What are the pedagogical uses of learning analytics?

Last Updated: Tue January 22, 2019

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.

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 — including online discussions, Facebook and Twitter —  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 the Canvas learning management system and Pattern.

Canvas
Canvas, the learning management system in the Learn@UW suite of learning technologies, offers a couple of options for learning analytics work, including the following:

  • Course Analytics is part of the existing Canvas interface available for instructors that provides several views of student activity, submissions and grades.
  • Other student course-access reports can be accessed that use third-party tools.
  • Instructors can also look at other views in Canvas to learn more about student activity and course materials. For example, on the People page in Canvas you can see when students last accessed the course. In addition, Quiz Statistics may be useful to see where students need additional support.

Pattern
Often referred to as a FitBit for studying, Pattern is an interactive study log or student quantified-self tool that compiles student-generated data, and provides reports to both students and instructors. It provides customized recommendations to students based on comparisons of their activities with their classmates.

Are the Tools Integrated into Canvas?

Currently, Canvas Analytics is part of the Canvas interface. Other learning analytics tools in development are expected to be integrated into Canvas. However, some learning analytics tools such as Pattern are available external to the Canvas course, and will not connect with Canvas in any technical way.

Who Can I Talk to For More Information?

The Learn@UW-Madison learning technology consultants are happy to help you choose the best tool to fit your needs and start using it to improve student success. You can contact us via email at academictech@doit.wisc.edu or phone at (608) 262-5667. In addition, the Learn@UW KnowledgeBase offers helpful documents for instructors, course owners and students.