Skip to: Navigation | Content | Sidebar | Footer

A Layman’s Guide to Learning Analytics


There's a lot of buzz about the use of learning analytics in higher education these days, especially where online learning is concerned. The systems we use to access course materials are capable of recording a large volume of information behind the scenes, which can then be studied for matters of learning effectiveness. The emerging field of learning analytics seeks to find out what information is available and how it might inform decisions about learners and learning environments. Many of these decisions, related to accountability, quality measures, and costs, take place at the institutional level, but they affect and work to improve the formal learning experience.

The 2012 Horizon Report for Higher Education, an annual research project from EDUCAUSE and the New Media Consortium, identifies learning analytics as an emerging technology that will move into more widespread adoption in the next two to three years. The report preview [PDF] defines learning analytics as "the interpretation of a wide range of data produced and gathered on behalf of students in order to assess academic progress, predict future performance, and spot potential issues."

As students and instructors participate in online courses their specific tasks and interactions, such as submitting assignments and participating in discussion threads, can be mined for data that may be used to help schools refine course materials and develop more personalized approaches to learning. This kind of data can come from a variety of sources that include both in-class and extracurricular activities, as well as support services, that are part of the student's overall learning experience. In many cases this information has been collected in different ways, by different groups, but not thoroughly analyzed and integrated for a more holistic approach to improved learning outcomes.

 

Information Improving Learning

 

An article from Susan Grajek, vice president of data, research, and analytics at EDUCAUSE, outlines areas in which data collection and analysis from an information technology perspective can be used to answer questions in higher education – through establishing connections between technology and "institutional efficiency, learning effectiveness, and college completion."

Learning analytics applied at the course level may include:

  • Finding patterns of student use of course materials,
  • Improving the flow of communication and connection-making among course participants,
  • Making course revisions that enhance student achievement of learning outcomes,
  • Developing models that predict the achievement of individual learners, and
  • Creating personalized learning experiences based on the needs of individual students.

Many schools are already involved in analyzing learning data. There are also a number of large-scale initiatives underway to further explore new methods and implications for their use in the future.

  • Course Signals at Purdue University is a system that "detects early warning signs and provides intervention to students who may not be performing to the best of their abilities before they reach a critical point." Instructors can choose to use this application in their courses to help them track student progress using advanced modeling techniques and a range of data collected from the school's use of the Blackboard learning management system. Purdue reports improved overall course grades.
  • The Progress and Course Engagement (PACE) system at Rio Salado College also provides "automated tracking of student progress – with intervention as needed." According to Michael Cottam, associate dean over instructional design and new program development, through pilot testing of this application they can "predict with 70% accuracy, whether any given student will complete the course successfully."
  • Rio Salado is also part of a larger project being conducted by WCET with funding from the Bill and Melinda Gates Foundation. This group is working to validate the Predictive Analytics Reporting (PAR) Framework, which aims to determine "factors impacting loss, progression and completion." Six schools are working together on this effort to analyze large sets of data across institutions and identify patterns and ways that they can inform decisions about learners and learning environments.
 

Leading the Way

 

In addition to the groups and institutions already mentioned in this post, there are organizations forming to further study how all of the available data may be used and make recommendations. Take a look at some of these opportunities to find out more about learning analytics.

It's becoming more important for institutions to not only collect data about their learners and learning materials, but also thoughtfully analyze this information to inform decisions about existing and future course offerings. There are still many unanswered questions related to the process, as well as the development of interventions based on the findings. Consider how learning analytics may play a role at your institution in how learning progress is tracked, support is provided, and learning is achieved. This emerging field may improve the educational experience in the future.

February 15th, 2012 written by Staff Writers

Facebook Comments

Bookmark the permalink.