গ্রোথ/ইতিবাচক প্রেরণা

দল:	গ্রোথ
শুরু:	2022-02-25
দলীয় সদস্য:	Gergő Tisza (software engineer), Benoît Evellin (movement communications specialist), Elena Tonkovidova (QA engineer), Sergio Gimeno (software engineer), Mew Ophaswongse (software engineer), Morten Warncke-Wang (data analyst), Rita Ho (senior user experience designer), Mariya Shilova (program manager)
দলপতি:	Kosta Harlan
ব্যবস্থাপনা:	Kirsten Stoller (product)
হালনাগাদ:	Most updates posted here.

This page is a translated version of the page Growth/Positive reinforcement and the translation is 23% complete.

Outdated translations are marked like this.

Tracked in Phabricator
Task T248330

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: দলের মধ্যকার আলোচনার মাধ্যমে প্রকল্পের যাত্রা শুরু হয়েছে
2022-03-01: প্রকল্পের পাতা সম্প্রসারিত হয়েছে
2022-05-11: সম্প্রদায়ের আলোচনা
2022-08-12: user testing complete
2022-11-24: current designs and measurement and experiment plan added
2022-12-01: new impact module released to pilot wikis
2023-02-07: Leveling up and Personalized praise work started & second community discussion started
2023-02-14: published Newcomer task milestone analysis which will help guide Leveling up work
2023-03-22: Leveling up features released as an A/B test at Growth pilot wikis
2023-03-24: published Thanks Usage analysis
2023-05-25: released Personalized praise module on Growth pilot wikis
2023-10-01: released new Impact module on all Wikipedias & published Leveling up experiment results
Next: release Personalized praise on all Wikipedias

সারাংশ

গ্রোথ দল “নবাগতদের সমন্বিত অভিজ্ঞতা” নির্মাণের জন্য কাজ করছে, যা নবাগতদের উইকিপিডিয়ার সম্প্রদায়ের অনুসরণকৃত আচারের সাথে যুক্ত হতে প্রয়োজনীয় উপাদানগুলো দিয়ে সহায়তা করবে। উদাহরণস্বরূপ, নবাগতদের কাজের ক্ষেত্রে আমরা তাদের অংশগ্রহণের সুযোগ এবং মেন্টরশিপ মডিউলের মাধ্যমে মেন্টরশিপ-এর সুযোগ দিয়েছি। পরামর্শকৃত সম্পাদনার মাধ্যমে অনেক নবাগত তাদের প্রথম সম্পাদনা করতে সক্ষম হয়েছে। এর ধারাবাহিকতায় আমরা নবাগতদের উৎসাহিত করতে প্রয়োজনীয় পদক্ষেপ নিতে চাই যেন তারা আরো সম্পাদনা করেন। এর মাধ্যমে একটি অনুন্নত উপাদান আমরা লক্ষ্য করেছি, যা হলো কর্মক্ষমতা মূল্যায়ন। আমরা এই প্রকল্পকে বলছি “ইতিবাচক প্রেরণা”।

আমরা চাই নবাগতরা যেন বুঝতে পারে যেন উইকিপিডিয়ায় নিয়মিত অবদানকারীদের অগ্রগতির সুযোগ এবং কাজের মূল্য রয়েছে, যেন প্রথম সম্পাদনা করা অবদানকারীরা আবার ফিরে আসে।

আমাদের এখানে বড় একটি প্রশ্ন হলো: আমরা কীভাবে নীড়পাতায় পরিভ্রমণ করা নবাগতদের নিয়মিত সম্পাদনা করতে এবং তাদের গতি বাড়াতে সহায়তা করব?

প্রেক্ষাপট

যখন ২০১৯ সালে নবাগতদের নীড়পাতা প্রথম যাত্রা শুরু করে, এতে একটি সাধারণ "অবদানের প্রভাব" রাখা হয়, যেখানে নবাগত কর্তৃক সম্পাদিত পাতার পরিভ্রমণসংখ্যা দেখানো হয়। গ্রোথ বৈশিষ্ট্যের এই অংশেই কেবলমাত্র নবাগতদের তাদের কাজের প্রভাব সম্পর্কে ধারণা দেয়া হয়। আমরা প্রথমবার স্থাপনের পর থেকে এটা নিয়ে আর কোনো কাজ করিনি।

এর মাধ্যমে আমরা ইতিবাচক প্রেরণা নিয়ে কিছু গুরুত্বপূর্ণ ব্যাপার দেখতে পেয়েছি:

