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The Moodle Analytics API allows Moodle site managers to define prediction models that combine indicators and a target. The target is the event we want to predict. The indicators are what we think will lead to an accurate prediction of the target.
Below are the features of Moodle Learning Analytics:
Two types of models supported:
Machine-learning based models, including predictive models
"Static" models to detect situations of concern using simple rules
Three built-in models: "Students at risk of dropping out", "Upcoming activities due" and "No Teaching".
A set of student engagement indicators based on the Community of Inquiry.
Built-in tools to evaluate models against your site's data
Proactive notifications using Events
A list of suggested Actions is provided with the Insight notifications for each model. For example, in the Students at risk of dropping out model, instructors can easily send messages to students identified by the model, or jump to the Activity report for that student for more detail about student activity within the course
An API to build indicators and prediction models for third-party Moodle plugins
Machine learning backend plugin type - supports PHP and Python, and can be extended to implement other ML backends
The system can be easily extended with new custom models, based on reusable targets, indicators, and other components. For more information, see the Analytics API developer documentation.