Moodle and Learning Analytics: Unlocking the power of eLearning data

June 21, 2023 By Niamh Macdonald

In today’s data-driven world, educators and administrators are continually seeking new ways to improve their eLearning courses and programs. Moodle offers powerful learning analytics tools to help optimize the learning experience and achieve better outcomes for both learners and organizations. This blog will explore the role of learning analytics in education, the various types of learning analytics, and how Moodle’s core functionalities, plugins, and certified integrations can support data-driven decision-making in eLearning.

Understanding learning analytics

Learning analytics refers to the process of measuring, collecting, analyzing, and reporting data about learners to gain deeper insights into their learning experiences and optimize the eLearning environment. This data-driven approach helps educators and organizations better understand how learning occurs and develop best practices to support student development, engagement, and success.

Learning analytics in education

Learning analytics can be applied in various educational settings, including K-12 and higher education. Some common uses of learning analytics in these contexts include:

  • Identifying learners at risk of failing a course or dropping out of their studies without additional support.
  • Developing best practices for students, educators, and institutions to better understand how learning occurs.
  • Supporting student development, including lifelong learning skills and strategies.
  • Providing personalized and timely feedback to students.

Types of learning analytics

There are four main types of learning analytics that can help educators and organizations improve their courses or staff training. These types range from basic to more advanced levels of analysis:

1. Descriptive analytics

A method used to search and summarize data to identify patterns of behavior and performance.

2. Diagnostic analytics

Helps educators and organizations understand what has occurred within a specific learning journey.

3. Predictive analytics

Uses existing data findings to predict what is likely to happen to learners in the future.

4. Prescriptive analytics

Seeks solutions to questions about what should be done to create the best learning outcomes.

Analytics for enterprise learning

Corporate or enterprise learning analytics play a crucial role in developing a strong workforce and measuring the return on investment of learning initiatives. By providing information about how corporate training aligns with organisational goals and employee needs, learning analytics can help organizations:

  • Make data-driven decisions about what and how employees learn
  • Identify knowledge gaps and develop plans to support employees
  • Target improvements across employees and provide a personalized learning experience, improving employee wellbeing and retention rates
  • Mitigate risks through risk and learner gap assessments and compliance training, equipping organizations to make the best decisions for success.

Moodle’s learning analytics capabilities

The Moodle Learning Analytics API is an open system that can serve as the foundation for a wide variety of models to provide learning analytics in Moodle. Models can contain indicators (predictors), targets (the outcome being predicted), insights (the predictions themselves), notifications (messages sent as a result of insights), and actions (offered to recipients of messages, which can also become indicators).

Moodle LMS core offers three analytics models: “Students at risk of dropping out,” “Upcoming activities due,” and “No teaching.” Other models can be added to the system by installing plugins, as discussed in the “Moodle Analytics Plugins” section below.

In Moodle LMS, educators can:

  • Get predictions with machine learning algorithms.
  • View insights and predictions, such as selecting insights per course with the “Students at risk of dropping out” model.
  • View previous evaluation logs, such as learning curve graphs and TensorBoard logs.
  • Edit models by modifying the list of indicators or the time-splitting method (previous predictions will be deleted when a model is modified)

There are also over 30 useful learning analytics plugins, which can be downloaded directly from the Moodle plugins directory.

Moodle Certified Integrations for learning analytics

Moodle Certified Integrations IntelliBoard and LearnerScript seamlessly integrate with Moodle to provide a comprehensive range of learning analytics and enhanced reporting.

IntelliBoard collects and analyzes your LMS data to help you evaluate and improve the performance of your eLearning initiatives. While LearnerScript is a Moodle Certified Integration that helps you measure learning outcomes, understand unique learner needs, and get actionable insights on how to improve your courses and programs.


Moodle’s range of learning analytics tools, from core features to plugins and certified integrations like IntelliBoard and LearnerScript, make eLearning analytics more accessible and manageable. With a greater understanding of how learners respond to courses, educators and organizations can create an eLearning experience that benefits everyone, ensuring every element helps learners achieve their goals.

Learn more about learning analytics with Moodle

Unlock the power of data-driven insights in eLearning.