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Academic Analytics and Big Data Techniques
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
Academic analytics
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.
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 Academic analytics
Learning analytics in a nutshell (2020)
This video presents a brief introduction to learning analytics, covering its key elements, as well as sources and uses of data.
What is analytics? (2012) 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. https://www.youtube.com/watch?v=Gm-HbTvKw_0
What is Learning Analytics? (2022)
In this video, Director of the Learning Analytics Research Network at New York University, Alyssa Wise, gives an overview on learning analytics, covering the aspects about data, computation, theory, insight and action, as well as issues to note for implementation.
Big data techniques MindCET snapshot -big data & education (2014) 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. https://www.youtube.com/watch?v=7MrWQUMgcyk
References
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.
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