What are the pedagogical uses of learning analytics?

Last Updated: Tue September 15, 2020

What is learning analytics?

Learning analytics is much more than a tool; rather it is a process and can span many diverse approaches to help improve learning outcomes. The focus of learning analytics is to provide actionable information that can improve teaching and learning.

While there are many definitions of learning analytics, UW-Madison’s Learning Analytics Roadmap Committee (LARC) has contextually defined it as the undertaking of activities designed to improve student outcomes by informing structure, content, delivery, or support of the learning environment. In practical terms learning analytics uses data generated within courses to inform and improve teaching and learning on our campus.

More information about learning analytics at UW-Madison is available from the Vice Provost for Teaching and Learning and DoIT Academic Technology. Also before leveraging any learning analytics approaches please review UW-Madison’s Learning Analytics Guiding Principles, which state that students are real and diverse individuals, and not just their data or information. The principles — beneficence, transparency, privacy and confidentiality, and minimization of adverse impacts — aim to uphold the dignity of students while ensuring learning analytics are used to improve educational outcomes.

What can I use learning analytics for?

There are a variety of ways that an instructor can use learning analytics. To help instructors understand the educational benefits, the Blended Learning Fellowship on Evidence-Based Teaching explored and adapted a Learning Analytics Functional Taxonomy. The following are the different categories included in the taxonomy.

Access Learning Behavior
Learning analytics can collect user-generated data from learning activities and offer trends in learning engagement. Analyzing those trends can reveal students’ learning behavior and identify their learning styles. This approach measures engagement and student behavior rather than performance, giving instructors insight into how their students interact with their course materials.

Evaluate Social Learning
Learning analytics can be applied to investigate a learner’s activities on any digital social platform — such as online discussions in Canvas —  to evaluate the benefits of social learning. This measures and tracks student-to-student and student-to-instructor interactions to help understand if students are benefiting from social learning in their course.

Improve Learning Materials & Tools
Learning analytics can track a student’s usage of learning materials and tools to identify potential issues or gaps, and offer an objective evaluation of learning materials and tools. This allows instructors to make deliberate decisions about modifying approaches. Using aggregate student data, instructors can see ways to improve the process of learning or the structure of their course.

Individualized Learning
Adaptive or individualized learning systems apply learning analytics to customize course content for each learner. Furthermore, user profiles and other sets of data can be collected and analyzed to offer greater personalized learning experiences. This approach uses continuous feedback to help individual students in their learning.

Predict Student Performance
Based on already existing data about learning engagement and performance, learning analytics applies statistical models and machine learning techniques to predict later learning performance. By doing so, likely at-risk students can be identified for targeted support. Focus is on using data to prompt the instructor to take immediate action to intervene and help a student course correct before it is too late.

Visualize Learning Activities
This approach traces all learning activities performed by users in a digital ecosystem to produce visual reports on the learning process. The reports can support both students and teachers to boost learning motivation, adjust practices and leverage learning efficiency. This is about facilitating awareness and self-reflection in students about their learning patterns and behaviors.

What are my learning analytics tool options?

Learning analytics is a relatively new and fluid space. Institutions, vendors and consortiums like Unizin are actively developing new tools to collect and analyze data about learning environments. Currently the most readily available learning analytics tools on campus are analytics tools within Canvas, Kaltura, and the Unizin Engage eText. Additionally, the Learner Engagement Analytics Dashboard (LEAD), which uses data from all three of those tools, is now available to Principal Instructors. More information about these tools and how to use them is listed below: 

Are the tools integrated into Canvas?

Currently, New Analytics is part of the Canvas interface, you can also access Quiz Logs and course-access reports in Canvas. Other learning analytics tools in development are expected to be integrated into Canvas. However, some learning analytics tools are available external to the Canvas course, and will not connect with Canvas in any technical way. Also while not technically integrated within Canvas, LEAD does use student-generated data from Canvas in its visualizations.

Who can I talk to for more information?

Our learning technology consultants are happy to help you choose the best tool to fit your needs and start using it to improve student success. Contact the DoIT Help Desk to schedule an appointment with a DoIT Academic Technology consultant. In addition, the Learn@UW KnowledgeBase offers helpful documents for instructors, course owners and students. Other learning analytics information, resources, events and news are available on the DoIT Academic Technology’s Evaluation Design & Analysis page as well as the Office of the Provost’s Teaching & Learning site.

 

Photos by Jeff Miller / UW-Madison