Disponible en Español.

Post Pages - Post Inline - WIRIS

Corrections made on June 15th.

Since Moodle HQ announced that Project Inspire, a next-gen analytics initiative, was in the making, the Moodleverse anticipation has been higher than the rate of official announcements. Which is why the following recap could be worthwhile.

At the beginning, details were scarce but a few things were clear:

  • Project Inspire would be led by Elizabeth Dalton, Learning Analyst for Moodle HQ. She is currently completing her Ph.D. in Curriculum and Instruction from the University of New Hampshire with a focus on Learning Analytics.
  • It would source data from Moodle users and installations around the world. The data would be provided voluntarily by registered Moodle site admins and would be “anonymized” to protect the users’ identities.
  • It would involve some degree of machine learning.

Project Inspire was officially announced by Dalton’s talk at MoodleMoot Australia in December. She highlighted the technical and pedagogical assumptions behind true learning analytics, and not those solutions “carried over” from digital marketing or business metrics.

A few months into 2017, four Courses/Discussion Forums were launched at the Moodle forums: Data Collection, Project Inspire Background, Roadmap, and Working Group. This is arguably the beating heart of Project Inspire’s progress. As with most early stage initiatives, reception and activity by users are the main predictors of timely development.

Several weeks before the launch of Moodle 3.3, documentation for the Project Inspire plugin started to detail the Inspire Plugin, a predictive tool that would offer levels of confidence for future student success according to their behavior, and even prescriptive commands in case remedial action were necessary.

Jumping into the present, the Inspire plugin, which is the most explicit outcome of the project to date, is available for limited forecasting functionality. The predictive accuracy of the plugin, depending on the rate of accurate learning of the algorithm, is bound to increase over time. The developers confirm that plugin’s predictions are evaluated empirically.

Whether the plugin draws from the anonymous Moodle data set of users worldwide is still unclear. Currently, it needs records of past Course behavior, otherwise the model may not be able to offer predictions.

As for the future, the Inspire plugin is expected to join the Moodle core. On June 1st, an unofficial statement by the CTO of Spanish Moodle Partner Inserver during Open Expo España stated that Analyse, either a new plugin or the next stage of the original Inspire plugin, will be ready for Moodle 3.4.

Install and download the Inspire plugin here. (Moodle 3.3 and PHP 7 required.) The plugin is maintained by David Monllaó, with Dalton’s help.

Read the latest documentation for the Inspire plugin here.

Check out new plugin features in development at the Inspire prototype Moodle site.

For information about getting involved with Project Inspire, read our guide. For technical details, you can read a preliminary overview of the Inspire API here.

Corrections made on June 15th. An earlier version of this article stated or suggested that:

  • Project Inspire started more than two years ago. Previous work were not directly related to it.
  • Elizabeth Dalton had already completed her PhD.
  • There were no information regarding the evaluation of the Inspire plugin’s outcomes.
  • The project might not be advancing under the planned schedule.

eThink LogoThis Moodle Governance related post is made possible byeThink Education, a Certified Moodle Partner that provides a fully-managed Moodle experience including implementation, integration, cloud-hosting, and management services. To learn more about eThink, click here.


Previous article6 Concesiones que Deberás Hacer al Elegir tu LMS ideal
Next articleWant to know what makes a good Moodle quiz? Check out this presentation by Dr. Tim Hunt #moodletips



Please enter your comment!
Please enter your name here

This site uses Akismet to reduce spam. Learn how your comment data is processed.