আমরা সম্প্রদায়ের সদস্যদের মধ্য থেকে এই মডিউল নিয়ে ইতিবাচক মন্তব্য পেয়েছি। অভিজ্ঞ অবদানকারীরা বলেছেন যে এটা আকর্ষণীয় এবং তাদের জন্য মূল্যবান।
অন্যান্য ব্যবহারকারীদের থেকে প্রাপ্ত প্রশংসা অবদানকারীদের ধরে রাখতে সহায়তা করে বলে দেখা গেছে, যেমনটা দেখা গেছে "ধন্যবাদ" দেয়ার ক্ষেত্রে (এখানে এবং এখানে দেখুন) এবং জার্মান উইকিপিডিয়ায় পরিচালিত গবেষণায়। আমরা বিশ্বাস করি যে প্রকৃত ব্যক্তির থেকে আসা এই প্রেরণা সিস্টেম থেকে স্বয়ংক্রিয়ভাবে আসা কোনো কিছুর চেয়ে আরো বেশি কার্যকর হবে।
সম্প্রদায়ের সদস্যগণ জানিয়েছেন যে নবাগতদের জন্য সহজ থেকে শুরু করে ধীরে ধীরে আরো কঠিন কিছুতে যাওয়াটাই বেশি গুরুত্বপূর্ণ, সহজ কিছুর মধ্যে সীমাবদ্ধ থাকার চেয়ে।
গুগল, ডুয়োলিঙ্গো, গিটহাব প্রভৃতি প্ল্যাটফর্ম নানাবিধ ইতিবাচক প্রেরণাসূচক ব্যাজ ও লক্ষ্যের ব্যবস্থা করে থাকে।
সম্প্রদায় খারাপ সম্পাদনাকে উৎসাহিত করা নিয়ে চিন্তিত। আমরা দেখেছি যে সম্পাদনা প্রতিযোগিতা ক্যাশ পুরস্কার দিয়ে থাকে, বা যখন ব্যবহারকারীর অধিকার "স্বয়ংনিশ্চিত" হওয়ার জন্য সম্পাদনা গণনার ব্যাপার থাকে, তখন মানুষ সমস্যাযুক্ত সম্পাদনা করতে উৎসাহিত হতে পারে।

ব্যবহারকারীর ব্যক্তিত্ব

নবাগতদের যাত্রার বিভিন্ন অংশে আমরা ধারণ করার দিকে গুরুত্ব দিতে পারি। আমরা একটি বা কয়েকটি সম্পাদনা করার পরে থেমে যাওয়া নবাগতদের প্রতি দৃষ্টি আরোপ করতে পারি, অথবা আমরা কয়েক সপ্তাহ সক্রিয় থেকে থেমে যাওয়া নবাগতদের প্রতিও দৃষ্টি দিতে পারি। এই প্রকল্পের জন্য আমরা সেইসব নবাগতদের দিকে দৃষ্টি দিতে চাই, যারা প্রথম সম্পাদনার ধাপ অতিক্রম করে থেমে গেছে। আমরা দ্বিতীয়বারের জন্য তাদেরকে ফিরিয়ে আনতে চাই। এই ডায়াগ্রামে এ সমস্ত কিছু হলুদ তারা দিয়ে দেখানো হয়েছে।

আমরা এই ধাপের নবাগতদের দিকে দৃষ্টি দিতে চাই, কারণ সম্পাদনার ধারায় তারাই সর্বাগ্রে আছেন, এবং আমরা তাদের ধরে রাখতে চাই। এই স্থানে আমরা উল্লেখযোগ্য হারিয়ে যাওয়ার হারও দেখতে পাই। অতএব আমরা যদি এই স্থানে নবাগতদের ধরে রাখতে পারি, তবে অবদানকারীর উন্নয়নে এটি উল্লেখযোগ্য অবদান রাখতে পারবে।

গবেষণা ও নকশা

উইকি এবং উইকির বাইরে মানুষকে অবদান রাখতে উৎসাহিত করার বিভিন্ন প্রক্রিয়া নিয়ে গবেষণা পরিচালনা করা হয়েছে। গবেষণা থেকে প্রাপ্ত কিছু মূল তথ্য হলো:

উইকিপিডিয়া অবদানকারীদের জন্য প্রেরণা অনেক রকম হতে পারে, এবং অভিজ্ঞতা ও সময়ভেদে পরিবর্তিত হতে পারে। নতুন অবদানকারীরা প্রায়শই আগ্রহ এবং সামাজিক সংযোগ দ্বারা পরিচালিত হয়, মতাদর্শের চাইতে।
অভ্যন্তরীণ প্রকল্প অন্তর্নিহিত প্রণোদনার প্রতি গুরুত্ব দেয় যা পরোপকারী হিসেবে উৎসাহিত করে, এবং সুনির্দিষ্টভাবে প্রয়োগ করা হয় না।
মতাদর্শভিত্তিক উদ্দীপণার বাইরে গিয়ে উৎসাহ দেয়ার মাধ্যমে বিভিন্ন ধরণের অবদানকারীদের উইকিপিডিয়ায় প্রতি আকৃষ্ট করে তোলা সম্ভব হবে।
অভিজ্ঞ অবদানকারী ও মেন্টরদের থেকে ইতিবাচক বার্তা প্রাপ্তি সংক্ষিপ্ত সময়ের জন্য ধরে রাখার ক্ষেত্রে কার্যকর হিসেবে প্রমাণিত হয়েছে।

