Academic analytics refers to the applications of analytics in academic settings and includes two subtypes, namely institutional analytics and learning analytics, at the institutional level and course level respectively (Robert, Dunbar & Xavier, 2014).
Big data techniques are to aggregate, manipulate, analyze, and visualize enormous amount of data -drawing from the techniques and technologies used in the fields including statistics, computer science, applied mathematics, and economics (Manyika et al., 2011).
Relevant online videos
Perspectives -Academic Analytics (2010)
With experience in applying academic analytics, educators in Purdue University shared their opinions on the benefits and concerns of using analytics to enhance students' learning performances.
The video first introduces the meaning of academic analytics and its various uses in educational institutions. After that, education professionals talked about the challenges for educational institutions to collect and manage the data before analyses are done.
In the video, the speaker discusses both benefits and risks of adopting big data techniques into education. While we do not want the future of our children be defined simply by big data algorithms, the speaker suggests that a responsible and open-minded approach to this new technology could help reconstruct our current education system.
In this video, The Economist's data editor Kenneth Cukier pointed out that the education sector has been slow to embrace big data techniques in improving teaching and learning. He then suggests the reasons behind.
Dunbar, R. L., Dingel, M. J., & Prat-Resina, X. (2014). Connecting analytics and curriculum design: Process and outcomes of building a tool to browse data relevant to course designers. Journal of Learning Analytics, 1(3), 223-43.