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Indicators such as recency, usage frequency, retention, and platform access are fundamental in e-learning and platform-based learning contexts. The following dashboard presents an analysis of recency evolution, helping to understand how and how often students interact with the platform over time.
The goal is to answer the following questions: do students actually use the platform? how frequently do they engage with it?
Context
Context
In a B2B context, schools contract an EdTech company that provides an online learning platform. Students are expected to access the platform on a weekly or biweekly basis to progress through their activities.
Each week, student logins are analyzed to determine whether enrolled users accessed the platform during that period. If not, their last login date is recorded and classified into recency categories such as "This week", "Last 15 days", "Last month", or "Never logged in yet". This distribution can be observed in a snapshot chart for a given week.
By tracking these indicators over time, a bar is built for each week, allowing their evolution to be analyzed. The chart presents an analysis based on simulated data for the 2025 school year (Southern Hemisphere calendar, March–December).
Key Insights
Key Insights
Weeks 10–11: only a small number of schools begin program implementation during the initial weeks.
Weeks 12–16: steady growth in student enrollment as more institutions join.
Weeks 12–16: not all early users maintain consistent engagement, leading to the formation of categories such as "last 15 days" and "last month".
From week 16 onward: all students are enrolled (26,705), although a portion has never accessed the platform ("never yet").
Weeks 27–31: a sharp drop in activity due to winter holidays.
Weeks 32–34: gradual resumption of platform usage.
Weeks 34–47: sustained and progressive growth in activity.
Weeks 47–50: sudden increase in engagement; many students log in for the first time, while others significantly increase their frequency.
Week 51: overall decline as the school year ends.
Conclusions
Conclusions
Not all schools start implementation at the beginning of the academic year.
A significant share of students never access the platform.
Winter holidays lead to a sharp decrease in activity.
Toward the end of the year, previously inactive or delayed students significantly increase usage to meet course goals.
Approximately one quarter of students log in on a weekly basis.
Around half of students access the platform at least once every two weeks during regular weeks.
The remaining students show sporadic usage.
Further analyisis
Further analyisis
It would be valuable to correlate these indicators with student progress and achieved learning objectives. This raises further questions:
does usage frequency have a measurable impact on progress?
is the product appropriately designed given its expected weekly workload?
You can find more about this in Case Study 03.
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