ইতিবাচক প্রেরণার জন্য বর্তমান নকশার সারসংক্ষেপ দেখতে পাবেন এই এক নজরে নকশার পাতায়। আমাদের নকশা সম্প্রদায়ের মতামত এবং একাধিক ধাপের ব্যবহারকারী পরীক্ষণের ভিত্তিতে পরিবর্তিত হবে।

পরিকল্পনাসমূহ

ইতিবাচক প্রেরণার জন্য আমাদের মূল তিনটি পরিকল্পনা রয়েছে। আমরা প্রকল্পে সামনে এগোনোর সময় একাধিক পরিকল্পনা নিয়ে কাজ করতে পারি।

প্রভাব

প্রভাব: অবদানের প্রভাবকেই এখানে ঢেলে সাজানো হয়েছে। যুক্ত করা হয়েছে সংশ্লিষ্ট পরিসংখ্যান, লেখচিত্র, এবং অবদানের অন্যান্য তথ্য। পরিমার্জিত অবদানের প্রভাব মডিউলে নতুন সম্পাদকদের তাদের অবদানের ব্যাপারে আরো বেশি প্রেক্ষাপট জানানো হবে, একই সাথে তাদের অবদান রাখা চালিয়ে যাওয়াটাকেও উৎসাহিত করা হবে। অনুসন্ধানের স্থানসমূহের মধ্যে রয়েছে:
- পরামর্শকৃত সম্পাদনার মাইলফলক, যেন ব্যবহারকারীরা পরামর্শকৃত সম্পাদনা করতে চেষ্টা করে।
- কীভাবে সময়ের সাথে ব্যবহারকারী সম্পাদনা করছে, তার পরিসংখ্যান (অনেকটা এরকম X সরঞ্জাম)।
- “প্রাপ্ত ধন্যবাদ” সংখ্যা, যা সম্প্রদায়ের স্বীকৃতিলাভের ক্ষমতাকে তুলে ধরে।
- সাম্প্রতিক অবদানের কার্যকলাপ - যেখানে সময়ের সাথে নবাগতদের সম্পাদনার লেখচিত্র দেখানোর মাধ্যমে অবদান চালিয়ে যেতে কিংবা অবদান পুনরায় শুরু করার ব্যাপারে মনে করিয়ে দেয়া হবে।
- নবাগতরা অবদান রেখেছে এমন নিবন্ধে পাঠকদের পঠন সক্রিয়তা প্রদর্শন (উইকিপিডিয়া:পাতা_পরিদর্শন-এর মত)।

অবদানের মডিউলের নকশা ক - অন্যদের (পাঠক ও সম্পাদক) প্রতি ব্যবহারকারীর অবদানের উপর আরো গুরুত্ব আরোপ
অবদানের মডিউলের নকশা খ - ব্যবহারকারীর সাম্প্রতিক সম্পাদনার কার্যক্রমের প্রতি আরো গুরুত্ব আরোপ

পরবর্তী ধাপে গমন

পরবর্তী ধাপে গমন: এটা সম্প্রদায়ের জন্য গুরুত্বপূর্ণ যেন নবাগতরা আরো গুরুত্বপূর্ণ কাজের দিকে যেতে পারে। যারা অনেক সহজ কাজ করতে পেরেছে, আমরা তাদেরকে আরো কঠিন কাজের দিকে আকৃষ্ট করতে চাই। এটা কিছু সংখ্যক সহজ সম্পাদনা শেষ করার পরে হতে পারে, বা তাদের নীড়পাতাতেও উৎসাহ প্রদান করা যেতে পারে। অনুসন্ধানের স্থানসমূহের মধ্যে রয়েছে:
- নবাগতরা সম্পাদনার পরে সাফল্যের বার্তা দেখবে যা তাদের একই বা আরো কঠিন ধাপের সম্পাদনা করতে উৎসাহিত করবে।
- পরামর্শকৃত সম্পাদনা মডিউলে আরো কঠিন কাজ করার সুযোগ দেয়া হবে, যেন নবাগতরা সহজে দক্ষ সম্পাদক হতে পারে।
- অবদানের মডিউলে মাইলফলক গণক বা পুরস্কারের একটি স্থান থাকবে।
- নীড়পাতায় কিছু পুরস্কার (ব্যাজ/সনদপত্র) প্রাপ্তির জন্য চ্যালেঞ্জ দেয়া হবে, যার জন্য পৃথক একটি মডিউল থাকবে।
- আরো কঠিন কাজের দিকে নিয়ে যেতে বিজ্ঞপ্তির ব্যবস্থা করা।

