A widely used learning and assessment environment that offers embedded learning analytics is Moodle, a popular open‑source Learning Management System (LMS). Moodle supports learning analytics both through its built‑in reporting tools and via integrations with analytics platforms like IntelliBoard and LearnerScript, making it a good example of how analytics are integrated into educational technology. Moodle+1

How it works:
Moodle collects data from student interactions within courses—such as page views, quiz attempts, activity completions, and login patterns—through system logs and activity tracking. These data are processed by Moodle’s Learning Analytics API or external plugins to generate reports and dashboards. Educators can use built‑in models that predict learner behaviors, such as “students at risk of dropping out,” upcoming activities due, and gaps in engagement. Plugins like IntelliBoard extend these capabilities by consolidating Moodle data into visual dashboards, offering customizable analytics and predictive insights that help identify learners who need support earlier. Administrators, teachers, and learners can view these insights to understand patterns of engagement and performance over time. Moodle+1

What the effects are:

Improved instructional decisions: Learning analytics help educators identify which students are struggling and which course elements may need adjustment, enabling more targeted teaching interventions and adaptive support. Moodle

Early identification of risk: Predictive models alert instructors to learners at risk of disengagement or falling behind, allowing for earlier academic support which can improve retention and success rates. Moodle

Greater awareness of learning patterns: Dashboards and reports give both teachers and learners a clearer picture of activity completion, participation frequency, and performance trends, fostering reflective learning and planning. Moodle

Data‑driven curriculum improvement: At the institutional level, aggregated analytics inform curriculum design, resource allocation, and pedagogical strategies by revealing broader trends in learner behavior. Moodle

Overall, embedded analytics in environments like Moodle turn raw interaction data into actionable insights that support personalized feedback, targeted teaching interventions, and more informed decisions about instructional design. The effects seen in practice include better identification of at‑risk learners, improved engagement tracking, and a stronger basis for reflective educational strategies.