=Profile=
**Name** : Anne
**Email** : annemachinda@gmail.com
**Github** : https://github.com/Lukong123
**Location** : Cameroon (UTC +1)
**Typical working hours** : Between 8 am and 4 pm UTC +1
=Synopsis=
The Multilingual Wikipedia Editor Survey internship project aims to gain a comprehensive understanding of individuals who contribute to Wikipedia by editing and using translation across multiple language versions.
The goal of this project is to get a better understanding of those who use translation and editing across multiple language versions of Wikipedia. Understanding their motivations, goals, and challenges in a way that informs product development to support their efforts.
**Mentor** :
=Timeline=
| **Period** | **Task** |
|------------ |-----------|
|December 4 to December 15|**Community bonding period**. Familiarization with the community. Get more acquainted with the project objectives, goals, and requirements.Understanding the various groups we will be working on and any new tool. Conduct background research on survey methodologies, best practices, and reviewing relevant literature. Collaborate with mentor and or necessary groups to define survey objectives, identify target audience, and draft survey questions. Seek feedback on the survey design from mentor and or necessary group. Incorporate feedback and make necessary revisions to the survey design.
Submit bi-weekly report |
|December 18 to December 29|Develop a plan for collecting survey responses, considering factors such as target audience reach, platform selection, ethical considerations etc. Conduct a pilot survey to evaluate the survey's clarity, effectiveness, and identify any potential improvements. Launch the survey and begin collecting responses from the target audience. Regularly monitoring survey responses, ensuring data integrity and addressing any issues that arise. Submit bi-weekly report|.|
|January 2 to January 12| Learn from the feedback from the first months.Creating a comprehensive plan for analyzing the collected survey data, including the selection of appropriate statistical methods and data visualization techniques. Clean and organize the survey data where needed to ensure accuracy and consistency. Perform quantitative and quantitative analysis on the survey data(where applicable) to derive meaningful insights and identify trends. Document the analysis process, Studies and research for the next survey questionsmethodologies used, releasing the questions.|and come up with a comprehensive report. Submit Biweekly report|
|January 15 to January 26 26|Creating a comprehensive plan for analyzing the collected survey data,Interpret the analyzed data to derive key insights and meaningful conclusions. Writing the research report, ensuring clarity, coherence, and proper organization of findings. including the selection of appropriCreate statistical methods and data visualizvisual representation techniques.ns, Clean and organize the survey data where needed to ensure accuracy and consistency.such as charts, Perform quantitative and quantitative analysis on the survey data(where applicable) to derive meaningful insights and identify trends.graphs, Document the analysis processand diagrams, methodologies used,to enhance the presentation of survey results. and coming up with a comprehensiveSeek feedback from the mentor and/or group members on the initial draft of the research report. Submit bi-weekly report.||
|January 29|**Phase I evaluation**|
|January 30 to February 9|Interpret the analyzed data to derive key insights and meaningful conclusions. Writing the research report, ensuring clarity, coherence, and proper organization of findings8|Incorporate feedback received and refine the research report accordingly. CreGenerate visual representations,actionable recommendations based on the survey insights and findings. such as chartsComplete the final version of the research report and ensure all necessary sections, graphsvisuals, and diagrams,references are included. to enhance the presentation of survey results. Seek feedback from the mentor and/or group members on the initial draft of the research report.Submit bi-weekly report.|
|February 9|**Phase II evaluation**|
|February 12 to February 22|Incorporate feedback received and refine the research report accordingly. Generate actionable recommendations based on the survey insights and findings. Complete the final version of the research report and ensure all necessary sections, visuals, and references are included. Submit bi-weekly report.|
|February 23|**Phase II evaluation**|
|February 26 to March 1 |Create a presentation summarizing the research findings, insights, and recommendations.
Deliver the presentation to the mentor and/or other groups, highlighting the key findings and recommendations.
Reflect on the internship experience, document lessons learned, and prepare any necessary project documentation and final internship report. Obtain feedback from the mentor and the community|
|--------------|-------------------|
=Deliverables=
- Comprehensive analysis of survey responses: The end product of the project will involve conducting a thorough analysis of the survey data collected from multilingual Wikipedia editors. This analysis will include examining and interpreting the responses to gain insights into the motivations, goals, and challenges faced by these editors.
- Research report: A detailed research report will be compiled, summarizing the findings from the survey analysis. The report will provide an overview of the key findings, trends, and themes derived from the data.
- Insights for product development: The research findings will help identify areas where product interventions can be implemented to support the work of multilingual editors.
**Phase I evaluation**
- Onboarding UX for the automatic feedback feature.
- Feedback based on article grades (ORES predicted).
- Data based on the feedback of the previous version and the subsequent edit.( To identify target users, articles &c.)Cleaning Data
- Improving the feedback messages with the help of the feedback respones with ORES features.- Interpreting and analyzing data
- (Optional) Including in the feedback messages how the edit has improved from the previously given feedback
**Phase II evaluation**
- Improve feedback based on the data collected on- Create a presentation summarizing the effectiveness of the feedback messages.
research findings, insights, - Document useful features that can be added to ORESand recommendations.
- (Op- Deliver the presentational) UI of thumbs up/down to mark the feedback helpful to the mentor and/or other groups, highlighting the key findings and recommendations.
- Write documentation and update guides.
Document Internship process. Reflect on the internship experience, document lessons learned, and prepare any necessary project documentation and final internship report.
**Final evaluation**
=Participation=
- Work on a separate branch on git and uploading code to the forked repo almost on a daily basis. Creating pull requests as and when a complete feature is done.
- Online on IRC in my working hours ( 2pm to 3 am UTC +5:30)
- Communication on tasks will be through commenting on subtasks to the project created on Phabricator.
- Weekly reports will be published in my meta wiki [[ https://meta.wikimedia.org/wiki/User:KeerthanaS | user page ]]
- Publishing on my blog the summary of a task at the end of a task period as above in the timeline( https://developerbytes.wordpress.com/)
=About me=
I am currently a Masters Student at the University of Bamenda in the field of Computer Engineering. I am tech motivated and an open source lover. I am opened to the field of Software and Data Engineering.
The aspect of analysing data is a motivating part of this project for me. Moreover the fact that this analysed data will be used to help product development to better the experiences of the numerous multilingual editor is also a very encouraging factor for me, because this will go a long way to help the population.
=Past Experience=
I have acquired experience collaborating within teams like GDSC(Google Developer Student Club) co-lead and co team member, Technovation Girls Mentor etc.
Being in school, I have being opportuned to have and validate courses on Data Analysis, Data Analysis and Mining, Machine Learning which provided me with relevant knowledge understand and analyse data.
I have examined community surveys to support the project demonstration for both international competition and personal project.
**Microtasks completed**
- https://docs.google.com/document/d/12viWQSJ4H8kOkGnTthq1z9vpK2xhLVNM-OfPDp2vl5Q/edit?usp=sharing