দৈনিক সম্পাদনা যুক্ত নকশার ভাবনা
নবাগতরা আরো কঠিন কাজের দিকে যেতে পারেন এবং স্বীকৃতি পেতে পারেন।
নবাগতের নীড়পাতায় একটি পুরস্কার মডিউল
Newcomers making poor quality “fast edits” could receive guidance
Newcomers who complete a challenge could receive a sharable "Skilled newcomer" award

ব্যক্তিগতকৃত প্রশংসা

ব্যক্তিগতকৃত প্রশংসা: গবেষণায় দেখা গেছে যে অন্য ব্যবহারকারীদের থেকে প্রশংসা ও উৎসাহের মাধ্যমে নবাগতদের থেকে যাওয়ার হার বেড়ে যায়। আমরা চিন্তা করছি কীভাবে ভালো অবদানের জন্য নতুনদের ধন্যবাদ এবং পুরস্কার দিতে অভিজ্ঞ ব্যবহারকারীদের উৎসাহিত করে তোলা যায়। হয়ত মেন্টর ড্যাশবোর্ডে বা বিজ্ঞপ্তির মাধ্যমে এ ধরণের কিছু সম্পর্কে দৃষ্টি আকর্ষণ করা হলে মেন্টরগণ উৎসাহিত হবেন। আমরা বিদ্যমান যোগাযোগের প্রক্রিয়া ব্যবহার করতে পারি, যা গবেষণামতে ইতিবাচক প্রভাব রাখতে সক্ষম। অনুসন্ধানের স্থানসমূহের মধ্যে রয়েছে:
- নীড়পাতায় নবাগতদের মেন্টরের থেকে একটি ব্যক্তিগত বার্তা।
- উইকিমিডিয়া গ্রোথ দল বা মেন্টরের থেকে বিজ্ঞপ্তি।
- নির্দিষ্ট সম্পাদনায় “ধন্যবাদ” জ্ঞাপন।
- মেন্টর বা উইকিমিডিয়া গ্রোথ দলের থেকে সুনির্দিষ্ট সম্পাদনার জন্য মাইলফলক ব্যাজ।

নবাগতের নীড়পাতায় “ধন্যবাদ” প্রদর্শন।
নবাগতের নীড়পাতায় উইকিভালোবাসা প্রদর্শন করা

সম্প্রদায়ের আলোচনা

We discussed the Positive Reinforcement project with community members from আরবি উইকিপিডিয়া, বাংলা উইকিপিডিয়া, চেক উইকিপিডিয়া এবং ফরাসি উইকিপিডিয়া, and here on mediawiki.org.

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.

Impact


We heard...	Plans to iterate based on feedback
😊 Looks good!	This idea seems the least controversial and most supported. We will plan to start development on this first, and allow for more time to refine other ideas.
😐 The impact module would be more effective if it scaled with editors as they gained experience.	We plan to focus on newcomers for now, but the new impact module will be built in an extensible way to accommodate improvements in the future.

Leveling up


We heard...	Plans to iterate based on feedback
😊 Leveling up ensures newcomers don't get "stuck" on easy tasks	Once users have a certain number of unreverted edits of one type, we should suggest they try more difficult tasks.
😊 Newcomers are often eager for awards	If we give awards they will need to feel meaningful to newcomers, and ideally are sharable either on-wiki (on their user page) or off-wiki.
❌ Goal-based incentives might be problematic, and may result in low-quality edits	Incentives that include a time-based element (similar to service awards) might be an effective approach as they factor in not only number of edits, but length of time registered. Certain "quality gates" could help slow down and guide newcomers if they are making edits that are getting reverted. We plan to reduce the scope on the award-side of "Leveling Up" for now, and focus more on encouraging users to try more difficult task-types as they are successful with easier tasks.
❌ Daily goals might be stressful and demotivating for some people	We will review this idea further and likely allow for goal customization if we pursue this idea.

Personalized praise


We heard...	Plans to iterate based on feedback
😊 Spreading praise and positivity might help increase newcomer retention.	We are still refining designs for how to encourage more Thanks and personalized paise of newcomers, but hope to have further design ideas to present soon.
😐 Scaling personalized praise might be a challenge as it takes more time for experienced editors.	Mentors are already busy, so we hope to find a way to surface "praise-worthy" mentees. We will also brainstorm other ideas that don't rely on just mentors.
😐 We should use existing systems (Thanks, WikiLove, etc.)	Plans aren't finalized, but we definitely plan to take advantage of existing systems.

