Growth/Personalized first day/Newcomer tasks/ja

このページで解説する「新人編集者向けタスク」プロジェクトの作業は、Growthチームが取り組む「それぞれの人の初日」という大枠のイニシアチブ配下の固有のプロジェクトです. このページでは主要なアセット、設計、意思決定について述べます. 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、そして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.

チームの開発対象は、それぞれの試作品を見ると概要がつかめます (矢印キーで移動)：


 * デスクトップ
 * モバイル

2019年7月24日、このプロジェクトに基づく開発開始.

現状

 * 2019-07-24: 初回打ち合わせで新人編集者のタスクを協議
 * 2019-08-27: 設計コンセプトをめぐるチームの会議
 * 2019-09-09: 開発作業の管理を Phabricator で開始
 * 2019-09-23: デスクトップ版ユーザーテスト完了
 * 2019-09-30: モバイル版ユーザーテスト完了
 * 次へ: 開発を続けて11月にバージョン1の展開を目指す

要約
ウィキを初めて訪れた時、新人編集者にはあらゆる機会を得て成功体験をすることが重要だと考えています. ところが、まだ習熟していない段階では難易度が高すぎるタスクに取り組もうとする場合、あるいはやってみたくなるタスクが見つからない場合、もしくは最初の編集はしたものの、引き続き編集をするきっかけがわからない場合がしばしばあるようです. そのため二度とウィキを再訪しなくなってしまいます. 過去に試みたところ編集者にタスクをお勧めするとうまくいったため、新人編集者向けホームページを使い、新人編集者向きのタスクをお勧めすることが適切だと考えました.

'''そこで留意点がいくつかあります. '''
 * 多くの新人編集者はそれぞれ、何かやってみたいことがあり、例えば特定の記事に写真を追加したいと考えてウィキを訪問するようです. その目標達成の邪魔にならないようにします.
 * 新人編集者が編集の腕を上げるには、難易度が低い課題から高い課題へと段階を追っていきます.
 * 最初の段階で成功体験を得た新人編集者は、その後も編集を続ける意欲が高まります.

これらを念頭に置くと、新人編集者それぞれの場所と時間のタイミングに合わせ、また興味に沿うよう、正しい編集ができる技術を習得するタスクをお勧めしていきます.

歓迎アンケートというツールを使うと、新人編集者それぞれに適するタスク選びができます. このツールはそもそも新人編集者一人ひとりがその人らしい経験をするように作られました. アンケートでは、各人がウィキで目標にしていることや興味の対象の回答を得て、今後、それをオプション情報としてタスクのお勧めに反映できるようにする予定です.

解決すべき最大の課題のひとつは、新人編集者に適したタスクの選び方です. 既存のソースは十分にあり、たとえば記事で使うテンプレートやコンテンツ翻訳ツール内のお勧め、あるいは出典ハントなどのツールのヒントなどが使えそうです. 新人編集者それぞれが目標達成するには、どの選択肢が役に立つかが課題です.

まず最初は新人編集者向けのホームページを使ったタスクのお勧めに注力するもの、長期的には新規機能を開発して編集体験のお勧めへと拡張し、新人編集者が課題を達成するように補助する方法も考えられます.

また同じく長期的には、タスクのお勧め機能をその他の新人編集者の経験、例えばホームページの影響力モジュールあるいはヘルプパネルと連携させることも検討課題になるかもしれません.

Why this idea is prioritized
We know from research and experience that many newcomers fail early in their editing journey for one of these reasons:
 * They arrive with a very challenging edit in mind, such as writing a new article or adding an image. Those tasks are difficult enough that they likely fail and don't return.
 * They arrive without knowing what to edit, and can't find any edits to make.

We also know that on the newcomer homepage, the most frequently clicked-on module is the "user page" module -- the only thing on the page that encourages users to start editing. This makes us think that many users are looking for a clear way to get started with editing.

And from past Wikimedia endeavors, we've seen that task recommendations can be valuable. SuggestBot is a project that sends personalized recommendations to experienced users, and is a well-received service. The Content Translation tool also serves personalized recommendations based on past translations, and has been shown to increase the volume of editing.

