff, Alberta.
This is a work week. We have now covered the course content that relates to tools and methodologies in learning analytics. Over the next two weeks we will focus on institutional-level analytics. In the final week, we will explore concerns around privacy and data ownership.
Updated August 17 2021 by FST Course Production Staff