Growth/Positive reinforcement/ja

このページでは Growth 機能セットに含まれる「肯定的な動機づけ」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.

進行中の増分更新のほとんどは全般的なGrowthチームの更新ページに投稿されます. このページにはいくつかの大規模または詳細な更新を掲載します.



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: コミュニティの議論
 * 2022-08-12: 利用者テスト実施済み
 * 2022-11-24: current designs and measurement and experiment plan added
 * 2022-12-01: new impact module released to pilot wikis
 * Next: 設計の反復作業と技術は影響度のレベルアップと個人対象の顕彰に着手

概要
Growth チームでは、新規参加者にはウィキメディアの行動するコミュニティに参加する手助けとなる要素があると考え、それを使う「きっかけ」（access）となる「一連の新規参加者の経験」の設計に注目してきました. 一例として、新規参加者タスクは参加のチャンスが手に入り、指導役のモジュール mentorship module では、指導役との出会いの機会が実現します. また「お勧め編集」は初めての編集作業を完了する新規参加者の数を増やしてきました. これらの成功によって、新規参加者にはもっと編集作業を続けてほしいと願って、力づけるような方策を実施したいと考えます. ここから新規参加者が使いたいのに、まだ十分に開発できていない要素に注目しました. つまり、パフォーマンスの評価です. 当チームではこのプロジェクトを「肯定的な肯定的な動機づけ」“positive reinforcement” と名づけました.

新規参加者にウィキペディアで投稿を続けることに進歩と価値があることを理解してもらい、編集作業の第一歩を踏み出した利用者の定着率を向上させたいと考えています.

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
新規参加者ホームページは2019年に導入されました. これには新規参加者が編集したページの閲覧数を一覧にする、基礎的な「影響モジュール」が含まれていました. これはGrowth 機能のうち、自分自身がどんな影響を与えたか新規参加者に感じさせる唯一のパーツであり、これを導入して以来、改善していませんでした. ここをスタート地点と考え、肯定的な動機づけに関する重要な学びを以下のように集めてみました.


 * コミュニティの皆さんからモジュールに寄せられた感想は良好で、熟練の編集者からも興味を引かれるし価値を認めるとのご意見でした.
 * 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.
 * コミュニティの皆さんのご意見では、新規参加者が簡単なタスクから始めた後、もっと価値のあるタスクに移行することの優先順位は高く、いつまでも容易なタスクを続けない方が良いとのことです.
 * 他のプラットフォーム、例えば Google（グーグル）、Duolingo（デュオリンゴ）、Github（ギットハブ）などでは、バッジや中間ゴールなど、肯定的な動機づけのためにいくつもの仕組みを採用しています.
 * 不健全な編集に報償を与えるようではいけないとコミュニティでは心配しています. 編集コンテストで賞金がもらえるとき、あるいは「拡張承認された利用者」のような有用な役割が編集回数に依拠しているとき、多くの問題のある編集を行う動機となりうることをこれまで見てきました.



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.



調査と設計
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:


 * 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.

現状で集まった肯定的な動機づけに関する設計案の概要は、この設計のまとめ Design Brief をご参照ください. Our designs will evolve further through community feedback and several rounds of user testing.

アイデア類
肯定的な動機づけには主に3つの発想があります. このプロジェクトの進行につれて、複数案を念頭に置くかもしれません.

影響

 * 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 en:Wikipedia:Pageview_statistics).



レベルを上げる

 * Leveling up: It is important to communities that newcomers progress to more valuable tasks. For those who do many easy tasks, we want to nudge them toward trying more difficult tasks. This could happen after they complete a certain number of easy tasks, or by encouragement on their homepage. Areas of exploration include:
 * The newcomer will see success messages post-editing that motivate them to do more edits of the same or different levels of difficulty.
 * In the Suggested Edits module, provide opportunities to do more difficult edits, so that newcomers can become more skilled editors.
 * In the Impact module, include a milestone counter or award area.
 * On the Homepage, add a new module with set challenges to attain some reward (badge/certificate).
 * Add notifications to prompt newcomers to try a more difficult task.



個人からの賞賛

 * Personalized praise: research shows that praise and encouragement from other users increases newcomer retention. We want to think about how to encourage experienced users to thank and award newcomers for good contributions. Perhaps mentors could be encouraged to do this on their mentor dashboards or through notifications. We can utilize existing communication mechanisms which past studies have proven to have a degree of positive effect. Areas of exploration include:
 * A personal message from the newcomer’s mentor appearing in the homepage.
 * An echo notification from the mentor or the Wikimedia Growth team.
 * “Thanks” on a specific edit.
 * A new milestone badge awarded by the mentor or the Wikimedia Growth team relating to a specific edit.



