Growth/Positive reinforcement/bn

এই পাতায় গ্রোথ দলের বৈশিষ্ট্যগুচ্ছের অংশ হিসেবে "ইতিবাচক প্রেরণা" প্রকল্পকে লিপিবদ্ধ করা হয়েছে। এই পাতায় প্রধান প্রধান সামগ্রী, নকশা, উন্মুক্ত প্রশ্নাবলী এবং সিদ্ধান্তসমূহ লিপিবদ্ধ রয়েছে।

ধারাবাহিক উন্নতির হালনাগাদকৃত তথ্য সাধারণত গ্রোথ দলের হালনাগাদকৃত পাতায় দেয়া হবে। বৃহত্তর এবং বিস্তারিত তথ্য এখানে উল্লেখ করা হবে।

বর্তমান অবস্থা

 * 2021-03-01: প্রকল্পের পাতা তৈরি করা হয়েছে
 * 2022-02-25: project kicked off with team discussions
 * 2022-03-01: project page expanded
 * 2022-05-11: community discussion

Summary
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.

Our big question here is: How might we encourage newcomers who have visited our homepage and tried our features to keep editing and build on their momentum?

Background
When the newcomer homepage was deployed in 2019, it contained a basic "impact module", which listed the number of pageviews for the pages the newcomer had edited. That is the only part of the Growth features that give the newcomer any sense of their impact, and we have not improved on it since it was first deployed. 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.
 * Appreciation from other users has been shown to increase retention, such as in the case of "thanks" (here and here) and in an experiment on German Wikipedia. We believe that these reinforcements from real people would be more effective than automated ones coming from the system.
 * 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.

User persona
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.

Ideas
We have three main ideas for positive reinforcement. We may pursue multiple ideas as we work on this project.

“Thanks received” count, to highlight the ability to receive community recognition.
 * 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).

Community discussion
Adding incentives to the editing experience will hopefully encourage constructive contributions, but we also know that it could cause unexpected or undesired behavior from users. 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!


 * What has worked well on your wiki for motivating newcomers?
 * Which of the ideas above do you think have the most promise? The least promise?
 * What could go wrong and what should we try to avoid?

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.