Other ideas:

Community members suggested several other ideas for improving newcomer engagement and retention. We think these are all valuable ideas (some of which we are already exploring or want to work on in the future) but the following ideas won't fit within the scope of the current project:

Send newcomers onboarding and welcome emails (the Growth team is actually currently exploring engagement emails in collaboration with the Marketing and the Fundraising teams).
Expose newcomers to Wikiprojects that relate to their interests.
Include a customizable widget on the newcomer homepage to allow wikis to promote certain newcomer tasks or events.
Send notifications to users who welcome newcomers once the newcomer reaches certain editing milestones (to help prompt the user to offer Thanks or Wikilove).

Second community consultation:

In February 2023, we completed a community consultation in which we reviewed the most recent Leveling up designs with the Growth Pilot wikis. This consultation was completed in English on MediaWiki, and at Arabic Wikipedia, Bengali Wikipedia, Czech Wikipedia, and Spanish Wikipedia. (T328356) In general, feedback was quite positive. These two tasks help address feedback mentioned by those that responded to our questions:

Leveling up: Community configuration (T328386)
Leveling up: Second design iteration of "Try a new task" dialog (T330543)

In March 2023, we completed a community consultation in which we reviewed the most recent Personalized praise designs with the Growth Pilot wikis. This consultation was completed on English Wikipedia, Arabic Wikipedia, Bengali Wikipedia, Czech Wikipedia, French Wikipedia, Spanish Wikipedia, and at MediaWiki in English. (T328356) Most feedback was supportive of Personalized praise features, but several further improvements were requested. We've created Phabricator tasks to address these further improvements.

On Arabic Wikipedia, and other wikis with Flagged Revisions, mentors want to see not only the number of edits a user had completed, but more details on the review status of edits (T333035)
Mentors want to be able to view the number or percentage of reverts their mentee has, and customize how many reverts a newcomer can have to be considered praiseworthy (T333036)
Mentors would appreciate knowing which edit a mentee is Thanked for (T51087)

ব্যবহারকারী পরীক্ষণ

সম্প্রদায়ের আলোচনার মাধ্যমে আমরা কিছু প্রাথমিক নকশা ও অনুকল্প যাচাই করতে চাই। So our design research team conducted Positive Reinforcement user testing aimed to better understand the project's impact on newcomer contribution across several different languages.

We tested several static Positive Reinforcement designs with Wikipedia readers and editors in Arabic, Spanish, and English. 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

Tracked in Phabricator
Task T222310

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

Tracked in Phabricator
Task T317813

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.

“Level up” post-edit dialog message
Post-edit dialog for non-suggested edits
New notification

Personalized praise

Tracked in Phabricator
Task T317817

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 (T51541, T90404). We hope this will increase Thanks and positivity across the wikis, and hopefully newcomers will benefit from this directly or indirectly.

Design for a new Mentor dashboard module
Design of the settings view of the new dashboard module

পরিমাপ ও ফলাফল

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.

The Positive Reinforcement features increase our core metrics of retention and productivity.
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.
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.
The positive feedback cycle created by the Positive Reinforcement features will lead to a significantly higher proportion of "highly active" newcomers.
The Positive Reinforcement features increase the number of Daily Active Users of Suggested edits.
The average number of edit sessions during the newcomer period (first 15 days) increases.
"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:

Impact: treatment group gets the updated Impact module.
Leveling up: treatment group gets both the updated Impact module and the Leveling up features.
Personalized praise: all mentors get the Personalized praise features.

Leading indicators and plan of action

While we believe that the features we develop are not detrimental to the wiki communities, we want to make sure we are careful when experimenting with them. It is good practice to define a set of leading indicators together with plans of what action to take based if a leading indicator suggests something isn't going the way it should. We have done this for all our past experiments and do so again for the experiments we plan to run as part of this project.

Impact

Impact of the Impact module - results published on Jan 24, 2023.
Indicator	Expected result	Plan of action	Results
Impact module interactions	No difference or increase	If Impact module interactions decrease, then this suggests that we might have performance or compatibility issues with the new Impact module. If the proportion of newcomers who interact with the new Impact module is significantly lower than the old module we investigate the cause, reverting back to the old module if necessary.	Significant decrease
Mentor module interactions	No difference	The new Impact module takes up more screen real estate than the old module, which might lead to newcomers not finding the Mentor module as easily as before. If the number of newcomers who interact with the Mentor module is significantly lower for those who get the new Impact module, we investigate the need for design changes.	No signifiant difference
Mentor module questions	No difference	Similar concerns as for interactions with the Mentor module, if the number of questions asked to mentors is significantly lower for newcomers who get the new Impact module, we investigate the need for design changes.	No signifiant difference
Edits and revert rate	No difference in both edits and reverts, or an increase in edits and a decrease in revert rate	If there is an increase in the revert rate, this may suggest that newcomers are making unconstructive edits in order to inflate their edit or streak count. If the revert rate of newcomers who get the new Impact module is significantly higher than the old, we investigate their edits and decide whether changes are needed.	No signifiant difference (once outliers are removed)

Tracked in Phabricator
Task T323788

Impact module interactions: We find that the proportion of newcomers who interact with the old module (৬.১%) is significantly higher than for the new module (৫.০%): $χ^{2} = 17.5, d f = 1, p ≪ 0.001$ This difference showed up early on in the experiment, and we have examined the data more closely understand what is happening. One issue we identified early on was that not all interaction events were instrumented, which we subsequently resolved. Examining further, we find that many of those who get the old module click on links to the articles or the pageviews. In the new module, a graph of the pageviews is available, thus removing some of the need for visiting the pageview tool. As a result, we decided that no changes were needed.

