KlickerUZH v3.1: Release Information

For information on the latest major release, v3.0, as well as any information related to our old version, KlickerUZH v2.0, please refer to KlickerUZH v3.0: Release Information.

Dear KlickerUZH community,

We are excited to announce our latest release, v3.1, of KlickerUZH.

This post will summarize the new key functionalities included in the release. More information as well as documentation on the new functionalities will be added to this post and our official documentation soon.

As always, we are excited to hear any feedback you might have, experiences and use cases, as well as suggestions and feature requests.

What’s New

The following overview shows the highlights of KlickerUZH v3.1:

The most important changes and additions when compared to KlickerUZH v3.0 are the following:

  • PowerPoint Add-in: The PowerPoint Add-in can now be used by anyone affiliated with UZH directly within Microsoft PowerPoint. For any users outside UZH, the Add-in could be deployed by your Microsoft administrator. For more information on usage outside UZH, see the following thread: KlickerUZH Evaluation PPT Add-in outside of UZH.

  • Flashcards: By providing flashcards to your students, you can enable them to practice content, like definitions, by heart using a much simpler modality. By grading their response based on the provided explanation, flashcards in practice quizzes can be practiced in a spaced-repetition manner. Import capabilities for, e.g., tools like Anki or Card2Brain, as well as collaborative creation of such elements, are still under consideration. If you have an interesting use case or would be interested in related features, feel free to respond to this post.

  • Content Elements: Using content elements, you can provide your students with theoretical inputs (e.g., summaries of a lecture) directly within practice quizzes and microlearnings, without having to include them in question content. Future extensions of content elements might include video embeds or other embedded content.

  • Improved Course Overview: The course overview has been restructured such that all linked KlickerUZH activities (live quiz, practice quiz, microlearning, and group activity) are shown in dedicated lists. This will provide the most relevant information at one glance and allow you to have direct access to all relevant actions and links related to those elements.

  • Practice Pool: Students can access all content elements, flashcards, and questions associated with a course (when used in a published practice quiz) within one central practice pool. This pool provides a chunk of 25 elements at a time, selected by a spaced-repetition algorithm, and allows students to practice in smaller chunks and quick sessions.

  • Achievements: Students automatically collect achievements when they rank first/second/third on a gamified live quiz. These achievements are shown on their personal profile and result in the distribution of additional points and experience.

  • Group Activities: Students can form groups and participate in group activities provided by lecturers. The use cases is described in more detail on our website and a community post. For more details on how to implement and grade group activities, see the corresponding documentation.


As part of our current projects (see KlickerUZH), we will be investigating further use cases in the area of teaching interaction.

  • Import and Export using QTI: KlickerUZH v2.0 supported the export and import of questions using a custom JSON-format. To make KlickerUZH interoperable with other tools like LMS (Moodle, OLAT), we will build a standardized import and export using the QTI standard. This functionality is expected to be released in Q2 2024.

  • Learning Analytics

    • For lecturers: What insights can we provide to lecturers about their courses and the learning progress of their students and how should this be illustrated? This could include, for example, the visualization of the strength of students in a course across its topic areas.
    • For students: How can we provide students with insights on their own learning progress and behavior? This could include, for example, the visualization of the strength of competencies for each student.
    • This includes integrating KlickerUZH with competency-frameworks by, e.g., labeling questions and activities with related skills, as well as being able to manage and use these competencies across courses (and users), and for the purpose of analytics.
  • Artificial Intelligence:

    • Content Generation: How can AI be used to develop/generate learning contents? For example, when applied within KlickerUZH, suggested contents could be generated by simply uploading a set of slides or a script.
    • Feedback on and grading of open-ended questions: How can AI be used to provide students with instant and well-formulated feedback on open-ended questions? When applied within KlickerUZH, this could enable the usage of open-ended questions within practice quizzes and microlearnings, while still providing automated grading feedback.
    • Course Chatbots: How can AI be used to build course chatbots based on own learning materials?

These additional use cases will serve as an extension to the ones on interaction and gamification that have been published already (see KlickerUZH v3.0: Release Information or KlickerUZH).

Survey on Learning Analytics and AI

In our latest survey, we would like to get your feedback on the current version of KlickerUZH and find out what would be valuable for you to further improve future versions of the platform, especially regarding our upcoming use cases.

In particular, this concerns the following two areas of future development:

Learning analytics: Learning analytics involve collecting, analyzing, and interpreting data on students’ learning behavior to enhance and optimize teaching processes, offering personalized learning experiences, implementing more effective teaching methods, and identifying early-stage difficulties. Learning analytics will be the focus of our next project phase.

AI in education: AI in education uses machine learning and algorithms to improve learning methods, streamline administrative tasks for teachers and improve the quality of teaching and the overall student experience. As with many other tools, AI could also have a high impact on the use of KlickerUZH, but we need your feedback to support you in the best way possible.

Click here to take part in the survey.

Changes to Support

Note that our support e-mail will be changing in April. As our department is being renamed, our future support e-mail for UZH and Catalyst users will be klicker@df.uzh.ch.

We provide support for all users on a best effort basis through this community platform. You can log in using the same credentials as used for KlickerUZH. For users at UZH and partner institutions (Catalyst program), we additionally provide support through our klicker@bf.uzh.ch address. If you are not part of UZH or the catalyst program and contact us via e-mail, we reserve the right to redirect you to the community. For more information on the Catalyst program, see KlickerUZH.

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