Book Image

Moodle 4 Administration - Fourth Edition

By : Alex Büchner
Book Image

Moodle 4 Administration - Fourth Edition

By: Alex Büchner

Overview of this book

This updated fourth edition of the classic Moodle Administration guide has been written from the ground up and covers all the new Moodle features in great breadth and depth. The topics have also been augmented with professional diagrams, illustrations, and checklists. The book starts by covering basic tasks such as how to set up and configure Moodle and perform day-to-day administration activities. You’ll then progress to more advanced topics that show you how to customize and extend Moodle, manage authentication and enrolments, and work with roles and capabilities. Next, you'll learn how to configure pedagogical and technical Moodle plugins and ensure your LMS complies with data protection regulations. Then, you will learn how to tighten Moodle’s security, improve its performance, and configure backup and restore procedures. Finally, you'll gain insights on how to compile custom reports, configure learning analytics, enable mobile learning, integrate Moodle via web services, and support different types of multi-tenancy. By the end of this book, you’ll be able to set up an efficient, fully fledged, and secure Moodle system.
Table of Contents (24 chapters)

Making predictions with Moodle Analytics

In this section, you will learn about Moodle Analytics, including how to create learning analytics models.

Custom and log-based reports are descriptive; they tell viewers what happened but not why, and don’t predict outcomes or advise users on how to improve. While reporting tells us about who, what, when, and where, learning analytics aims to explain why and how well.

Important note

Moodle Analytics predicts or detects unknown aspects of learning based on (historical) log data and current behavior.

There are two types of models that Moodle Analytics supports:

  • Machine learning-based models: Sophisticated models use mathematical tools to make predictions; for instance, students with a high likelihood of failing a course
  • Static models: Simple models are based on assumptions and do not require sophisticated analyses; for example, students who have not enrolled in a course

The following diagram illustrates the...