New in Canvas Catalog: Course Recommendations Based on Real Enrollment Trends

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Building on Our First Recommendation Feature

At Canvas Catalog, we are continually improving how learners discover relevant courses. Earlier this year, we introduced a recommendation feature that helped surface suggested courses on the Homepage. This allowed institutions to increase course visibility and guide learners toward additional opportunities.

Now, we are taking it a step further. Our new Recommendations Tab on the course listing page builds on this foundation, using real enrollment trends from the last six months to surface Frequently Bought Together courses. This makes recommendations more dynamic and data-driven, helping learners explore courses that others have already enrolled in together.

What’s New?

• A Dedicated "Recommendations" Tab—Now available on course listing pages, displaying courses frequently bought together over the last six months.
• Automatically Generated Suggestions—Recommendations are based on actual purchase data from the past six months, dynamically reflecting learner enrollment trends.
• Fallback Recommendations—If not enough "frequently bought together" data is available, the system suggests popular or trending courses instead.
• Expanded Course Visibility—Recommendations can include courses from the same subcatalog, other subcatalogs, or the root catalog, ensuring learners see relevant offerings across their institution.

How This Evolves Course Discovery in Catalog

Our first recommendation version helped improve course discovery, but this new update goes further by dynamically reflecting real purchasing behavior.

With this enhancement:

• Recommendations on the listing page are fully automated, based on real enrollment data
• They appear directly within course listings, ensuring that suggestions are shown at the moment of decision-making, when a learner is actively considering enrollment.
• The system continuously updates recommendations, ensuring they remain relevant over time.

While our Homepage recommendations remain a valuable tool for showcasing popular or trending courses, this new tab ensures that learners see suggestions tied to real purchasing patterns while browsing individual courses.

How It Works

1. A learner visits a course listing page.
2. The "Recommendations" tab appears.
3. The system analyzes past six months of enrollments and suggests courses frequently bought together.
4. If not enough data exists, the system defaults to Catalog-wide most popular or trending courses.
5. Learners can explore and enroll in recommended courses seamlessly.

What Admins Can Configure

While recommendations are automatically generated, admins still have control over key aspects of the feature:

• Enable or disable recommendations at the root catalog level.
• Set the number of recommendations displayed.
• Decide whether to allow fallback recommendations when frequently bought together data is limited.
• Allow subcatalog admins to override settings, ensuring flexibility for different departments and learning programs.

This ensures that institutions maintain control over their Catalog experience while benefiting from automated, real-time recommendations.

How This Benefits Learners and Institutions

With the new Recommendations Tab, learners now receive smarter, more relevant course suggestions while browsing. This enhancement:

• Encourages continued learning by surfacing courses that align with real enrollment trends.
• Reduces friction in course discovery, making it easier for learners to find their next opportunity.
• Provides institutions with a hands-off way to increase course visibility and engagement.

By combining automated recommendations with admin flexibility, this update represents the next step in our mission to make learning more accessible and intuitive.

Get Started

The Recommendations Tab is now live in Canvas Catalog. Enable it today to enhance course discovery, drive engagement, and help learners explore valuable learning opportunities.

Turn on Recommendations today and let real enrollment data guide your learners to their next course.