Learning analytics environments like Canvas LMS and Moodle empower educators to track, interpret, and respond to student learning behaviors in real time. These platforms use data to personalize instruction, improve engagement, and support timely interventions.

How It Works?

Learning analytics involves the collection, analysis, and interpretation of data about learners and their contexts. In platforms like Canvas LMS, instructors can access dashboards that visualize:

Student activity logs: Assessment performance trends

Engagement metrics: These insights are generated through backend algorithms that aggregate and analyze user interactions. Educators can then use this data to identify at-risk students, adjust pacing, or offer differentiated support. For example, Moodle’s Learning Analytics API allows teachers to set up predictive models. These models flag students who may need help based on patterns like missed deadlines or low quiz scores. Teachers receive alerts and can initiate support before performance declines.

 Effects on Learning and Assessment

1. Improved Feedback Loops: Real-time data enables formative assessment that is timely and targeted.
2. Personalized Learning Paths: Analytics help tailor content and assessments to individual needs.
3. Early Intervention: Predictive analytics identify struggling students early, reducing dropout rates.
4. Reflective Teaching: Teachers gain insights into which materials or methods are most effective.

These environments shift assessment from a static event to a dynamic, ongoing process that supports Assessment for Learning principles.

Media and References

1. What is Learning Analytics – SoLAR
2. Using Learning Analytics to Assess Student Learning in Online Courses – ERIC
3. Learning Analytics and Assessment – BJET