YouTube at the School of Fashion and Textiles: Teaching YouTube to Teach

Experience, Featured, Global, Industry and experience, Pathways, Research and innovation, Systems, TLConf2016 Leave a Comment

Here’s an audio recording of a discussion about this project at the RMIT Learning and Teaching Conference for 2016.

We’ve been working with teachers and students in the School of Fashion and Textiles, trying to get the most out of Youtube and other social media. This work stems from the Fashion Youtube project.

We’ve shown teachers and students how to:

  1. set up and manage YouTube channels;
  2. use a smart phone to record effective video;
  3. produce screen recordings;
  4. edit video with YouTube;
  5. add closed captioning;
  6. create and manage Youtube playlists
  7. generate QR codes that link to playlists
  8. understand the networking and machine learning capacity in YouTube.

We’ve collected our posts about this work here.

Example Youtube Channels

Here’s a playlist of video from some of the teachers we have been working with:

Laura Holmes Brown and Travis Hart manage their own Youtube channels for distributing a range of screen recordings they produced for their Computer Aided Design courses.

Sharon Koenig, Julie Wood and Betty Kanzurovski co manage the Fashion Stitched Up Youtube channel, to distribute a large collection of instructional videos to support their Garment Construction courses.

Andrew Robinson manages his own Youtube channel to distribute instructional videos and industry interviews for his courses in Custom Made Footwear. He has also lead the way in modelling how to use Youtube in combination with other websites and social media, to develop an online professional network and web presence for small businesses.

Developing a professional/learning network

The longer term goal with this work is to establish a professional network across the various disciplines of the School of Fashion and Textiles. This network connects teachers, students, industry and public. Here’s a series of screengrabs from a network map of what’s developed so far in Youtube:

This shows all the people we have worked with to set up Youtube channels, as well as the connections they’ve made in Youtube since. You can see two major nodes here, Andrew Robinson who teaches footwear, and Digital Learning DSC who support teachers and students with digital learning. Around Andrew you can see lots of two way connection between Andrew, students and industry. The nodes between Andrew’s network and DL DSC are the people who ‘bridge’ both. This accurately reflects the relations that DLDSC people have with Andrew and the School of Fashion and Textiles.

Here’s just the teachers and the students

fashion-network-teachers-and-students-october-2016

In this view we are looking only at those labelled “teacher” or “student”. You can see Digital Learning DSC’s network disappears, leaving the teachers and students who networked around DLDSC disconnected. Andrew’s network remains in tact of course, just thinner without the industry connections showing, and revealing very little connection between students. Here we can see more work needs to be done connecting across nodes in a network, decentralising from any one node.

Here’s just teachers and industry

fashion-network-teacher-and-industry-october-2016

 

In this view, we can see a stronger network of teachers connected through DLDSC again, where DLDSC is labelled as “industry”. A smaller but not insignificant network remains around Andrew, showing that Andrew is facilitating a connection with industry and other teachers in Youtube.

Here’s just the students and industry

fashion-network-students-and-industry-october-2016

And in this view, where teachers are no longer included, just students and industry, we can see that the students in Andrew’s network have almost no connection to industry or each other in Youtube. There are just 2 students in this map who share a connection with one industry channel.

This shows us connection and disconnection

The loss of connection in the network when key nodes are removed shows us what we need to start focusing on. We will start activities that cause teachers and students to subscribe to each other’s channels, and to explore more industry channels.

We’ve already shown the sort of impact these sorts of activities have on the associations and recommendations that Youtube then makes to a user, arguably assisting them in their informal and serendipitous learning during and after the course, as well as further online networking.

We will map this network as it exists in Facebook, Instagram, LinkedIn and possibly Pinterest – all of which are commonly used in the fashion industry.

Our hope is that by visualising it like this we will discover more about the network functionality, and inspire others to want to join in.

Teaching Youtube to Teach

This approach to using Youtube to try and establish a School wide learning network is lead by some of our thinking described in Can we teach the machine to teach, where we are keen to find out if there are things we can do – activities we can undertake, that all together improves the usefulness of Youtube to the user – both teacher and learner. We’re approaching this by paying attention to the sorts of data that impacts how videos are recommended in Youtube. For example, we’re observing how the titles, descriptions, tags and closed captions of a video or a playlist impact on the sorts of videos that Youtube recommends along side the video being played. We’re finding that these recommendations are made more relevant and useful if care is taken with the titles, descriptions, tags and closed captions – especially the closed captions. Longer term we’re interested to find out what happens when people subscribe and interact with each other’s channels. We expect such interaction will have a rapidly useful impact on the sorts of videos and channels that Youtube recommends. Critical to the success of this is that users create channels that separate away their professional use from their everyday personal use. This way, we can keep the usage data focused on particular interest areas, and train Youtube to make recommendations that take us deeper into that interest area and connect us with other users. This same practice works for Facebook, Instagram, Pinterest and other socially networked media.

 

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