Employing data to improve online learning

Employing data to improve online learning

http://www.dreamstime.com/stock-images-web-seo-analytics-concept-flat-design-modern-vector-illustration-website-data-analysis-using-modern-electronic-mobile-image35512084EDUCAUSE’s Next Generation Learning Challenges initiative defines learning analytics as “the use of data and models to predict student progress and performance, and the ability to act on that information.” Many institutions of higher education are focusing on the data that is now available from online education.
MindEdge collects significant amounts of “click-by-click” student data from its learning resources and simulations. When it comes to employing this data as part of MindEdge’s continuous improvement approach to online learning resources, we consider the Four P’s. They are:

  • Principles
  • Progress
  • Performance
  • Pivot Points

The principles involved are simple: first, to consider the data in context; second, to analyze the data without preconceptions; and finally, to revise or adjust the learning resource based on those insights garnered from the analysis and then carefully assess the impact, positive or negative, of those changes.
We look first at student progress. How smoothly are students proceeding through the learning resource? What do completion rates look like? Are there modules or assignments where students spend longer periods of time? How does the rate of progress compare with the past? Are assignments being completed on time?
The next point of focus is performance. How are students doing on quizzes or tests? How does their performance compare with the past? Are students completing graded assignments?
Then we analyze the data looking for pivot points. Where do students encounter difficulty? Are there specific places in the learning resource where progress stalls or performance falters? Are there key trends apparent? What revisions or adjustments may be helpful at these pivot points?
We see learning analytics as a way to help in:

  • Intervening when students encounter barriers or struggle through alerting instructors to take action;
  • Personalizing the learning resource by developing adaptive learning based on the data analysis;
  • Predicting student outcomes based on past performance data;
  • Revising the learning resource to provide additional scaffolding where appropriate.

MindEdge has developed an extensive data dashboard to allow real-time monitoring of student progress and performance. Allowing instructors to review this data, and empowering them to act on it when necessary, is a key step in the process of improving student outcomes.
One sometimes overlooked aspect of learning analytics is the role of the student in monitoring their own progress and performance and “self-correcting” when they fall behind. MindEdge seeks to offer students access to this data in an easy-to-read and understand format within the learning resource—especially within adaptive learning segments—so that they feel in control of their own “learning destiny.”


Copyright © 2014 MindEdge, Inc.

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