Open edX News

EdX Launches the "Birch" Release – A Sneak Peak of Its Features

birchedx

The Open edX “Birch” release –the second version after “Aspen”– is almost here. It is scheduled to be released in February.

For now, this version is a release candidate.

“Birch” will include many new features, capabilities and APIs, as well as many small changes and bug fixes. edX’s Release Notes provide a cumulative list of changes listed after the release of Aspen, which was based on the version from September 4, 2014.

Here is a summary:

  • Prerequisite courses. You can require that students pass specific edX courses before enrolling into your course.
  • Entrance Exams. You can require that students pass an entrance exam before they access your course materials.
  • Student Notes. Learners can highlight text and take notes while progressing through a course. They can then review their notes either in the body of the course or on a separate “Notes” tab.
  • Course Reruns. You can create a new course easily by re-running an existing course. When you re-run a course, most –but not all– of the original course content will be duplicated onto the new course.
  • Google Calendar and Google Drive Components. You can embed Google calendars and Google Drive files into your course. Learners may see the calendar or file directly in the courseware. Learners can also interact with Google Forms files, and complete forms or surveys in the courseware.
  • Support for “Graded Problems” in “Content Experiments”. You can now use graded problems in content experiments.
  • Split Mongo Modulestore. This refers to the separation of identity, structure and content, and it enables you to use more advanced capabilities while developing and managing courses.
  • Cohorts for Discussions and Content. You can now define smaller communities of students within the larger, course-wide community. Learners in a given cohort may have private discussions.
  • Content libraries and randomized content. You can create a content library that contains a pool of components that can be used in randomized assignments.
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