Growth/Personalized first day/Structured tasks/uk

Ця сторінка описує роботу Команди зростання над проектом «структурованих завдань», що пов'язаний із проектами «завдання для новачків» та «домашня сторінка новачка». Ця сторінка містить основні активи, дизайни та рішення. Більшість поступових змін буде публікуватися на загальній сторінці оновлень Команди зростання, а деякі великі або детальні оновлення публікуватимуться тут. This page contains major assets, designs, open questions, and decisions. Most incremental updates on progress will be posted on the general Growth team updates page, with some large or detailed updates posted here.

Поточний стан

 * 2020-05-01: планування на документування початкових нотаток

Загальний опис
Команда зростання розгорнула проект «завдання новоприбулих» у листопаді 2019 року, який дає новоприбулим канал пропонованих статей для редагування на їхніх особистих сторінках. Станом на квітень 2020 року, пропоновані статті отримуються лише зі статей з шаблонами підтримки, застосованими досвідченими редакторами, які не дають новоприбулим конкретного напрямку, в якому речення, слова чи розділи особливо потребують уваги. Попри цю нестачу напрямку, ми щасливі бачити, що новоприбулі роблять продуктивні пропоновані редагування.

Хоча шаблони підтримки дають новоприбулим робити різні типи редагувань, вони можуть бути надто широкими чи відкритими для новоприбулих, щоб упоратися з ними. А на мобільних пристроях візуальні чи вікітекстові редактори можуть переповнити новоприбулих на малих екранах.

Таким чином, ми хочемо експериментувати з ідеєю, званою «структурованими завданнями». Мова йде про розбиття робочих потоків редагування на послідовність кроків, які новоприбулі легко можуть виконати. Після успішних прикладів роботи команд Android і Мовної, ми думаємо, що ці типи редагувань буде легше робити новоприбулим і на мобільних пристроях, більше допомагаючи новоприбулим робити більше редагувань. Ці структуровані завдання будуть доступні новоприбулим як частина проекту завдань для новоприбулих.

Редагування складне
Through the Growth team's experience, we've come to believe that a newcomer's first moments on the wiki can quickly determine whether they want to stay or leave. We believe that newcomers want to stay when they can quickly make an edit and have a positive experience. But contributing to Wikipedia -- almost any type of contribution -- is complicated, and this makes it hard for them to succeed quickly. For instance, there are about a dozen steps required in order to do something as simple as adding a single sentence to an article:


 * 1) Знайти потрібну статтю.
 * 2) З'ясувати, чи присутня інформація, яку ви хочете додати, у статті.
 * 3) Обрати розділ, до якого додати речення.
 * 4) Натиснути для початку редагування.
 * 5) Набрати речення у потрібному місці.
 * 6) Натиснути на кнопку цитування.
 * 7) Повернутися до джерела для отримання посилання чи інформації про цитування.
 * 8) Заповнити та зберегти форму цитування.
 * 9) Натиснути для публікації редагування.
 * 10) Заповнити опис редагування.
 * 11) Опублікувати.

Newcomers looking at the visual or wikitext editor for the first time don’t know what those steps are, what order in which to do them, or which buttons to click to make them happen. In other words, their experience is not structured. They may just be overwhelmed and leave. Or they may use trial-and-error, make a mistake, and get negative feedback from experienced editors. That's what this project is about: how might we help newcomers step through these workflows in the right order?

Building on knowledge from other teams
Adding structure to editing workflows has been part of the Wikimedia projects for a long time. Some examples include:


 * HotCat: lets users choose categories to add to articles with a few clicks, instead of manually editing the wikitext.
 * Commons Upload Wizard: breaks the process of uploading media to Commons into a series of a simple steps.
 * Citoid: available in the Visual Editor, this breaks down the process of adding a citation into steps that include algorithms to automatically produce the citation text and template.

Most recently, the idea of "structured tasks" has been working well on the Wikipedia Android app and in the Content Translation tool. We're inspired by their work.

With their "suggested edits" project, the Android team broke down the process of adding a title description to a Wikipedia article into one easy step of typing into a text box. They have since done the same with translating title descriptions across languages. In order to do the same tasks without a structured workflow, users would have to go to Wikidata and go through several steps to make those same edits. The team learned that this method works: many Android users make hundreds of these small contributions.

The Language team built the Content Translation tool, which does several things to structure the process of translating an article. It offers a side-by-side interface built for translations, it breaks the translation down into sections, and it automatically applies machine translation algorithms. Though Wikipedians could translate articles before the existence of the tool, the number of manual steps required made it very difficult. This tool is successful, with hundreds of thousands of translations completed. We learned that when translating an article is broken down into steps, with rote parts (e.g. running machine translation) taken care of automatically, more articles get translated.

