New Release: Two New Content types and Major Accessibility Improvements

Table of Contents 

I'm eager to share with you the launch of two new content types, Flashcards and Image Sequencing! In addition, there have been major improvements made to accessibility and score feedback; both of which represent huge steps forward in terms of usability for H5P.


Longtime H5P users will remember the first version of flashcards and now it's back with a bang. It's not technically a 'new' content type but it has been improved significantly. The next step is to make it fully accessible.


Image Sequencing

A debut content type from Jithin, Image Sequencing is a neat learning task that challenges learners to order images in a certain way. Have a go at this example:


Accessibility and user experience are extremely important to us, and the H5P Core Team has been focusing on improving H5P in both areas in several releases. It has however been few improvements per release. Organizations in the community have been very willing to fund new features, and finally, earlier this year, Michigan Virtual reached out to us, interested in helping us make major accessibility upgrades happen by both funding the work and through knowledge sharing. Soon after University of Nevada, Las Vegas (UNLV) also reached out to us, wanting to help improve the accessibility of H5P, and Benjamin Root and Philip Voorhees of UNLV have contributed their expertise in accessibility by testing the improvements we have made and contributed feedback.

We're now releasing the first results of the work. We've made substantial accessibility improvements to the content types listed below:

  • Drag Text
  • Drag and Drop
  • Summary 
  • Dialog Cards
  • Image Hotspots
  • Interactive Video

For each content type, we link to any known accessibility issues and we urge you to let us know if you find unknown issues in the content types:

Drag and Drop:



Customizable Feedback 

Two types of customizable feedback have been added. The first allows content creators to add specific feedback for correct and incorrect answers. This custom feedback appears in a table below the question, colour coded for better legibility. Try answering some wrong and right to see how the new custom feedback looks:


The second type of customizable feedback allows content creators to set feedback depending on the score achieved by the learner at a granular level:

This feature has been added to the following content types:
  • Fill in the blanks
  • Drag and Drop
  • Drag Text
  • Mark the Words
  • Multiple Choice
  • Question Set
  • Single Choice Set
  • Summary

Clearer Score Calculation  

Previously it has been difficult for users to see how the score was calculated. This was particularly confusing when some content types used negative scoring. Animated labels have now been added to make it very clear. Check it out:

Translation Contributions

This has also been a big release in terms of languages. Two community members, in particular, have made huge contributions, Gerardo Fallani and Takahiro Kagoya who have translated the majority of the content types into Italian and Japanese respectively. 

The translation community has been testing out with recently and the feedback I have received is that it is a much better interface for translations. This is great to hear and although the CrowdIn integration is suspended for the moment while some bugs are attended to, I am hoping to get it up and running again by the end of this year. 

Since H5P Con I've seen some great Danish, Finnish and Dutch translations come in. I can't wait to see them out in the open soon. Well done everyone and keep up the good work!

Beta Testers 

This release would not have been possibel without the help of our beta testers. Thank you all for taking the time to make sure all these improvements are as good as they could be!

  • Dustin Hosseini
  • David Bevington
  • Nicola Avery
  • Robert Eckardt
  • Melissa Santoso
  • Mervyn Lim
  • Peter Bright
  • Katrien Bernaerts
  • Maude 
  • Diane Berthoin-Hernandez
  • Bill Fisher



 A special thank you to Michigan Virtual and University of Nevada, Las Vegas (UNLV) for their contributions towards these massive accessibility improvements. Joubel, Michigan Virtual, and UNLV are continuing our work in the coming releases. 

What next?