I watched the video, in which Aaron did discuss social and motivational
barriers as being more complex and difficult to solve than technical issues
with VisualEditor.
I liked the questions that Aaron asked ("Did you make friends? Did you find
the work rewarding? Did you identify with the community?") because my
understanding is that in the wide world of volunteer associations,
questions like those are strongly related to volunteer retention and
activity levels.
I have a hunch that in-person workshops and editathons can do a lot to
improve the onboarding and retention experience for new editors. My
understanding from WMF Learning and Evaluation is that editathon series and
writing contest series are particularly effective at retaining editors. I
would guess that this effect happens because people in general may find it
easier to make friends and identify with a community when they have
face-to-face, positive interactions with other members of that community.
However, also note that most students who write Wikimedia content for their
classroom assignments don't remain active contributors after the completion
with their assignments, so I speculate that the third issue ("did you find
the work rewarding?") may be significantly affected by the intrinsic
motives and interests of potential contributors, as well as competition for
the time of those potential contributors from other activities (like good
grades, fulfilling jobs, or happy activities with family and friends) that
also provide rewards.
There is ongoing work to improve the effectiveness of mentorships and
wikiprojects on English Wikipedia, which may also help to address the "did
you make friends" and "did you identity with the community" questions.
I'm thinking about how I can implicitly take these issues into account when
designing the content of the video project that I linked earlier in this
thread, and how the video content could help with lowering
social-motivational barriers. Suggestions from other participants on
Research-l would be most welcome.
Thanks,
Pine
Pine
On Sun, Aug 2, 2015 at 1:20 AM, Kerry Raymond <kerry.raymond(a)gmail.com>
wrote:
I haven’t yet had the opportunity to watch the YouTube
version of the
talk, but just taking the question at face value.
I don’t think the data is likely to be able to distinguish people doing
their first edits at a training class or edit-a-thon because in general
there is nothing to distinguish these folk from any other new contributors. It
might be that some events use some system of categories for either the
users or the articles (editathons often tag the articles with the event
name) so you might be able to spot edits arising from a specific event but
in general I don’t think you can tell them apart.
I teach a lot of edit training and, although I have yet to switch to the
VE, I am looking forward to being able to do so as soon as possible. Markup
is definitely a barrier to some people and I think the VE will be preferred
by most users. However, while VE may make editing easier, it does not solve
the problem of having newcomers’ good faith contributions being reverted by
others. WP:NOBITE is the most ignored policy of Wikipedia.
Kerry
*From:* wiki-research-l-bounces(a)lists.wikimedia.org [mailto:
wiki-research-l-bounces(a)lists.wikimedia.org] *On Behalf Of *Pine W
*Sent:* Sunday, 2 August 2015 3:58 PM
*To:* Wiki Research-l <wiki-research-l(a)lists.wikimedia.org>
*Subject:* Re: [Wiki-research-l] July 2015 Research showcase
I read the summary of the VE study, and I have a question. Anecdotally, I
am hearing from multiple sources that new editors *who attend workshops or
editathons in person* prefer VE over wikitext for ease of use. Do we have
any data specifically about the productivity and longevity of this
population of users when they are introduced to to Wikipedia editing on VE
instead of wikitext?
Thanks!
Pine
On Jul 29, 2015 11:09 AM, "Leila Zia" <leila(a)wikimedia.org> wrote:
A friendly reminder that this is happening in 23 min. :-)
YouTube stream:
https://www.youtube.com/watch?v=vGyrVg_qKSM
IRC: #wikimedia-research
Best,
Leila
On Mon, Jul 27, 2015 at 2:47 PM, Leila Zia <leila(a)wikimedia.org> wrote:
Hi everyone,
The next Research showcase will be live-streamed this Wednesday, July 29
at 11.30 PT. The streaming link will be posted on the lists a few minutes
before the showcase starts (sorry, we haven't been able to solve this, yet.
:-() and as usual, you can join the conversation on IRC at
#wikimedia-research.
We look forward to seeing you!
Leila
This month:
*VisualEditor's effect on newly registered users*
By *Aaron Halfaker* <https://www.mediawiki.org/wiki/User:Halfak_%28WMF%29>
It's been nearly two years since we ran an initial study
<https://meta.wikimedia.org/wiki/Research:VisualEditor%27s_effect_on_newly_registered_editors/June_2013_study>
of VisualEditor's effect on newly registered editors. While most of the
results of this study were positive (e.g. workload on Wikipedians did not
increase), we still saw a significant decrease in the newcomer
productivity. In the meantime, the Editing
<https://www.mediawiki.org/wiki/Editing> team has made substantial
improvements to performance and functionality. In this presentation, I'll
report on the results of a new experiment designed to test the effects of
enabling this improved VisualEditor software for newly registered users by
default. I'll show what we learned from the experiment and discuss some
results have opened larger questions about what, exactly, is difficult
about being a newcomer to English Wikipedia.
*Wikipedia knowledge graph with DeepDive*
By *Juhana Kangaspunta* and
*Thomas Palomares (10-week student project)*
Despite the tremendous amount of information present on Wikipedia, only a
very little amount is structured. Most of the information is embedded in
text and extracting it is a non-trivial challenge. In this project, we try
to populate Wikidata, a structured component of Wikipedia, using DeepDive
tool to extract relations embedded in the text. We finally extracted more
than 140,000 relations with more than 90% average precision. We will
present DeepDive and the data that we use for this project, we explain the
relations we focused on so far and explain the implementation and pipeline,
including our model, features and extractors. Finally, we detail our
results with a thorough precision and recall analysis.
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