Mentor module interactions: We find no significant difference in the proportion of newcomers who interact with the Mentor module. The proportion for newcomers who get the old module is ২.৪%, for those who get the new module it's ২.২%. A Chi-square test finds this difference not significant: $χ^{2} = 1.5, d f = 1, p = 0.219$

Mentor module questions: We do not see a substantial difference in the number of questions asked between the old module (২৬৯ edits) and the new module (২৮১ edits). The proportion of newcomers who asks their mentor a question is also the same for both groups, at ১.৫%.

Edits and revert rate: We do not see a substantial difference in the number of edits nor in the revert rate between the two groups measured on a per-user average basis. There are differences between the groups, but these are driven by some highly prolific editors, particularly on the mobile platform.

Levelling up

Leading indicators for the Levelling up experiment
Indicator	Expected result	Plan of action	Results
Levelling up post-edit dialog: interactions	No difference or increase	The percentage of users who click / tap on a Levelling up post-edit dialog should be similar or higher than the percentage of users who click / tap on the standard post-edit dialog. If there is a decrease, then we need to investigate what causes this difference.	Higher on mobile, no difference on desktop
Levelling up post-edit dialog: "Try a suggested edit" click through	>১০% click through to suggested edits	If the "try a suggested edit" dialog isn't resulting in more newcomers exploring suggested edits, then this notice is just extra noise for newcomers and we should investigate or consider removing the feature.	Significantly higher than ১০%
Levelling up post-edit dialog: "Increase your skill level" click through	>১০% click through to Try new task	If the "increase your skill level" isn't resulting in more newcomers trying more difficult tasks, then this notice is just extra noise for newcomers and we should investigate or consider removing the feature.	Significantly higher than ১০%
Levelling up notifications: "get started" click through	>৫% of users who view this notification click on it	We don't have a great baseline to compare this to, but if this number is too low we should investigate if there are technical issues or an issue with the language used.	More than ৫% on desktop, less than ৫% on mobile
Levelling up notifications: "keep going" click through	>৫% of users who view this notification click on it	We don't have a great baseline to compare this to, but if this number is too low we should investigate if there are technical issues or an issue with the language used.	More than ৫% on desktop, less than ৫% on mobile
Activation	No difference or increase	If we see a significant decrease in the treatment group, similar to what we discovered for the New Impact Module experiment, then we examine monitoring and event data to try to identify a cause of this difference.	Decrease

Tracked in Phabricator
Task T328055

Levelling up post-edit dialog interactions: We find a higher proportion of newcomers interacting with the post-edit dialog in the Levelling Up group (৯০.৮%) compared to the standard post-edit dialog (৮৬.৫%). This is largely driven by mobile where the Levelling Up interaction proportion (৮৮.০%) is a lot higher than the other group (৮১.৬%). The proportion is still higher for the Levelling Up group on desktop (৯৩.৬%) compared to the control (৯২.২%), but we regard it as "virtually identical" because the high proportion in the control group means there is little room for an increase.

Try a suggested edit click through rates: ২১.৯% of newcomers who see the "Try a suggested edit" post-edit dialogue chooses to click through, which is significantly higher than the threshold set. The proportion is higher on desktop (২৪.০%) than on mobile (১৯.৭%), but in neither case is there a reason for concern.

Increase your skill level click through rates: We find that ৭৩.১% of newcomers who see the "increase your skill level" dialog click through to see the new task, which is a lot higher than our expected threshold of less than ১০%. Proportions are high on both desktop (৭১.১%) and mobile (৭৭.৩%).

Get started click through rates: ৩.৮% of newcomers who get the "Get started" notification clicks through to the Homepage. Users who registered on desktop are more likely to click the notification (৫.৫%) than those on mobile (২.৫%). Because the threshold of ৫% is met, we are investigating further to understand this difference between desktop and mobile behaviour, particularly to understand if our ৫% threshold is reasonable.

Keep going click through rates: We find that ৯.৬% of users who get the "Keep going" notification clicks through to the Homepage. Similarly as we do for the "Get started" notifications, we find a much higher proportion on desktop (১৬.২%) compared to mobile (৪.৭%). Our investigations into differences in notification behaviour by platform will hopefully give us more insight into this difference.

