HOSHEN - User Journey / Feedbacks Data Dashboard
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HOSHEN - User Journey / Feedbacks Data Dashboard

🏳️‍🌈 LGBTQ+ Organization
votes number
Technical requirements
Automation
Backend
BI
Scraper
Database
Education Nerd
Frontend
NLP
Analyst
Fullstack
projectManager
tagline
Hoshen, Feedback
Voters
Team stack
Tech level ( 1- low)
2
Mentor
Shirley
Priority 1- low
2
Status
Approved

Context

HOSHEN - EDUCATION & CHANGE For the past 18 years, Hoshen has been advancing tolerance and understanding toward the LGBTQI community all over Israel. The central activity of Hoshen is the running of workshops and talks, in various forums in Israel: The education system, to students from grade 7 through to 12 and to educators from early learning until high school. The main method of Hoshen to promote familiarity and understanding of the LGBTQI community is called “The Personal Story Workshop”. In this workshop, members of the organization share with the crowd their own journeys regarding their sexual and gender identity, their struggles with them and their path to acceptance and happiness. The workshop contains other elements that are used depending on the age and needs of the audience. The workshops for educators, for example, include a talk about different sexual and gender identities and give professional tools to create a safe educational space for LGBTQI youth and their families.

What is the problem and its effects?

All through the process of scheduling a workshop in a school Hoshen is collecting a lot of data - asking the school counselor what is the atmosphere in the school / classes? what are the teaching staff’s knowledge and opinions? are there any teachers or kids that are out? etc
But at the end of the day - nothing is being done with that data and it piles up to a big chunk of lost feedback that the organization could learn from for next time in order to improve the attitude or change the product they bring to school. Some schools don’t even invite the organization no more and they don’t even know why.

Current state

  • There is no statistic / automatic diagnosis of the collected data.
  • All the target audiences are asked to fill in the same feedback form after the workshop is done. (see form here)
  • The data is only based on feedback forms.

Proposal for solution

  • Create a dashboard (preferably that can work with Monday) to gather the data and analyze it - What is the general atmosphere at the school? At the specific classes? How’re the kids feelin’? Have there been any LGBTQIPhobia events? What is your safety level in school? Are there any teachers or kids that are out? LGBTQI families? etc
  • Define and create customized feedback forms for each different target audience (Counselor, Teacher, Kid) in order to get more accurate data and forms filled
  • Gather anonymous questions before the workshops in order to address them beforehand
  • Bonus: Create an internet scraper for more relevant data - LGBTQI bullying in the specific school, hate crimes in the city, level of violence, “Madad Ha’Gaava” by The Aguda etc
 
Constraints
  • I know that writing new forms is a pain in the ass, especially doing the micro-copy
  • Internet scraper?? For like the whole internet?? she’s nuts!
 
OUT OF SCOPE
In the future, it could be Uber-cool to take all this data and do like “Madad Ha’Gaava” but with schools instead of city councils

Metrics for success

  • Did we create a dashboard? It that can work with Monday?
  • Did we succeed to write customized new feedback forms? How Many?
  • Bonus: Did we create an internet scraper?
 

Technical requirements

Dashboard (web interface, preferably that can work with Monday’s DB):
  • Should be secured and grant access only to Hoshen employees
  • Displays the cities / schools that we are tracking
  • Ability to add more / remove schools profiles to track
→ Front end (web interface):
  • Infographic of good feedback mount over time (2y)
→ Server end:
  • Bonus: Scrape local news websites, local FB groups via API key for (any of the above will be great):
    • Facebook
    • Twitter
    • school, hate crimes in the city, level of violence, Madad Ha’Gaava
  • Analyze each target audience’s feedback content and use AI to determine whether the content is good \ bad
  • [Feedback is stored in Monday’s DB]
→ A database (or something that can use Monday’s DB)