The Inspire plugin implements open source, transparent next-generation learning analytics using machine learning backends that go beyond simple descriptive analytics to provide predictions of learner success, and ultimately diagnosis and prescriptions (advisements) to learners and teachers. From Moodle HQ.
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.
Includes
- A built-in prediction model: Students at risk of dropping out of courses.
- A set of student engagement indicators based on the Community of Inquiry.
- An API to build prediction models for your plugins
- Machine learning backend plugin type
More information
Note that Students at risk of dropping out of courses prediction model is experimental, we are still working on improving the model to make it applicable to all kind of courses.
Requirements
- Moodle 3.3
- PHP 7.x (required if you are using the PHP predictions processor, more info below)
Installation
https://docs.moodle.org/32/en/Installing_plugins#Installing_a_plugin
Configuration
A couple of important sitewide settings:
Predictions processor
Prediction processors are the machine learning backends that process the datasets generated from the calculated indicators and targets and return predictions. This plugin is shipped with 2 prediction processors:
- The PHP one is the default, the only requirement is PHP 7.x, no other system requirements
- The Python one is more powerful and it generates graphs with the model performance but it requires setting up extra stuff like Python itself (https://wiki.python.org/moin/BeginnersGuide/Download) and the moodleinspire package.
pip install moodleinspire
Time splitting methods
The time splitting method divides the course duration in parts, the predictions engine will run at the end of these parts. It is recommended that you only enable the time splitting methods you could be interested on using; the site contents analyser will calculate all indicators using each of the enabled time splitting methods. The more enabled time splitting methods the slower the evaluation process will be.
Usage
Please note that it is very important to properly set courses start and end dates. If both past courses and ongoing courses start and end dates are not properly set predictions will not be accurate. We include a command line interface script (https://github.com/moodlehq/moodle-tool_inspire/blob/master/cli/guess_course_start_and_end.php) that tries to guess course start and end dates by looking at the student enrolments and students' activity logs, but you should still check that the guess start and end dates script results are correct.
Process the site contents
Using the command line interface
// Evaluate the model using your site' contents (this is optional but useful as you want to see how the different time splitting methods perform)
php cli/evaluate_model.php --modelid=1 --non-interactive
// Enable the model (you can replace quarters for any other splitting method).
php admin/tool/inspire/cli/enable_model.php --modelid=1 --timesplitting=\"\\tool_inspire\\local\\time_splitting\\quarters\"
// These are the 2 tasks that will run through cron regularly, you can force their execution.
php admin/tool/task/cli/schedule_task.php --execute=\\tool_inspire\\task\\train_models
php admin/tool/task/cli/schedule_task.php --execute=\\tool_inspire\\task\\predict_models
Using the web interface (not for big sites)
- Go to Site administration > Reports > Inspire models
- Select Evaluate from the Actions drop down menu (this is optional but useful as you want to see how the different time splitting methods perform)
- Select Edit from the Actions drop down menu, check 'Enabled' checkbox and select the time splitting method you prefer
- Select Execute to start getting predictions
View predictions
Note that you will first need to enable and execute the model.
As a manager/admin you can access model predictions by:
- Going to Site administration > Reports > Inspire models
- Selecting a context from the predictions list menu
As a teacher you can access model predictions by:
- Going to an ongoing course and Course administration > Reports > Insights
User with tool/inspire:listinsights capability will receive notifications when new predictions are available for them. e.g. Course teachers will receive a notification about their students at risk of dropping out.
Limitations
This plugin only reads activity logs from the standard log store. A log store selector will be added in future versions of the plugin.