Activation: We find a decrease in constructive article activation (making a non-reverted article edit within 24 hours of registration) of ২৭.০% compared to ২৭.৭%. As soon as we noticed this we opened T334411 to investigate the issue, with a focus on patterns in geography (countries and wikis) and technology (devices and browsers). We did not find clear patterns explaining the issue. The investigation of this decrease in activation will be investigated further: T337320.

Personalized praise

Leading indicators for the Personalized praise experiment
Indicator	Expected result	Plan of action	Results
Personalized praise notification click through	At least ১০% of Mentors who view a Personalised praise notification click on it	If this number is much lower than the click through on other notifications, then we should investigate if there are technical issues or an issue with the language used.	73% of Mentors who received a notification clicked on it
Personalized praise mentor dashboard module click through	At least ১০% of Mentors who view a Personalised praise suggestion on their Mentor dashboard end up clicking through to send praise	If this threshold is met then we should investigate if there are technical issues or an issue with how Mentors are interpreting this call to action.	27.5% of Mentors who view a Personalized praise list click through

Tracked in Phabricator
Task T337069

Data was gathered on 2023-06-13, from the four pilot wikis where the feature is deployed (Arabic Wikipedia, Bengali Wikipedia, Czech Wikipedia, and Spanish Wikipedia).

Personalized praise notification click through: Although this is still a relatively small sample, results seem healthy and show that Mentors are indeed receiving notifications and clicking through to view their praise-worthy mentees.

Personalized praise mentor dashboard module click through: Only 27.5% of Mentors are clicking through to a mentee's talk page, however it's to be expected that some of the mentees we are surfacing aren't deserving of praise. Based on this data and feedback from Mentors, the Growth team will pursue the following tasks to help improve this feature:

Add revert scorecard to Personalized praise module on Mentor dashboard (T337510)
Exclude blocked accounts from the Personalized praise suggestions (T338525)

Experiment Results

Many of the experiments that the Growth team runs will focus on the same set of key metrics (commonly referred to as KPIs), and this includes all of the Positive Reinforcement experiments. The key metrics are defined as follows:

Constructive activation is defined as a newcomer making their first edit within 24 hours of registration, and that edit not being reverted within 48 hours.
- Activation is similarly defined as constructive activation, but without the non-revert requirement.
Constructive retention is defined as a newcomer coming back on a different day in the two weeks after constructive activation and making another edit, with said edit also not being reverted within 48 hours.
- Retention is similarly defined as constructive retention, but without the non-revert requirements.
Constructive edit volume is the overall count of edits made in a user's first two weeks, with edits that were reverted within 48 hours removed.
Revert rate is the proportion of edits that were reverted within 48 hours out of all edits made. This is by definition 0% for users who made no edits, and we generally exclude these users from the analysis.

Impact module experiment results

The New Impact module reduced activation for mobile web newcomers

We initially found a significant decrease in constructive activation for newcomers who registered on mobile web and got the New Impact module.

There was no difference in activation for newcomers who registered on desktop.

This was quite surprising as the empty state for the old Impact module was nearly identical to the empty state of the new Impact module.

First-day activity correlates strongly with later activity, and as a result we also found a significant decrease in edit volume for mobile web users.

Again, there was no difference for desktop users.

We found no difference in retention rates and revert rates. While there are features in the New Impact module that focuses on staying active and making good contributions, such as the number of thanks received and the streak counter, we often do not see significant impacts on metrics unless there's a clear call to action or we are able to isolate a specific subgroup motivated by the feature.

As soon as we learned about the decrease in activation we started investigations into probable causes of this in T330614. Unfortunately we could not identify a specific reason and we also found that the issue was not replicated in another dataset.

We decided to add activation as a leading indicator to the Levelling Up experiment so that we could take action more quickly. When we noticed that the issue persisted, we started a new investigation in T334411 and created an "epic" task that connects all relevant subtasks: T342150. We restarted experiment data collection after making several small changes, and we now see that activation is identical between the experiment and control group, which is what we would expect.

Although we are pleased that we have received positive feedback from new editors regarding the new Impact module, we have found that the Impact module alone hasn't resulted in significant changes in newcomer retention, edit volume, or revert rates. Our next experiment will combine the new Impact module with the Leveling up features. We hope that this combination of Positive Reinforcement features will lead to substantial improvements in activation, retention, and edit volume. We will soon publish a detailed report that highlights the outcomes of this experiment.

Levelling up experiment results

For this experiment, we completed both an analysis of the overall effects across the whole newcomer population, and individually for each of the four components of the Levelling up features. These consist of the two notifications sent to newcomers 48 hours after registration, and two post-edit dialogues. The notifications are based on the number of suggested edits a newcomer might have done. If the newcomer has not made any suggested edits they get the "Get started" notification, and if they have made one to four suggested edits they get the "Keep going" notification. Newcomers who have made five or more suggested edits do not get any notifications.

