Hi, my name is Yan Chen.
I am a recent PhD graduate from the University of Michigan.
Today, I'm presenting my internship project
at Google [INAUDIBLE] team, which studies the motivation
and barriers that streamers and viewers have
when they participate in livestreaming sessions.
So this is joint work with Professor Walter Lasecki, also
at Michigan, and my internship mentor, Dr. Tao Dong.
So let's first watch a short clip of what
livestream session looks like.
JAY: Do you get this issue and I'd
like a certain kind of outcome is like your question because--
So that was Jay, who has been livestreaming for a few years.
So on average, there are 150 people watching live
per episode, and people are very active in chat.
After posting the archived video,
there are a few hundred views on average.
So what exactly is livestream programming?
It's a practice where stream hosts, which often called
streamer, broadcast videos of themselves doing
software development work in real time
to an online audience, which are often called viewer.
And there are a lot of streamers just like Jay
who like to share their programming
knowledge with others using livestream platforms
such as Twitch or YouTube Live.
Livestream has become a new medium for learning programming
in that community, and so we want to better understand
the position of livestreaming and this whole programming
education ecosystem so we can help streamers and viewers
to make better use of them.
Specifically, we asked 26 participants, 14 of them
are streamers, 12 are viewers, to compare their experience
of livestream versus pre-recorded videos regarding
their motivation and barriers.
So I'm going to highlight some of our findings.
So we found that streamers use livestream
to share the process of software development and video
recording to answer specific questions.
As an example, the streamer said,
livestreams are good for teaching the whole process.
They are very organic.
And the viewers mentioned that they
were looking for high level guidance or advanced practice
by experts.
For example, V3 said, it's not really
about me trying to solve a problem.
It's just me trying to learn something or see someone else
coding.
And V8 said, I think it's really awesome
because I get to see production code which I don't really
see often.
And the production code that this viewer mentioned
is not only in private code base, but also
open source projects, which we have seen a lot,
like shown in this screenshot.
And we also find that streamers face many issues
with livestream.
For example, streamers have to be
cautious about their privacy, such as passwords
or other sensitive information.
And the nature of liveness could also
be something difficult to work with.
The situation is even worse for female streamers.
For example S10 said, it's all a gender aspect
so people just keep commenting irrelevant things
like physical appearance.
And sometimes the harassment could
go outside that livestream.
Like S9 said, I tend to get more creepy messages
to my email and my Twitter.
So we can see this liveness brought
into some unique challenges in this space.
We also observed that streamers and viewers constantly
exchange information, such as code, URL, and other materials
during the livestream.
Like the screenshot on the left, the streamer
is typing code on Slack, sending them over to other viewers.
And on the right hand side, it was the streamers
was helping viewers to debug by asking
if they see certain things.
So this kind of information sharing
is inefficient because of limited bandwidth
and easily missed context.
Like S5 said, people tend to comment on things
without providing context, so running the communication
is really delayed.
On the viewers' side, we have fewer patterns,
but still very interesting.
For example, viewer had a different learning preference,
such as pace or the layout.
Right now, you can only watch livestream as a regular video,
but because it's live, you can't really pause it or rewind it.
And if you miss something, it's really hard
to continue following along.
So this screenshot shows how hard it is to see the output.
With all these findings, we offer design recommendations
along these four lines that promote
a healthy, safe, and engaging learning environment.
We have so many other interesting findings
and analysis in our paper.
Please go and check it out.
Thank you.