For all these reasons, we think that recommending specific editing tasks for newcomers will give them a clear way to get started. For those newcomers that have an edit in mind that we want to do, we'll encourage them to try some easy edits first to build up their skills. For those newcomers who do not have a specific preference on what to edit, they'll hopefully find some good edits from this feature.

Glossary
''There are many terms that sound similar and can be confusing. This section defines each of them.''


 * "Task recommendations" or "Task suggestions"
 * Lists of articles that need editing work, suggested automatically to users.


 * "Personalized"
 * Software that adapts automatically to each user to fit their needs.


 * "Customized"
 * Software that the user adapts to fit their needs.


 * "Topic"
 * A content subject, such as "Art", "Music", or "Economics".


 * "Maintenance template"
 * Templates that are put on articles indicating that work needs to be done on them.

Recommending tasks
The core challenge to this project is: Where will the tasks come from and how will we give the right ones to the right newcomers?

The graphic below shows our priorities when recommending tasks to newcomers.

As shown in the graphic above, we would give newcomers tasks that...


 * ...arrive at the right time and place for a newcomer's journey.
 * ...teach relevant conceptual and technical skills.
 * ...gradually guide users to build up their editing abilities.
 * ...be personalized to their interests.
 * ...show them the value and impact of editing.
 * ...motivate them to participate continually.

For instance, we do not want to give newcomers tasks that are irrelevant to what they hope to accomplish. If a newcomer wants to write a new article, then asking them to add a title description will not teach them skills they need to be successful.

We're splitting this challenge into two parts: the sourcing the tasks and topic matching.

Sourcing the tasks
There are many different places we could find tasks for newcomers to do. Our team listed as many as we could think of and evaluated them for whether they seem to be achievable for the first version of the feature. Written below is our determination on the task type we'll be starting with, followed by a a table listing many of the task sources that we evaluated.

Maintenance templates
We're going to be starting by using maintenance templates and categories to identify articles that need work. All of our target wikis use some set of maintenance templates or categories on thousands of articles, tagging them as needing copyediting, references, images, links, or expanded sections. And previous task recommendations software, such as SuggestBot, have used them successfully. These are some examples of maintenance categories:


 * Articles needing links in Arabic Wikipedia
 * Articles needing copyediting in Korean Wikipedia
 * Articles needing references in Czech Wikipedia



In this Phabricator task, we investigated exactly which templates are present and in what quantities, to get a sense of whether there will be enough tasks for newcomers. There seem to be sufficient numbers for the initial version of this project. We are likely to incorporate other task sources from the table below in future versions.

It's also worth noting that it could be possible to supplement many of these maintenance templates with automation. For instance, it is possible to automatically identify articles that have no internal links, or articles that have no references. This is an area for future exploration.

During the week of October 21, 2019, the members of the Growth team did a hands-on exercise in which we attempted to edit articles with maintenance templates. This helped us understand what challenges we can expect newcomers to face, and gave us ideas for addressing them. Our notes and ideas are published here.

Full evaluation of task types
Below is a table showing the many sources of tasks that we evaluated in coming to the decision to start by using maintenance templates.

Topic matching
Past research and development shows that users are more likely to do recommended tasks if the tasks match their topical interests. SuggestBot uses an editor's past editing history to find similar articles, and those intelligent results are shown in this paper to be executed on more often than random results. The Content Translation tool also recommends articles based on a user's previous translation history, and those recommendations have increased the translation volume.

Our challenge with newcomers is a "cold start problem", in that newcomers do not have any edit history to use when trying to find relevant articles for them to edit. We have several ideas for how to allow users to indicate topics of interest in a list, or to type topics of interest into a search. Those ideas are being investigated in this Phabricator task.