コミュニティの議論
ar:ويكيبيديا:مشروع فريق النمو (التعزيز الإيجابي)bn:উইকিপিডিয়া:আলোচনাসভাcs:Diskuse k Wikipedii:Zkušenosti nových wikipedistů/Pozitivní posílenífr:Discussion Projet:Aide et accueil/Volontaires コミュニティの皆さんと肯定的な動機づけプロジェクトをめぐり、このページならびにmediawiki.orgで話し合ってきました.

We received direct feedback about the three main ideas, along with many other ideas for improving new editor retention.

Below is a summary of the main themes from the feedback, along with how we plan to iterate based on the feedback.

影響


個人からの賞賛


その他の発想：
参加者の新規登録や定着について、コミュニティの参加者からその他の発想がいくつか寄せられました. どれも価値のある発想であり（すでに当チームで進行中もしくは将来の採用検討中の案を含む）、それでも以下の発案はプロジェクトの現状の視点には適合しないようです.
 * 新規参加者に勧誘と歓迎メッセージをメールで送信（現状で当 Growth チームはマーケティング部門と募金活動部門と連携して参加のお願いメールの可能性を探索中です. ）
 * 新規参加者の興味に合わせて、ウィキプロジェクトを紹介.
 * 新規参加者向けホームページには、カスタマイズ可能なウィジェットが設定してあり、それぞれのウィキはこれを利用すると、新規参加者対象のタスクやイベントの呼びかけができます.
 * Send notifications to users who welcome newcomers once the newcomer reaches certain editing milestones (to help prompt the user to offer Thanks or Wikilove).



ユーザー テスト
Along with community discussion, we wanted to validate and add to our initial designs and hypothesis by testing designs with readers and editors from several countries. 設計調査担当では当肯定的な動機づけプロジェクトが新人編集者の投稿にどう影響するか把握するため、複数言語で利用者テストを実施しました.

肯定的な動機づけの設計案を統計的にテストし、対象はウィキペディアの読者と編集者、言語はアラビア語版、スペイン語版、英語版としました. Along with testing Positive Reinforcement designs we introduced data visualizations from xtools as a way to better understand how these data visualizations are perceived by newcomers.



User testing results

 * Make impact data actionable: Impact data was a compelling feature for participants with more experience editing, which several related to their interest in data—an unsurprising quality for a Wikipedian. For those new to editing, impact data, beyond views and basic editing activity, may be more compelling if linked to goal-setting and optimizing impact.
 * Evaluate the ideal editing interval: Across features, daily intervals seemed likely to be overly ambitious for new and casual editors. Participants also reflected on ignoring similar mechanisms on other platforms when they were unrealistic. Consider consulting usage analytics to identify “natural” intervals for new and casual editors to make goals more attainable.
 * Ensure credibility of assessments: Novice editor participants were interested in the assurance of their skills and progress the quality score, article assessment, and badges offer. Some hoped that badges could lend credibility to their work reviewed by more experienced editors. With that potential, it could be valuable to evaluate that the assessments are meaningful measures of skill and further explore how best to leverage them to garner community trust of newcomers.
 * Reward quality and collaboration over quantity: Both editor and reader participants from esWiki were more interested in recognition of their knowledge or expertise (quality) than the number of edits they have made (quantity). Similarly, some Arabic and English editors are motivated by their professional interests and skill development to edit. Orienting goals and rewards to other indicators of skilled edits, such as adding references or topical contributions, and collaboration or community involvement may also help mitigate concerns about competition overtaking collaboration.
 * Prioritize human recognition: While scores and badges via Growth tasks is potentially valued, recognition from other editors appears to be more motivational. Features which promote giving, receiving, and revisiting thanks seemed most compelling, and editors may benefit from selecting impact data which demonstrates engagement with readers or editors most compelling to them.
 * Experiment with playfulness of designs: While some positive reinforcement features can be seen as the product of “gamification”, some participants (primarily from EsWiki) felt that simple, fun designs were overly childish or playful for the seriousness of Wikipedia. Consider experimenting with visual designs that vary in levels of playfulness to evaluate broader reactions to “fun” on Wikipedia.

Design
Below are the current designs for Positive Reinforcement. We have refined the three main ideas outlined above, but the scope of plans and the actual designs have evolved based on feedback from community discussions and user testing.

Impact
The revised impact module provides new editors with more context about their impact. The new design includes far more personalized info and data visualizations than the previous design. This new design is fairly similar to the design we shared previously when discussing this feature with communities. You can view the current engineering progress at beta wiki, and we hope to release this feature to Growth pilot wikis soon.

