Growth/Personalized first day/Structured tasks/fr

Cette page décrit le travail de l'équipe Croissance (Growth team) sur le projet « tâches structurées », qui est lié aux projets « tâches des nouveaux arrivants » et « page d'accueil des nouveaux arrivants ». Cette page contient les principaux atouts, les conceptions, les questions ouvertes et les décisions. La plupart des mises à jour progressives sur l'état d'avancement des projets seront affichées sur la page générale des mises à jour, certaines mises à jour importantes ou détaillées étant affichées ici.

Statut actuel

 * 2020-05-01: planification et documentation des notes initiales
 * 2020-05-17: begin community discussion
 * 2020-05-29: initial wireframes
 * 2020-08-24: week of planning meetings
 * 2020-09-08: call for community discussion on latest designs
 * 2020-10-21: user testing of desktop designs

Résumé
L'équipe chargée de la croissance a déployé le projet "newcomer tasks" en novembre 2019, qui suggère aux nouveaux arrivants un flux d'articles à modifier sur leur page d'accueil personnelle. Depuis avril 2020, les articles suggérés proviennent uniquement d'articles dont les modèles de maintenance ont été appliqués par des éditeurs expérimentés, qui ne donnent pas aux nouveaux arrivants d'indications particulières sur les phrases, mots ou sections qui nécessitent une attention particulière. Malgré ce manque d'orientation, nous sommes heureux de constater que les nouveaux venus ont fait des suggestions d'édition productives.

Bien que les modèles de maintenance offrent divers types de modifications à effectuer par les nouveaux arrivants, il se peut qu'ils soient trop vastes et trop ouverts pour que les nouveaux arrivants puissent réaliser leurs modifications avec succès. Et sur les appareils mobiles, les éditeurs visuels ou de wikitexte peuvent submerger les nouveaux arrivants qui tentent de les faire sur un petit écran.

C'est pourquoi nous voulons expérimenter une idée appelée « tâches structurées ». Il s'agit de décomposer le processus d'édition des articles en une série d'étapes que les nouveaux venus peuvent accomplir facilement. En suivant les exemples réussis des équipes Android et Language, nous pensons que ces types de modifications seront plus faciles à effectuer pour les nouveaux arrivants et plus faciles à réaliser sur un téléphone portable, ce qui aidera un plus grand nombre de nouveaux arrivants à effectuer davantage de modifications. Ces tâches structurées seraient accessibles aux nouveaux arrivants dans le cadre du projet de tâches pour les nouveaux arrivants. This is about breaking down editing workflows into a series of steps that newcomers can accomplish easily. Following the successful examples from Android and Language team work, we think these types of edits will be easier for newcomers to do and easier to do on mobile, helping more newcomers do more edits. These structured tasks would be accessible to newcomers as part of the newcomer tasks project.

Modifier, c'est compliqué
Grâce à l'expérience de l'équipe Croissance, nous en sommes venus à penser que les premiers moments d'un nouveau venu sur le wiki peuvent rapidement déterminer s'il veut rester ou partir. Nous pensons que les nouveaux arrivants veulent rester lorsqu'ils peuvent rapidement faire une modification et avoir une expérience positive. Mais contribuer à Wikipédia — pour presque tout type de contribution — est compliqué, et cela rend difficile pour eux de réussir une modification rapidement. Par exemple, il faut suivre une douzaine d'étapes pour faire quelque chose d'aussi simple que d'ajouter une seule phrase à un article : 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) rechercher le bon article ;
 * 2) voir si l'information que vous souhaitez ajouter est déjà présente dans l'article ;
 * 3) choisir une section dans laquelle ajouter la phrase ;
 * 4) cliquer pour commencer à modifier ;
 * 5) taper la phrase à la bonne place ;
 * 6) cliquer sur le bouton « Sourcer » ;
 * 7) retourner sur la source pour en avoir le lien ou pour citer l'information ;
 * 8) remplir et valider le formulaire de référence ;
 * 9) cliquer pour publier la modification ;
 * 10) remplir le résumé de modification ;
 * 11) publier.

Les nouveaux arrivants qui ouvrent l'éditeur visuel ou l'éditeur de wikitexte pour la première fois ne savent pas quelles sont ces étapes, dans quel ordre les faire, ni sur quels boutons cliquer pour les réaliser. En d'autres termes, leur expérience n'est pas « structurée ». Il se peut qu'ils soient simplement dépassés et qu'ils partent. Ou ils peuvent aussi tenter de réaliser quelque chose, commettre une erreur et recevoir des commentaires négatifs de la part de rédacteurs expérimentés. C'est le but de ce projet : comment aider les nouveaux venus à réaliser ces étapes dans le bon ordre ? 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.

