Analytics FAQ

From MoodleDocs

Will I start getting predictions as soon as I enable the model?

Static models do not require training, and will begin to deliver insights as soon as they are enabled (and the circumstances that trigger the model occur). Machine-learning based learning analytics models, such as Students at risk of dropping out, must be trained on your site data before they can generate predictions. The Moodle Learning Analytics machine-learning engine needs historical data (previous courses or other activity, depending on the model), so it will need to be enabled on a production site or a copy of your production site. The model training process happens in the background once the model is enabled on your Moodle site. Once the model has been trained, you will start to receive predictions about current courses or other components. See Managing analytics for more information on how to manage notifications.

Is there a way to export and import model data between sites?

Model training data can be exported from one site and placed in the model data directory of a new site. This consists of a file of calculated target and indicator values for each sample examined by the model, along with some header information. No personally identifying information is included, but one row per sample (e.g. per course enrolment and analysis interval) is included. We are working to collect model data from many sites to construct a training set we can provide with Moodle installations.

Is there a way to export and import the settings of a model between sites?

Yes. Models can be created and tested on one site, and can be exported with weights and imported to a new site. This data does not reference individual users or courses in any way, and can be safely shared with researchers or other sites. On the new site, a model can be re-trained after import, or the imported model weights can be tested on historical site data to evaluate the accuracy of the model on the new site. We are working to collect model configuration and weighting data from many sites to construct a pre-trained model we can provide with Moodle installations.

How can I disable Moodle Learning Analytics notifications, or restrict them to certain users, while still enabling models for testing purposes?

Notifications go to users with the "analytics:listinsights" capability in the context of the prediction-- what this means for the Students at risk of dropping out model is that notifications go to teachers in each course. What you can do is modify the "Teacher" role (editingteacher) to remove that capability. Currently that capability is also turned on for Managers by default. If you want to restrict the notifications to a few individuals, make a new role with just this capability, and assign that role to the individuals you wish to grant permission to in each context.

How accurate are the model predictions?

This varies depending on the quality and quantity of site data (including how many activities are in each course and what percentage of the course is conducted online in Moodle). See Using analytics: Review evaluation results for more details on how to review model accuracy.

How can I create my own Learning Analytics models?

New machine learning models can be created by using the Analytics API, by importing an exported model from another site, or by using the web UI. For more information, see Using analytics: Creating and editing models. (Note: "static" models cannot be created using the web UI at this time.)

Why am I seeing the error "Not enough course activity between the start and the end of the course"?

Courses that are used for training the "Students at risk of dropping out" model need to have a minimum of 10 activity logs for user. So if your course has 322 students a minimum of 3220 activity logs between the start and the end of the course are required to consider this course valid for training.

If you are seeing this error, check that:

  • The number of activity logs between the start and the end of the course is more than the number of students * 10
  • The course start and end date are correctly set
  • The start and end of the student enrolments are correctly set
  • Use a longer analysis interval (e.g. quarters or quarters accumulative rather than tenths), to increase the number of log entries per student per analysis interval.

Any further questions?

Please post in the Analytics and reporting forum on