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Moodle learning Analytics

The measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of discovering patterns, understanding and optimizing learning and the environments in which it occurs is known as Learning Analytics

Learner interaction in Moodle leaves a lot of digital traces behind.

This huge amount of aggregate data that is sourced from as many students as possible is known as Big Data.

Moodle’s database Scheme

The most common use of learning analytics is to identify students who appear less likely to succeed academically and to enable—or even initiate—targeted interventions to help them achieve better outcomes.

LA tools to identify specific units of study or assignments in a course that cause students difficulty generally. Instructors can then make curricular changes or modify learning activities to improve learning on the part of all students.

Much of the data on which LA applications depend comes from the learning management system (LMS), including:

  • log-in information
  • rates of participation in specific activities
  • time students spend interacting resources or others in the class,
  • grades

Reports can take various forms, but most feature data visualizations designed to facilitate quick understanding of which students are likely to succeed

Own Moodle learning analytics

Beginning in version 3.4, Moodle core now 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. ( We use moodle 3.6 and up )

The system can be easily extended with new custom models, based on reusable targets, indicators, and other components (Using APIS)

system ships with two built-in models:

Students at risk of dropping out : This model predicts students who are at risk of non-completion (dropping out) of a Moodle course, based on low student engagement. In this model, the definition of “dropping out” is “no student activity in the last quarter of the course.” This prediction model uses the Community of Inquirymodel of student engagement


  1. By abstracting the concepts of “cognitive presence” and “social presence,” this prediction model is able to analyze and draw conclusions from a wide variety of courses, and apply those conclusions to make predictions about new courses, even courses never taught on the Moodle system before. The model is not limited to making predictions about student success only in exact duplicates of courses offered in the past.


  1. This prediction model assumes 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 Moodle.
    1. Because of this model design assumption, it is very important to properly set course start and end dates for each course to use this model. If both past courses and ongoing courses start and end dates are not properly set predictions cannot be accurate.
    2. Courses will not be included in training or predictions if the end date is before the start date.
  2. This model requires the use of sections within the courses, in order to split all activities into time ranges.
  3. Courses with start and end dates further than one year apart will not be used.
  4. This model requires a certain amount of in-Moodle data with which to make predictions. At the present time, only core Moodle activities are included in the indicator set (see below). Courses which do not include several core Moodle activities per “time slice” will have poor predictive support in this model. This prediction model will be most effective with fully online or “hybrid” or “blended” courses with substantial online components.

No teaching activity : This target describes whether courses due to start in the coming week will have teaching activity.

Important Built in indicators

Assignment cognitive This indicator is based on the cognitive depth reached by the student in an Assignment activity.
Course accessed after end date This indicator reflects if the student accessed the course after the course end date.
Course accessed before start date This indicator reflects if the student accessed the course before the course start date.
Read actions amount This indicator represents the number of read (view) actions taken by the student.
Quiz cognitive This indicator is based on the cognitive depth reached by the student in a Quiz activity.
Completion tracking enabled This indicator represents that completion tracking has been enabled for this course.
Course potential cognitive depth This indicator is based on the potential cognitive depth that could be reached by a student participating in course activities.
Course potential social breadth This indicator is based on the potential social breadth that could be reached by the student participating in course activities.

We can deal with this analytics either using built in ones or using Api to create another through database table called analytics

Moodle can integrate with many plugins that offers student tracking like

Intelliboard : https://intelliboard.net/

Zoola : https://www.zoola.io/

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