The Growth team is thinking about applying these same principles to content edits in articles, like adding links, adding images, adding references, and adding sentences.

A structured task sketch
The best way to explain how we're thinking about structured tasks may be through showing a quick sketch. The first structured task we've thought about is "add a (wiki)link". But the same ideas could apply to structured tasks for "add an image", "add a reference", or even "add a fact".

In the newcomer tasks feature, lots of newcomers complete "add a (wiki)link" tasks -- in which they add internal blue links in articles that don't have many. This seems like a simple editing task to get started. But we think that many newcomers may not understand how to go through the steps of adding a link and may not know which words to make into links. We're imagining a workflow that walks them through it, step-by-step, with the assistance of an algorithm that can guess which words or phrases might make the best links.

In the sketch below, the newcomer arrives on an article, and is given a suggestion of a word that might make a good (wiki)link. If they agree that it should be made a link, they are walked through the steps of making the link. This will hopefully teach them to add links on their own in the future -- and perhaps they'll enjoy continuing to receive these algorithmic link suggestions. Regarding the algorithm, the WMF Research team has done some preliminary work that makes us confident that such an algorithm is possible.



In thinking further about this, we sketched a second idea. Instead of being aimed toward teaching the newcomer to add links using the visual editor, this next workflow lets the user quickly confirm or reject recommendations from the algorithm, directly editing the article. While it does not teach them how to add links via the editor, it might help a newcomer edit at high volume, and might be a better fit for a user who is trying to be productive with simple tasks while they are on the go. Or perhaps might be a good fit for users who only are interested in very simple edits, similarly to how the Android app has many editors who only want to write title descriptions.



In thinking about structured tasks, it looks like this might be a big question: should workflows be more aimed toward teaching newcomers to use the traditional tools, or be more aimed toward newcomers being able to do easy edits at higher volume?

Why this idea is prioritized
We think that quickly making productive edits is what leads to newcomer success. Once they've done some edits, the rest of the wiki experience quickly becomes richer. Newcomers can then see their impact, get thanked, ask informed questions to their mentors, create their userpage, etc. Therefore, we want lots of newcomers to make their first edits as soon as possible. We have already seen from the newcomer tasks project that many newcomers are looking for easy tasks to do. But we also have observed these things:


 * Only about 25% of the newcomers who click on a suggestion actually edit it.
 * Only about 25% of those who do a suggested edit do another one.
 * There are a handful of newcomers who really thrive on suggested edits, doing dozens of them every day. This shows the potential for newcomers to accomplish a lot of wiki work.
 * In live user tests, when newcomers are told to copyedit an article or add links to an article, they frequently want to know exactly which sentence or words need their attention. In other words, attempting to edit the full article is too open-ended.

Taking these points along with the experiences described above of the Android and Content Translation teams, we think we could increase the number of newcomers editing and continuing to edit by structuring some of the content editing workflows in Wikipedia.

Opportunities with structured tasks
When we break down editing workflows into steps, we call them "structured tasks". Here are some of the possible benefits we think could come from structured tasks:


 * Make it easy for newcomers to make meaningful contributions.
 * Develop editing workflows that make sense for mobile. Mobile design principles tell us that users should see one step at a time, not a complicated workspace.
 * Let newcomers increase their skills incrementally. They could take on successfully more challenging types of tasks.
 * Let people find an editing experience that fits them. By giving newcomers a feed of structured tasks, they could find the type of tasks that they prefer.
 * Perhaps similar workflows could be opened to experienced editors in the future.

Concerns and downsides to structured tasks
Whenever we add new ways for people to edit Wikipedia, there are many things that can go wrong:


 * By making editing too quick and easy, we may attract vandals, or users who don't apply enough care when editing.
 * Giving newcomers simple workflows may keep them from learning the traditional editing tools, which are essential for doing the most impactful wiki work.
 * Structured tasks may not be good at accounting for differences across languages, idiosyncrasies with wikitext, and could cause other kinds of bugs.
 * Algorithms that surface structured tasks may not be accurate enough, and falsely encourage newcomers to complete edits they shouldn't.

Types of tasks
There are many different editing workflows that have the potential to become structured. We began to list workflows when we first designed the newcomer tasks workflow here, and we have since narrowed down to a shorter list of task types that seem best suited to being structured. The table below contains that short list, ranked in a potential priority order.

