Scientific approach
to education
We use modern research in psychology and neuropsychology, as well as conduct our own studies to create efficient motivating mechanics as well as precise and up-to-date studying materials.
Meeting our users' expectations
The SETURON research laboratory studies the needs of our target audience and predicts the demand for our educational programs so we can meet it. We are also constantly tracking how effective are our educational materials, making them easier to understand and use.
Studying the market
To meet the market demand, we analyze it using two major sources:
Analyzing efficiency
Regular analysis of users' studying process helps us reveal the weak parts of our courses, understand where and why users lose interest, what materials they understand better, and areas that need improving. Quantitative data and interviews allow us to constantly modify our content.
Internal platform data - statistic data on the areas which are most popular and allow us to retain the subscribers.
External sources - analysis of search queries, market research, bestsellers sales rates. This data helps us to attract new subscribers.
Developing educational environment
We perfected the process of developing our educational platform. It allows us not only to improve existing mechanics, but also to carefully introduce new ones, and make studying more fun without any harm to existing behavior patterns.
We collect quantitative indicators in different parts of our service to monitor its status and detect problems.
Keeping track of key metrics
Staying in touch with our users
We regularly conduct interviews and content research to take into account the preferences of our users.
Collecting feedback
Feedback from our users allows us to determine hidden and hard to notice difficulties they are facing and correct the situation.
Introducing new ideas
Analyzing data and receiving feedback allows us to be proactive and to introduce new ideas for our services.
Vetting new implementations
Before we introduce new tools and ideas, we test them to make sure the quality of the service provided is high.
Constantly improving the services
Once we made sure new ideas improve users' experience we do not hesitate to implement them into our services.
We study motivation of our users
Based on cognitive-behavioral approach we identified several user segments and for each one of them developed unique motivational mechanics to turn their studying process into a game and keep it fun.
user segments
We have determined different segments of our target audience by studying their behavioral patterns. We adjust our product for all this user types.
motivational mechanics
For each user segment we develop unique motivational mechanics which make their learning process more fun.
Community member
Strives to join a community of people with similar interests. His motivation is based on searching for new contacts, and the desire to share ideas and collaborate.
He will stay to be a part of the community.
Puzzle solver
Tries to solve difficult tasks, just because he likes it. His motivation is based on his passion for finding solutions to difficult problems.
He will stay, because we will never run out of interesting challenges for him.
White cloak
Strives for knowledge, because he wants to share it with others. His motivation to study is based on his desire for public good.
He will stay, because he cannot leave, when he is already making the world a better place (and when it is clearly stated in his personal account).
Has a need to possess most recent knowledge in his area of expertise. His motivation is based on the desire to always be one step ahead.
He will stay not to lose a valuable information source.
Learning professional
Wants to broaden his horizons. He seeks a way to develop what he already knows.
He will stay, because the amount of courses is constantly growing and updating.
Adapting our platform for every student
People are different, and they all need personalized approach to studying. We developed tools and interface that provide deep personalization of all user activities.
CV based on your results
A system creates an interactive resume for every student. It includes user assessments on different metrics: time it takes to cover different materials, amount of mistakes made and many other things. For every user there is a chart of competences which allows to see how he has developed his skills.
Personal recommendations
A system of algorithms offers different courses to different students. Recommendations are based on behavior pattern and personal interests.
Course adaptation
The platform adapts to every user from the moment he starts completing the course. The learning path depends on how many mistakes a user has made along the way.