Comparative review
Our team's designer reviewed the way that other platforms (e.g. TripAdvisor, Foursquare, Amazon Mechanical Turk, Google Crowdsource, Reddit) offer task recommendations to newcomers. We also reviewed Wikimedia projects that incorporate task recommendations, such as the Wikipedia Android app and SuggestBot. We think there are best practices we can learn from other software, especially when we see the same patterns across many different types of software. Even as we incorporate ideas from other software, we will still make sure to preserve Wikipedia's unique values of openness, clarity, and transparency. The main takeaways are below, and the full set of takeaways is on this page:


 * Task types – bucket into 4 types: Rating content, Creating content, Moderating/Verifying content, Translating content
 * Incentives – Most products offered intangible incentives mainly bucketed into the form of: Awards and ranking (badges), Personal pride and gratification (stats), or Unlocking features (access rights)
 * Reward incentives – promote badges or attainments of specific milestones (e.g., a badge for adding 50 citations)
 * Personalization/Customization – Most have at least one facet of personalization/customization. Most common customization is user input on surveys upon account creation or before a task, most common system-based personalization type is geolocalization
 * Visual design & layout – incentivizing features (stats, leaderboards, etc) and onboarding is visually rich compared to pared back, simple forms to complete short edits.
 * Guidance – Almost all products reviewed had at least basic guidance prior to task completion, most commonly introductory ‘tours’. In-context help was also provided in the form of instructional copy, tooltips, step-by-step flows,  as well as offering feedback mechanisms (ask questions, submit feedback)

Initial version
Our evolving designs can always be found in two sets of interactive mockups (use arrow keys to navigate): Those mockups contain explorations of all the difference parts of the user journey, which we have broken down into several parts:
 * Desktop
 * Mobile


 * 1) Gathering information from the newcomer: learning what we need in order to recommend relevant tasks.
 * 2) Feature discovery: the way the newcomer first encounters task recommendations.
 * 3) Task recommendations: the interface for filtering and choosing tasks.
 * 4) Guidance during editing: once the newcomer is doing a task, the guidance that helps them understand what to do.
 * 5) User feedback: ways in which the newcomer can indicate that they are not satisfied with the recommended task.
 * 6) Next edit: how we continue the user's momentum after the save an edit.

For the initial version of the project, we've decided on a subset of the user flow to deploy. This first version is itself broken down into a few parts that will roll out to users in sequence.


 * Version 1.0: users can initiate the suggested edits module, choose task types from maintenance templates based on difficulty, and click to go to the articles needing help. This version will help us see how many users are interested in receiving suggested edits, but we are not confident many will actually follow the suggestions until Version 1.1.
 * Version 1.1: users can also filter articles by topic area, such as "Art", "History", or "Technology". We expect that this will cause many more users to select edits to do, but we don't expect many will actually complete their edits until Version 1.2.
 * Version 1.2: after users click through on suggested tasks, they receive guidance on completing the task through the help panel. With this in place, we think it will be possible for users to find tasks they are interested in, and have enough informaiton to complete them.

Below are some of the current draft design concepts as the team continues to refine our approach.

Variant testing
After deploying the first version of newcomer tasks, we want to start testing different variants of the feature, so that we can improve it iteratively. Rather than just having one design of newcomer tasks, and seeing if newcomers are more productive with it than without it, we plan to test more than variant of newcomer tasks at a time, and compare them. We have compiled an exhaustive list of all the ideas of variants to test -- but we will only end up testing perhaps 10 per year, because of the effort and time it takes to build, test, and analyze. Below are some of the most important ones variants to test.

デスクトップ
2019年9月30日の週に usertesting.com 上でデスクトップ版新人編集者タスクの試作品について、ウィキメディア運動に関連のないインターネット利用者を対象に6回テストしました. 有償回答者が試作品を使って観察したことを言葉で表現し、経験について設問に答えました. 結果の全文はこちらの Phabricator タスクをご参照ください. このテストの目標は次の通りです.


 * 1) Gauge the discoverability  of the newcomer tasks module
 * 2) Identify improvements to the usability of the tasks module:
 * 3) Do users understand how to select and review article suggestions?
 * 4) Do users understand how to filter by interests and task difficulty?
 * 5) Do they know how to start editing a suggested article?
 * 6) Gauge user reactions to the suggestions and expectations about guidance through the task.


