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Assthree
Assthree ML & Crypto Intelligence
Machine learning model analysing cryptocurrency market data on multiple screens

Platform
Numbers
in Focus

Assthree tracks learning outcomes, quiz performance, and engagement patterns across every module. The figures below reflect real usage data gathered since the platform launched in 2020 - no projections, no rounded estimates.

38 400 quiz attempts completed across all ML modules
91.3% of learners finish a started quiz session
4.1 min average time spent per question set
6 800+ registered learners across all regions
Data updated quarterly - last refresh Q1 2025

What the engagement
data actually shows

These four figures capture the patterns that emerged over four years of learners working through cryptocurrency analysis and machine learning content. Each number points to a specific behaviour - not a target, just what happened.

Repeat sessions 3.7×

On average, learners return to the same quiz module 3.7 times before moving on. Repetition is built into how the content is consumed, not just how it is designed.

Score improvement +28 pts

The median score on a second attempt sits 28 points above the first. That gap narrows on the third attempt, which suggests the initial review does most of the corrective work.

Module depth 5 of 8

The average learner completes 5 of the 8 available topic modules. Drop-off concentrates at the neural network forecasting module - the most technically demanding section.

Mobile share 61%

More than six in ten quiz sessions start on a mobile device. This pushed the team to redesign tap targets and reduce scroll depth on every assessment screen.

Topic difficulty by average first-attempt score

Intro to ML concepts 78%
Data preprocessing for crypto feeds 64%
Classification models 71%
Time-series pattern recognition 55%
Neural network forecasting 41%
Model evaluation and validation 67%
Learner reviewing machine learning quiz results on a laptop screen Live performance dashboard
Portrait of Pieter Naudé

Pieter Naudé

Data analyst, Cape Town

"The instant feedback after each question is what made the difference for me. Seeing exactly where my reasoning broke down helped me correct it before moving to the next topic."

Portrait of Amahle Dlamini

Amahle Dlamini

Finance student, Durban

"I went through the time-series module four times before the score felt honest. The platform does not pretend the material is easy - and that is what kept me coming back."