Just a couple of graphs to further inform discussion. These are based on a 2012 survey, prior to the launch of VisualEditor.
https://commons.wikimedia.org/wiki/File:Editor_culture.png https://commons.wikimedia.org/wiki/File:Edit_solution.png Pine On Mon, Aug 3, 2015 at 12:43 AM, Pine W <[email protected]> wrote: > 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 <[email protected]> > 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:* [email protected] [mailto: >> [email protected]] *On Behalf Of *Pine W >> *Sent:* Sunday, 2 August 2015 3:58 PM >> *To:* Wiki Research-l <[email protected]> >> *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" <[email protected]> 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 <[email protected]> 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. >> >> >> >> >> _______________________________________________ >> Wiki-research-l mailing list >> [email protected] >> https://lists.wikimedia.org/mailman/listinfo/wiki-research-l >> >> >> _______________________________________________ >> Wiki-research-l mailing list >> [email protected] >> https://lists.wikimedia.org/mailman/listinfo/wiki-research-l >> >> >
_______________________________________________ Wiki-research-l mailing list [email protected] https://lists.wikimedia.org/mailman/listinfo/wiki-research-l