 * わかったことの概要


 * All users thought it made sense and intuitive to get suggestions based on their topics of interest.
 * Similarly, the different task difficulties was positively received by all participants.
 * Overall usability of the suggested edits module was extremely high. People knew how to click to view more articles, use the filter to change topics and task levels, and knew to click on the card to open a suggestion for editing.
 * 4/6 Participants did not initially realize that they should click on “See suggested edits” as a way to help them achieve their goal of writing a new article. This seemed to be a common mental model where users separated "Editing" as different from "Creating a new page".
 * Start module is clearly the starting point for all participants. Moreover many were drawn to “See suggested edits” button as a way to follow the progression of activities in the start module.
 * Users had a clear understanding and expectation they would be shown suggested articles for editing based on the intro dialogs to add topics and introducing task levels.
 * Everyone was able to select the popular topics and add their own topic easily.
 * Everyone understood the purpose of the suggested edits module.
 * Two people were confused/assumed that they could not create a new article until completing easy and medium tasks.
 * 5 of 6 participants knew to click on the help panel button for guidance once they entered the editor mode.
 * Four people expected to be able to contact their mentor in the help panel.
 * Task tips lacked sufficient level of guidance for a couple of participants.


 * 推薦事項


 * コンテンツの新規作成も編集行為であることの説明文の改善と、それに先立つ利用者教育の実施.
 * テスト結果に沿って影響度モジュールを更新、おすすめした編集を利用者が理解できるように補助する.
 * 編集中に役立つコンテンツヘルプを提供する. 編集に挑戦する編集者には重要.
 * ヘルプパネルのタスクのヒントに、利用者が自分で更新する「チェックリスト」を導入.
 * 何をするべきか短い説明文をつける.
 * 記事全体の文字編集をする必要はないことを利用者に伝える.
 * リアルライムの検索結果がわかると、利用者がお勧め記事の編集に関心を持ちやすくなり、自分に適した記事を探すためにフィルタ機能を使うよう誘導できる.

モバイル版
2019年9月30日の週に usertesting.com 上でモバイル版新人編集者タスクの試作品を6回テストしました. 結果の全文はこちらの Phabricator タスクをご参照ください. このテストの目標はデスクトップ版と共通しますが、さらにモバイル版の経験がデスクトップ版とどう異なると良いか項目を加えました. モバイル版のユーザーテスト被験者にはウィキペディアに画像を追加しようというシナリオを用意しました (デスクトップ版シナリオでは新規記事の作成).

わかったことの概要


 * ほぼすべての利用者が、(設計変更後の) スタートモジュールは何から始めるかガイドを段階的にわかりやすく示していると回答.
 * 以下のような「お勧めの編集」モジュールを追加すると混乱の原因にはならないものの、画像の追加タスクの途中でヘルプが見たいときにそれを開けば、やり方がわかると利用者に伝わっていない.
 * お勧めの編集は使う意欲を引き出すにはたいへん効果的で、参加者はそれぞれの要素を理解し使いこなした (フィルタ機能、記事をもっと見るなど). ところがお勧めの編集は学習目的あるいは退屈しのぎに取り組むものとしか思われなかった.
 * 回答者によっては、提示された広範な話題よりも、もっと個別の細かい話題を求めていた.
 * 掘り下げた詳しい情報の提供は教育的ではあるものの、うんざりさせる可能性も潜在する. 回答者は全員、「画像の追加」が難しい作業に分類されていることを意外に感じ、落差はあるものの一様に不満を述べた.
 * 興味の対象で絞り込みができる点はとても好評だった.
 * テストの終盤にかけて回答者3名から、「簡単」なタスクの修了「判定」もしくは必須条件を通過させてから、中程度・高度なタスクに取り掛かるべきだと述べた.
 * Everyone understood the purpose of the Suggested edits as giving edits that would users learn to edit, and also emphasize that it showed them some edits were harder to do.
 * All users struggled to use the guidance we offered through the help panel while they were editing. This is a major area we need to think hard about designing before we begin to build it.

Recommendations


 * Suggested edits call to action is inside start module, not its own card.
 * Improve copy and user education imagery to better convey that there is real world value in trying suggested edits beyond learning and that task difficulty is a guide only and tasks can be tried out of order.
 * オーバーレイを追加してそれぞれの編集者に特化した編集の提案と誘導を紹介
 * Including real-time counting of filtered results on both task and topic filters.
 * Incorporate more granular searching by interest topics by users.
 * Reiterate when a user opens a suggestion that it is a real, impactful edit.
 * 提供するヘルプのコンテンツがすべて明確に閲覧できるよう、タスク内に表示するヘルプパネルを設計更新します.

Measurement and results
To be written