The Learning Analytics Functional Taxonomy considers the question, “why are people using learning analytics?”  It describes various approaches and is organized into six categories. There are additional use cases and examples for each of the six approaches on the website, including some examples using Learn@UW tools.  These are the six categories:

• Access learning behavior
• Evaluate social learning
• Improve learning materials and tool
• Individualized learning
• Predict student performance
• Visualize learning activities

More details and examples appear below this summary table, which lists six approaches for using learning analytics, and then indicates whether a specific tool might be useful for each approach.  

• Notes about each approach are provided below. For more complete descriptions about a category, please refer to the LA Functional Taxonomy.  
• Check out the KnowledgeBase for more information about the Learn@UW suite of learning tools and analytics that you can leverage in each tool. 

Description:

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.

Note: Most tools capture user activity and might be useful to view to see whether students' are engaging with course content and activities. 

Potential questions for this approach:

• Are my students engaging in the course and getting off to a good start?
• Are students successfully using course materials to complete assignments and activities?
• Are struggling students spending a lot of time/energy on course material and still not getting it, or are they not putting in the time/energy?
• Is the assignment feedback provided on student work helpful/effective and used by students?
• How many students access optional resources? Is it worth my time to create/add these to the course? 
• Which course materials do students access the most? The least? Are there patterns of engagement? 
• When do students access course materials? How often? Do they have enough time to complete activities? 
• Is the class workload too much? How much time are students spending on course activities?
• Is my course inclusive, and does it provide diverse learning opportunities that all students can access? 

Canvas

Kaltura 

Description:

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.

Note: Social learning can occur anywhere, and there are many tools and platforms where it does occur, in addition to any synchronous conversations that happen virtually or in real space. 

• Who are the students who engage in online discussions frequently and with a variety of other students? Are they knowledge shepherds that bridge groups? 
• Who are the students who engage less frequently and/or with few other students in the online discussions?
• What can be learned about student attitudes and learning from an analysis of the content of discussion posts?
• Do students feel included, and that they belong in the course or program? Do they feel comfortable speaking up or asking questions? 

Piazza

TopHat

Feedback Fruits (College of Engineering pilot)

Description:

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 those course materials. 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.

Note: Instructors can look at engagement patterns and access to learning materials and activities to identify resources that may be overlooked, or are being revisited frequently. Most tools have some sort of engagement/access analytics, which might be useful when considering course iteration.

Potential questions for this approach:

• Does the course design align with learning objectives?
• Are course teaching practices effective for learners?
• Do learning activities and assessments help learners achieve course outcomes?
• What patterns can be seen in formative assessments to show students’ progress toward learning outcomes?
• Which learning materials and activities are MOST helpful and which are LEAST helpful for a given module? 
• Are course materials, activities, assessments and teaching practices inclusive for all learners? Does the course support diversity, equity, inclusion, belonging? 

Canvas

Kaltura

Description:

Adaptive or individualized learning systems apply learning analytics to customize course content for each learner. 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.

Note: Some tools are designed to customize course content and activities for learners; for example Engage eText or other digital learning tools (eg publishers’ tools with homework). Other tools have features that you can use to create a customized experience.  

Potential questions for this approach:

• Do students need practice with more basic skills and concepts? Would students benefit from supplemental or more advanced course materials?
• Can students choose their own learning path, or choose content based on their learning preferences, eg. can they choose to view a video or read an article instead?
• Can I provide continuous feedback to support independent learning? 
• If students have more opportunities to practice, study and review, will they do better with learning outcomes? 
• Will individualized learning provide more flexibility for diverse learners and increase equity, inclusion and belonging?  

Canvas

Engage eTexts and Digital Learning Tools

Description:

Based on already existing data about learning engagement and performance, learning analytics applies statistical models and/or 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.

Note: While we don't have a predictive tool, think about how your course is structured, key assignments and your own experiences. Where are there key points in their learning that seem to indicate they are on-track, performing at a high level, or needing more assistance? 

Potential questions for this approach:

• How can we use data (for example, collected in a learning management system or other) to  predict how students may perform in a course, a degree program, or set of courses?
• What ways can we leverage learning analytics data to support student learning, whether students are struggling or begin to struggle?
• What learning analytics approaches can be used to help students from dropping out of college, or their degree programs? 
• Can we use predictive analytics to support equity, diversity, inclusion and belonging? How do we mitigate biases?  

Canvas

Description:

This approach traces all learning activities performed by users in a digital ecosystem to produce visual reports on the learner behavior, as well as performance. 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.

Note: Many tools provide visualizations; this category as defined in the LA Functional Taxonomy also includes performance or grades, in addition to just how/when/if a student accesses course materials and activities. 

Potential questions for this approach:

• How much time are students’ successful peers spending interacting with different course materials?
• What time of the week/day are students engaging with course materials? Can this help inform instructors about when to send out timely messages?
• Are students viewing important course content, or viewing other course content less, and can this help instructors structure their courses differently?     

Canvas