A part of the Experiential Learning team, Academic Video Production creates high-quality, studio-based and field recording for university curriculum and other instructional content
Want to explore creative audiovisual work with us? Begin a Creative Project Request.
Experiential Learning Commons - Moody Library (Garden Level)
Learn more at: libguides.baylor.edu/elc-studios
Can't make it to our Baylor studios, or want to include remote colleagues in videos for your course? Kaltura Capture or Powerpoint Cameo recording may suit for talent in a single room, but what about talent in multiple locations? Zoom and Teams can record, but the result is challenging to edit, and quality can be sub-par. Let's discuss other options to get you a more professional result.
DIY tips and more ways we support remote recording at libguides,baylor.edu/remoterecording
If you have a spot in-mind, we'll complete a site check and can record on-site if the location is suitable. Indoor and outdoor locations are both possible. Distance, duration, complexity, and desired assets all affect the decision to opt for field recording over studio work. By appointment only. Submit a Creative Project Request above.
Full description of Kaltura, including features and use of the Canvas integration and Mediaspace are available at libguides.baylor.edu/kaltura101.
Kaltura integrates with Canvas and allows you to use video content in your courses. By default, the integration should be enabled on every Canvas course. You can verify it is enabled by looking for the My Media and Media Gallery menu items within the course. If you do not see these, you will need to enable them.
Kaltura Mediaspace is a public-facing platform for hosting videos in the Kaltura system. Many videos shared on Mediaspace can be accessed without a Baylor login, which makes it perfect for content outside the university. There are also videos which require a Baylor login but aren't tied to a single course.
Every video uploaded to Kaltura automatically receives machine captioning. However, machine captioning only achieves between 70%-80% accuracy. If your video contains a lot of technical language or proper nouns, you may want to edit the captions.
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