Admin tools: Inspire
This is Moodle's offical descriptive and predictive analytics engine, implementing machine learning backends.
Currently it's a plugin for 3.3, but once tested and improved it will go into core.
- One built-in prediction model: Students at risk of dropping out of courses.
- 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 for instructors using Events
- Instructors can easily send messages to students identified by the model, or jump to the Outline report for that student for more detail about student activity
- 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
- Project inspire information (mostly for educators / researchers): https://moodle.org/course/view.php?id=17233
- Architecture overview (mostly for developers / systems administrators / researchers): https://docs.moodle.org/dev/Project_Inspire_API
- Prototype (for everybody): http://prototype.moodle.net/inspirephase1/ (may not be up to date as development will continue on top of Moodle core).
The initial 3.3-r1 version of the Inspire plugin has some limitations:
This version is installed as a third-party plugin. Future versions will become part of Moodle Core.
This version only reads activity logs from the standard log store. A log store selector will be added in future versions of the plugin.
This version requires a site with previous course completion data (final grades and/or completion criteria and/or defined competencies). This data is used to train the prediction engine.
The prediction model included with this version requires that courses have fixed start and end dates, and is not designed to be used with rolling enrollment courses. Models that support a wider range of course types will be included in future versions of Inspire.
Models and predictions are only visible to teachers and administrators at present.
We are continuing to enhance Inspire, and expanded capabilities will be released going forward. To help contribute to our progress, please join the conversation at the Project Inspire Community. In particular, we need data sets from a wide variety of Moodle-using institutions in order to be able to ship a working prediction model that does not depend on local site data before it can be used.