Prioritizing "add a link"
The Growth team currently (May 2020) wants to prioritize the "add a link" workflow over the other ones listed in the table above. Although other workflows, such as "copyedit", seem to be more valuable, there are a set of reasons we would want to start first with "add a link":


 * In the near term, the most important thing we would want to do first is to prove the concept that "structured tasks" can work. Therefore, we would want to build the simplest one, so that we can deploy to users and gain learnings, without having to invest too much in the first version. If the first version goes well, then we would have the confidence to invest in types of tasks that are more difficult to build.
 * "Add a link" seems to be the simplest for us to build because there already exists an algorithm built by the WMF Research team that seems to do a good job of suggesting wikilinks (see the Algorithm section).
 * Adding a wikilink doesn't usually require the newcomer to type anything of their own, which we think will make it particularly simple for us to design and build -- and for the newcomer to accomplish.
 * Adding a wikilink seems to be a low-risk edit. In other words, the content of an article can't be as compromised through adding links incorrectly as it could through adding references or images incorrectly.

Notes on "copyedit"
In conversations with community members on this project's discussion page, many people brought up the question of how to make a structured task around copyediting. Correcting spelling, grammar, punctuation, and tone seemed to everyone to be a clearly useful task that should be prioritized. The Growth team initially shied away from this workflow because of scaling concerns: even if we were able to find or develop an algorithm that could reliably find copyedits in one language, would we be able to do that in dozens of other languages?

We began to learn more about this by talking with User:Beland, who developed the "moss" script for English Wikipedia's Typo Team. We wanted to understand how the process works, and what it might look like to do something similar in other languages. In short, it sounds like the most promising avenue is through existing open-source spellcheckers and dictionaries. Two examples are the aspell and hunspell libraries. Below are our notes from learning about "moss" with User:Beland.


 * Prospects for doing something similar in other languages
 * A process like this should theoretically work in other languages, given that other languages also have Wiktionaries and open-source spellcheckers.
 * But it would not be possible to deploy in a new language without native speakers validating it. There would likely need to be customization for many languages.
 * Likely more challenges for languages without word segmentation (e.g. Japanese).
 * Likely more challenges for agglutinative languages.
 * Different projects have differing manuals of style, which may cause issues.
 * If an algorithm is performing poorly, it should always be possible to change its thresholds so that it identifies fewer potential errors, but with higher confidence.
 * How does moss work?
 * Download the dump files of all of English Wikipedia every two weeks.
 * In order to cut down on false positives, remove templates and everything inside quotation marks, etc.  Only want to work on the main text in the article: the things written “in Wikipedia’s voice”.
 * Check that every word is in English Wiktionary.
 * Uses Python NLTK (natural language toolkit) for word segmentation.
 * Looks at edit distance to classify misspellings.  e.g. “T1” is one edit distance (95% precision).  Also classifies “TS” whitespace errors.
 * Also includes an English open-source spellchecker to narrow the search space so that the algorithm can run faster.
 * He has also started trying to add grammar rules (e.g. identifying passive voice), but that’s more experimental, and much more difficult than spelling.
 * At the end of the process, it produces a list of articles and likely typos.  The user opens the article and searches for the likely typo.

Many copyedit requests are also editors whose native language is not English, asking for English polishing. See WikiProject Guild of Copy Editors.

Design
While the "structured task sketch" section above contains some quick initial sketches to demonstrate the idea behind structured tasks, this section contains our current design thinking. To look into the full set of thinking around designs for the "add a link" structured task, , which contains background, user stories, and initial design concepts.

Comparative review
When we design a feature, we look into similar features in other software platforms outside of the Wikimedia world. These are some highlights from comparative reviews done in preparation for Android’s suggested edits feature, which remain relevant for our project.


 * Task types – are divided into five main types: Creating, Rating, Translating,  Verifying content created by others (human or machine), and Fixing content created by others.
 * Visual design & layout – incentivizing features (stats, leaderboards, etc) and onboarding is often very visually rich, compared to pared back, simple forms to complete short edits. Gratifying animations often compensate for lack of actual reward.
 * Incentives – Most products offered intangible incentives grouped into: Awards and ranking (badges) for achieving set milestones, Personal pride and gratification (stats), or Unlocking features (access rights)
 * Users motivations – those with more altruistic motivations (e.g., help others learn) are more likely to be incentivized by intangible incentives than those with self-interested motivations (e.g., career/financial benefits)
 * Personalization/Customization – was used in some way on most apps reviewed. The most common customization was via surveys during account creation or before a task; and geolocalization used for system-based personalization.
 * 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 wireframes
After organizing our thoughts and doing background research, the first visuals in the design process are "wireframes". These are simply meant to experiment and display some of the ideas we think could work well in a structured task workflow. For full context around these wireframes, see the.