Growth/Positive reinforcement/ja

このページでは Growth 機能セットに含まれる「肯定的な心理的強化」の作業を述べます. 主な利点、設計、未解決の疑問、決定事項を載せます.

進捗状況で増えた更新のほとんどは一般向けのGrowthチームの更新ページに、このページには特定の大規模または詳細な更新をそれぞれ掲載します.

現状

 * 2021-03-01: 新しく作成したプロジェクトのページ
 * 2022-02-25: チームの協議を経てプロジェクトが発足
 * 2022-03-01: プロジェクトのページを拡充
 * 2022-05-11: community discussion

概要
The Growth team has been focused on building a “cohesive newcomer experience” that provides access newcomers need to the elements that help them join the Wikipedia community of practice. For instance, with newcomer tasks, we have given them access to opportunities to participate, and with the mentorship module, we have given them access to mentorship. Suggested edits has been able to get more newcomers to make their first edits. With that success, we want to take action to encourage newcomers to continue to make more edits. This draws our attention to an undeveloped element to which newcomers need access: evaluating performance. We’re calling this project “positive reinforcement”.

We want newcomers to understand there is progression and value to sustained contributions on Wikipedia, increasing retention for those users who took the first step in making an edit.

ここで私たちには大きな疑問があります. 私たちのホームページを訪れて新規参加者がその機能をいくつか試してみたとして、もっと編集を続けたり前向きな勢いをつけるにはどのように励ますことができるでしょうか？

背景
新規参加者向けのホームページ導入は2019年で、当時は基礎的な「影響モジュール」として当該の新規参加者が編集したページについて、その後の閲覧数を載せただけでした. Growth 機能のうち、新規参加者にとって自分自身がどんな影響を与えたか知ることができるのはこれが唯一のパーツでおり、当チームでは導入以来、まったく改善しないままにしてきました. With this as a starting point, we have gathered some important learnings about positive reinforcement:


 * We have heard good feedback from community members about the module, with experienced editors saying that it is interesting and valuable to them.
 * 他の利用者から感謝されると定着率が上がる傾向があり、例えば「感謝」ボタン（これやこれ）やドイツ語版ウィキペディアが行った実証実験で見られました. 当チームは実在する人々からこれらの心理的な強化（reinforcements）を受ける方がシステムの自動的な反応よりも効果が高いと考えています.
 * Community members have explained that it is a high priority for newcomers to move on to more valuable tasks after starting with easy ones, as opposed to getting stuck just doing easy tasks.
 * Other platforms, such as Google, Duolingo, and Github, all utilize numerous positive reinforcement mechanisms like badges and goals.
 * Communities are wary of incentivizing unhealthy editing. We have seen that when editing contests offer cash prizes, or just when useful roles such as "extended confirmed" rely on edit counts, it can incentivize people to make many problematic edits.

利用者の人物像
There are many parts of the newcomer journey in which we could attempt to increase retention. We could focus on newcomers who have stopped editing after just one or a few edits, or we could focus farther down the journey on newcomers who have stopped editing after weeks of activity. For this project, we have decided to focus on those newcomers who have completed their first editing session, and who we want to return for a second session. The diagram illustrates these with a yellow star.

We want to focus on newcomers at this stage, as that’s the next stage of the editor funnel in which we can help improve retention. It is also where we see a very significant attrition rate currently, so if we can help retain newcomers at this point, it should have a meaningful increase in editor growth overtime.

Research and design
Research was conducted on the various mechanisms that have been employed to encourage people to contribute content to both on and off-wiki products. The following are some of the key findings from the research: To see a summary of the current design ideas for Positive Reinforcement, see this Design Brief. Our designs will evolve further through community feedback and several rounds of user testing.
 * Motivations for Wikipedia editors are multifaceted, and shift over time and experience. New editors are often driven more by curiosity and social connection than ideology.
 * Internal projects focus on intrinsic incentives, appeal to altruistic motivations, and are not systematically applied.
 * Broadening the appeal beyond ideological motivations may improve diversity of retained editors on Wikipedia.
 * Positive messages from experienced users and mentors is proven effective in short-term retention.

アイデア類
We have three main ideas for positive reinforcement. We may pursue multiple ideas as we work on this project.

Impact

 * Impact: An overhaul of the Impact module based on incorporating stats, graphs, and other contribution information. The revised impact module would provide new editors more context about their impact, as well as encourages them to continue contributing. Areas of exploration include:
 * Suggested edits milestone, to nudge users to try suggested edits.
 * Statistics on how much the user has edited over time (similar to what is in X Tools).
 * “Thanks received” count, to highlight the ability to receive community recognition.
 * Recent editing activity - including days in a row newcomers have edited (“streaks”) to encourage continued engagement or remind people to restart their contributions.
 * View reading activity on articles newcomers have edited over time (similar to info on Wikipedia:Pageview_statistics).

Community discussion
編集経験に何か報償があるともしかして建設的な貢献を促すかもしれませんが、それはまた利用者から予想外もしくは好ましくない言動を誘うことも判明しています. そこで、新規参加者をやる気にさせつつ、個別のウィキの価値を保つにはどうすれば良いかコミュニティの皆さんと意見交換したいのです. It's important that people contribute in good faith, and not just to earn the positive reinforcement. Therefore, we want to discuss with communities how we could encourage newcomers while still preserving the values of the wikis. Some of our ideas on this page may not align well to community values -- if so, we want to hear how we could take those ideas in the right direction! Please help us on this project!


 * 新規参加者のやる気を誘うため、皆さんのウィキでの成功事例を教えてもらえませんか？
 * Which of the ideas above do you think have the most promise? The least promise?
 * 否定的な影響の予測とその防止策とは？

User testing
Along with community discussion, we want to validate and add to our initial designs and hypothesis. We will conduct Positive Reinforcement user research aimed to better understand the project's impact on newcomer contribution across several different languages. We will share these results here.

Measurement and results
Once community discussion is complete, designs are refined, development and testing are complete, our staff Data Scientist will closely monitor the impact of the Positive Reinforcement project. We will share our initial measure plan and subsequent results here.