Leveling up
The Leveling up features focus on encouraging newcomers to progress to more valuable tasks. Ideas also include some prompts for new editors to try suggested edits, since structured tasks have been shown to improve newcomer activation and retention.
 * “Level up” post-edit dialog message: A new post-edit dialog message type is added to encourage newcomers to try a new task type. We hope this will encourage some users to learn new editing skills as they progress to different, more challenging tasks.
 * Post-edit dialog for non-suggested edits: Introduce newcomers who complete ‘normal’ edits to suggested edits. We plan to experiment by showing newcomers a prompt post 3rd and 7th edit. Desktop users who click through to try a suggested edit will also see their Impact module, which we hope helps engage newcomers and provides a small degree of automated positive reinforcement. We will carefully measure this experiment, and ensure there aren't any unintentional negative effects.
 * New notifications: New echo notifications to encourage newcomers to start or continue suggested edits. This acts as a proxy to “win-back” emails for those who have an email address and settings on to receive email notifications.

Personalized praise
Personalized praise features are based on research results that show that encouragement and thanks from other users increases editor retention.
 * Encouragement from Mentors: We will add a new module to the Mentor dashboard, that is designed to encourage Mentors to send personalized messages to newcomers who meet certain criteria. We will allow Mentors to customize and control how and when "praise-worthy" mentees are surfaced.
 * Increasing Thanks across the wiki: We plan to fulfill the community wishlist item to Enable Thanks Button by default in Watchlists and Recent Changes. We hope this will increase Thanks and positivity across the wikis, and hopefully newcomers will benefit from this directly or indirectly.



Hypotheses
The Positive Reinforcement features aim to provide or improve the tools available to newcomers and mentors in three specific areas that will be described in more detail below. Our hypothesis is that once a newcomer has made a contribution (say by making a structured task edit), these features will help create a positive feedback cycle that increases newcomer motivation.

Below are the specific hypotheses that we seek to validate across the newcomer population. We will also have hypotheses for each of the three sets of features that the team plans to develop. These hypotheses drive the specifics for what data we will collect and how we will analyse that data.


 * 1) The Positive Reinforcement features increase our core metrics of retention and productivity.
 * 2) Since the Positive Reinforcement features do not feature a call to action that asks newcomers to make edits, we will see no difference in our activation core metric.
 * 3) Newcomers who get the Positive Reinforcement features are able to determine that making un-reverted edits is desirable, and we will see a decrease in the proportion of reverted edits.
 * 4) The positive feedback cycle created by the Positive Reinforcement features will lead to a significantly higher proportion of "highly active" newcomers.
 * 5) The Positive Reinforcement features increase the number of Daily Active Users of Suggested edits.
 * 6) The average number of edit sessions during the newcomer period (first 15 days) increases.
 * 7) "Personalized praise" will increase mentor’s proactive communication with their mentees, which will lead to increase in retention and productivity.

Experiment plan
Similarly as we have done for previous Growth team projects, we want to test our hypotheses through controlled experiments (also called "A/B tests"). This will allow us to establish a causal relationship (e.g. "The Leveling Up features cause an increase in retention of xx%"), and it will allow us to detect smaller effects than if we were to give it to everyone and analyze the effects pre/post deployment.

In this controlled experiment, a randomly selected half of users will get access to Positive Reinforcement features (the "treatment" group), and the other randomly selected half will instead get the current (September 2022) Growth feature experience (the "control" group). In previous experiments, the control group has not gotten access to the Growth features. The team has decided to move away from that (T320876), which means that the current set of features is the new baseline for a control group.

The Personalized Praise feature is focused on mentors. There is a limited number of mentors on every wiki, whereas when it comes to newcomers the number increases steadily every day as new users register on the wikis. While we could run experiments with the mentors, we are likely to run into two key challenges. First, the limited number of mentors could mean that the experiments would need to run for a long time. Second, and more importantly, mentors are well integrated into the community and communicate with each other, meaning they are likely to figure out if some have access to features that others do not. We will therefore give the Personalized Praise features to all mentors and examine activity and effects on newcomers pre/post deployment in order to understand the feature’s effectiveness.

In summary, this means we are looking to run two consecutive experiments with the Impact and Leveling up features, followed by a deployment of the Personalized Praise features to all mentors. These experiments will first run on the pilot wikis. We can extend this to additional wikis if we find a need to do that, but it would only happen after we have analyzed the leading indicators and found no concerns.

Each experiment will run for approximately one month, and for each experiment we will have an accompanying set of leading indicators that we will analyze two weeks after deployment. The list below shows what the planned experiments will be:


 * 1) Impact: treatment group gets the updated Impact module.
 * 2) Leveling up: treatment group gets both the updated Impact module and the Leveling up features.
 * 3) Personalized praise: all mentors get the Personalized praise features.