Predictor.com uses data from 15 online courses with 500,000 students to build a predictive model using quadratic discriminant analysis that can determine if a student will complete a course with an F1 score of 86%, precision of 84%, and recall of 88%. The model found that factors like time spent, number of days worked, time lag between activities, gender, education level, country, and age were the most relevant predictors of student success. The site then uses this model to provide course recommendations personalized for each user based on their characteristics and the transition patterns of previous students.

1. Predictor.com
E-learning performance
Jesus Martinez-Manso

2. Enrolling in an online course:
Am I too late?!
Is it enough if I!
work 3 days per week?

3. !
15 courses - 500k students
(only 5% get certification)
Demographics!
(age, country, etc)
Timing!
(course launch, student
drop out, etc)
Activity!
(interactions, certification)
data

4. Predictive Model
Quadratic Discriminant Analysis
F1 score: 86%
Precision: 84% Recall: 88%

5. Feature relevance
Tim
e
span
#
days
w
orked
Tim
e
lag
G
ender
Ed.level
C
ountry
Age
Analytics
Engagement,!
time
demographics, !
education
>

6. This is me!
Galaxies Dark matter
Jesús

7. Ri
k = fi
k pk
Recommendation from
course i to course k for given user
fraction of course i
that went to k
prob. of user
certifying course k