The post-edit dialogues are shown after completed edits to articles based on certain criteria. If a newcomer has made three or eight article edits and not yet made any suggested edits, they get the "Try suggested edits" dialogue asking them if they want to try that feature. If a newcomer has completed five suggested edits of a specific task type, they get the "Try new task" dialogue suggesting a different type of task.

The Get Started notification increased editing within one week after receiving it.

Our overall analysis did not find any significant effects on the team's key metrics (described above), and so we focus instead on the individual components. For the "Get started" notification, we find that this is sent to the vast majority of newcomers as making suggested edits is fairly uncommon. In our dataset, more than 97% of newcomers got this notification. We find that the notification leads to a significant increase in newcomer activity in the week following the notifications being sent. Newcomers are more likely to return and make an edit, which also increases the average number of edits made during that week. We also find that this effect is lower for those who registered on mobile web, and reduced or negative for highly active newcomers. Based on this, we decided to introduce a threshold so that those who make ten or more edits will not receive the notification. (T342819)

The Keep Going notification increased editing within one week for desktop users.

When it comes to the "Keep going" notification, we again find a significant increase in newcomer activity in the week following notifications being sent for those who registered on the desktop platform. For users who registered on mobile web, we find that it does not increase their probability of returning to edit but does increase the average number of edits made.

For the "Try suggested edits" dialogue, our analysis finds that while it has a reasonably high click-through rate it does not lead to newcomers successfully completing suggested edits. In our leading indicators report above, the click-through rate was 21.9%, and in a dataset from late July 2023 we found the rate to be higher at 25.3%. Using event data, we find that few newcomers find a task they are interested in, and subsequently only a fraction of newcomers go through and complete an edit. We plan to make a few improvements to this "Try suggested edits" dialog to see if we can increase the percentage of editors who click through and go on to complete an edit. (T348205)

For the "Try new task" dialogue, which is shown to users who complete five suggested edits of a given task type, we find both high click-through rates and a reasonably high rate of completed edits. We reported a click-through rate of 73.1% in our leading indicators, and in our more recent dataset from late July 2023 the rate is 81.9%. Our analysis of subsequent edits shows that 33.3% of desktop users and 20.0% of mobile web users go through and complete a suggested edit of the new task type. One thing to keep in mind is that this dialogue is not shown to a large number of newcomers, and we therefore cannot draw conclusions about whether there are meaningful differences between platforms. What we can conclude, is that this dialogue is successful in introducing new task types. In order to show the dialogue to a larger number of newcomers, we decided to reduce the number of edits needed to see it from five to three. (T348814)

Personalized praise experiment results

For this experiment, we focused on the effect of praise on newcomer retention and productivity. Since praise is a response to editing activity, it means there will be some time period between registration and receiving a praise message. We therefore started with an analysis of the time between registration and a mentor clicking the "Send praise" button. In that analysis, we found that most newcomers get it within 30 days of registration. This led us to redefine the time period for retention and productivity to also use this 30-day period (instead of our default of 14 days).

The Personalized praise feature was deployed to the Arabic, Bangla, Czech, and Spanish Wikipedias in late May 2023. We analyzed the Spanish Wikipedia separately from the other three because on the Spanish Wikipedia 50% of newcomers are randomly assigned a mentor, which means the feature is part of a controlled experiment. All newcomers are assigned a mentor on the other three Wikipedias.

Using a Difference-in-Differences analysis approach, we compared a three-month period prior to deployment (January through March) with a similar period after deployment (June through August), and compared data from 2023 with data from 2022 and 2018. We use two comparison time periods as a robustness check since 2022 was affected by the COVID pandemic.

For the Arabic, Bangla, and Czech Wikipedia, we found no significant impact of Personalized praise on neither retention nor productivity. Digging further into this we found that usage of the feature was limited (we're not releasing specific counts in accordance with our data publication guidelines). In discussions with wiki ambassadors we learned that sending praise is a time-consuming process as the mentors need to check a mentee's edits, thus explaining why the feature isn't more widely used.

Personalized praise increased number of non-reverted edits made within 30 days of registration on Spanish Wikipedia.

When it comes to the Spanish Wikipedia, we found the feature has been more widely used. While we again found no significant impact on retention, we found a significant positive impact on newcomer productivity. This finding is encouraging since our preliminary analysis of mentorship found conflicting results of none or a negative impact.

As these results were not positive enough to justify the time investment from Mentors, we have decided to start conversations with our ambassadors and communities and consider further improvements before releasing the feature more widely. We will consider improvements related to reducing the amount of work needed by Mentors, potential design improvements, and improvements to how newcomers are selected to be displayed in the Personalized praise module.