Une esquisse de tâche structurée
La meilleure façon d'expliquer comment nous envisageons les tâches structurées peut être de montrer un rapide croquis. La première tâche structurée à laquelle nous avons pensé est « ajouter un wikilien » (lien interne). Mais les mêmes idées pourraient s'appliquer aux tâches structurées pour « ajouter une image », « ajouter une référence », ou même « ajouter un fait ».

Dans la fonctionnalité des tâches pour les nouveaux venus, beaucoup de nouveaux venus effectuent des tâches « ajouter un wikilien » — dans lesquelles ils ajoutent des liens bleus internes dans les articles qui n'en ont pas beaucoup. Cela semble être une simple tâche d'édition pour commencer. Mais nous pensons que beaucoup de nouveaux arrivants ne comprennent pas comment passer par les étapes de l'ajout d'un lien et ne savent pas quels mots transformer en liens. Nous imaginons un flux de travail qui les guide pas à pas, à l'aide d'un algorithme qui peut deviner quels mots ou phrases pourraient constituer les meilleurs liens. 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.

Dans le schéma ci-dessous, le nouveau venu arrive sur un article, et se voit suggérer un mot qui pourrait faire un bon lien interne. S'il est d'accord pour qu'il en fasse un lien, il est guidé à travers les étapes de la création du lien. Cela leur apprendra, espérons-le, à ajouter des liens par eux-mêmes à l'avenir — et peut-être apprécieront-ils de continuer à recevoir ces suggestions de liens algorithmiques. En ce qui concerne l'algorithme, l'équipe de recherche du WMF a effectué des travaux préliminaires qui nous rendent confiants dans la possibilité d'un tel algorithme. 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.



En y réfléchissant, nous avons esquissé une deuxième idée. Au lieu d'apprendre au nouveau venu à ajouter des liens à l'aide de l'éditeur visuel, cet flux de travail permet à l'utilisateur de confirmer ou de rejeter rapidement les recommandations de l'algorithme, en éditant directement l'article. Bien que ce système ne leur apprenne pas comment ajouter des liens via l'éditeur, il pourrait aider un nouveau venu à éditer à un volume élevé, et pourrait mieux convenir à un utilisateur qui essaie d'être productif avec des tâches simples, par exemple quand qu'il est en déplacement. Il pourrait également convenir aux utilisateurs qui sont « uniquement » intéressés par des modifications très simples, comme c'est le cas pour l'application Android, où de nombreux éditeurs sont « uniquement » intéressés par la description des titres. 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.



En ce qui concerne les tâches structurées, il semble que la question suivante se pose : les flux de travail doivent-ils être davantage axés sur la formation des nouveaux arrivants à l'utilisation des outils traditionnels ou sur la capacité des nouveaux arrivants à effectuer des modifications faciles à un volume plus important ?

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.

Community discussion
In May 2020, we conducted discussions with community members in six languages (English, French, Korean, Arabic, Vietnamese, Czech) about the above ideas for structured tasks. The English discussion mostly took place on the discussion page here, with other conversations on English Wikipedia, and local language conversations on the other five Wikipedias. We heard from 35 community members, and this section summarizes some of the most popular and interesting thoughts. These discussions heavily influenced our next set of designs.


 * Community members were generally positive about the potential for structured tasks to help newcomers start editing. But it was also a widely expressed view that it's important for newcomers to be introduced to the conventional source and visual editors during the process.  Community members want to make sure that newcomers are not siloed in a separate editing experience, and that they can find their way to more valuable edits.
 * The Czech community talked about ideas for how the structured tasks can place inside the visual editor, so that newcomers can start getting used to being in the editor. Perhaps the editing tools that are not needed for the structured task can be grayed-out.
 * Community members asked why we are choosing "add a link" as our first structured task, as opposed to higher-value types of edits. We talked about how this task is one of the easiest for us to build, which will help us prototype and learn from structured tasks sooner, and how it is a comparatively low-risk task, with fewer opportunities for newcomers to damage articles.
 * Several communities mentioned that spelling corrections would be a particularly valuable task, and we talked about technical options for how to generate lists of potential spelling mistakes. See these notes for more details.
 * We also talked about whether reverting vandalism is a good fit for newcomers. It doesn't seem like the answer is clear, and this will have to be discussed more in the future.
 * An idea that was mentioned multiple times is how to "step newcomers up" to progressively more challenging tasks, perhaps while giving them rewards for successfully completing easier ones.

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.