Answer to "What software tools are used in learning analytics?"

 

Topic Editor: Dr. Martin Pusic, New York University

 

There are a wide variety of software tools used for learning analytics.  In fact, one of the hallmark activities of learning analytics is to create afferent "listeners" in a digital environment so as to greedily collect as much data as could be relevant to improving learning.  These listener programs are layered into an application so as to create log files with rich time-stamped data on all learning activities.  How long did the learner spend on the page?  What did they click?  What did they not click?  Did they leave a comment?  Where did they go next?  Thus the software tools are programs that can be custom-developed for the particular learning activity.  See the Pecaric reference for a radiology example in which, within a digital learning environment for learning ankle radiograph interpretation, the investigators collected data on how learners switched between views of an ankle radiograph, whether they consulted the historical information, how sure they were of their answers, how long they spent reviewing the feedback information and the like.  These data have the potential to generate richer feedback for the learner. 

 

 

 

 

In learning analytics analysis, the full spectrum of Data Science tools can be brought to bear.  In fact, one of the key enablers of rich learning analytic processes is the increasing use of these powerful tools, and a switch from more standard ones.  In the diagram below, we list only a few of the new tools and their more traditional analogues. 

 

 

 

 

 

 

 

 

 

 

 

Below is a listing of popular programs in Data Science.

 

 

 

 

 

 

 

 

  1. Pecaric M, Boutis K, Beckstead J, Pusic M. A big data and learning analytics approach to process-level feedback in cognitive simulations. Academic Medicine. 2017 Feb 1;92(2):175-84.

  2. Bienkowski, Marie, Mingyu Feng, and Barbara Means. "Enhancing teaching and learning through educational data mining and learning analytics: An issue brief." US Department of Education, Office of Educational Technology 1 (2012): 1-57.

  3. Baker, Ryan Shaun, and Paul Salvador Inventado. "Educational data mining and learning analytics." In Learning analytics, pp. 61-75. Springer New York, 2014.

  4. Lang, Charles, George Siemens, Alyssa Wise, and Dragan Gasevic, eds. Handbook of learning analytics. SOLAR, Society for Learning Analytics and Research, 2017.  Free download at:  https://solaresearch.org/hla-17/

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