MONGO DB
MONGO DB
IMPROVING THE LEARNING PLATFORM FOR NEW MONGODB USERS
Primary Research
How might we help new MongoDB users efficiently navigate all learning resources to optimize the learning process based on their own learning preferences?
The Challenge
MongoDB is looking to enhance their learning resources for new developers using their software. We were tasked to help developers achieve proficiency faster with MongoDB's products by exploring MongoDB's own existing resources as well as secondary resources including artificial intelligence.
Participant Criteria: 25 to 35 year old software engineers with MongoDB or No SQL experience
From the interviews, we learned that users had different preferences and learning styles, but they all struggled navigating the different tools at their disposal. The lack of a centralized flow of learning led them to utilize second hand resources more often and spend time jumping around different resources.
Problem Space
26,707
Companies began using NoSQL Databases in 2023
Age: 80% of new Mongo DB users are aged 22-35 years old
Experience: 80% of new Mongo DB users have professional software engineering experience
With a growing number of companies moving towards NoSQL Databases, many individuals will be learning MongoDB from scratch in the coming years. With many of these individuals preferring different methods of learning and experience levels, its important that we optimize our eduction platform for our userbase.
Discover user preferences and common problems encountered as it pertains to learning to use MongoDB
Gain a hollistic understanding of users relationship with online and independent learning.
After conducting user interviews, we made a map to organize our findings. We focused on the most insightful responses from important questions that gave us strong insights into user's experiences. By comparing and contrasting answers we were able to find common themes and important differences to attend to.
Assess the users preferred secondary tools for learning the MongoDB software including any AI resources
MongoDB Pain Points
Learning Styles
Attention/Experience
Neel Patel
Kaman Tan
Jada Olivia
Ying Lee
Christine Sawyer
UX Researcher UI Designer
UX Researcher UI Designer
UX Researcher
Data Scientist
Software Engineer
Importance of Work
Research Goals
Interview Profiles
Analysis
User Persona
Task Analysis
Before we made any concrete design changes, we mapped out the primary task flow users would take to initiate learning the MongoDB software. We found any minor issues related to navigation of the learning resources or basic usability heuristics to change in addition to our primary solution.
Usability Error 1: Deisgn overlaps in Sign Up Screen makes words hard to read. Text can be improved to reflect the improved learning platform.
Usability Error 2: Lack of splash screen makes entrance into MongoDB confusing. Moving from dark screen to noisy light screen makes it jarring
Usability Error 3: Confusing organization of resources in tabs impedes learning. User tab should hold account information and learning in Get Help.
Current User Task Flow
Sign Up Screen
MongoDB Home
Help Bubble
Chat
Learning Resource
Resource Tab
Developer Center
Our Solution
Where would be the ideal place to incorporate a centralized learning station?
Centralized Learning Station: Learning resources should be easily accessed from one spot for easy navigation
AI / ChatGPT Integration: Many users use AI as a learning tool and we want to keep users learning within the MongoDB software so they do not spend valuable time searching for second hand tools
Modify learning experience based on user's learning preferences and experience level
On the landing page and throughout usage of the software, MongoDB has a chat feature. Users intuitively will use this any time they need help. Here, we will make our software and design changes to meet our goals
Final Plan
To make sure we addressed all three of our goals, we decided we would create a second tab in the chatbot for users to use ChatGPT AI. Our software engineer planned to write a code that would change the responses given by and direct them to learning resources that match their preferences stated in the sign up process.
Features
Implementation
Final Plan
The Finished Product
