1.
Learning Analytics in
MOOCs: Can Data Improve
Students Retention and
Learning?
Mohammad Khalil and Martin Ebner
Educational Technology

2.
Where is our research Department located...
our department
Educational Technology
TUGraz, Austria
OER
MOOCs
Learning AnalyticsPersonalized Learning
Big Data
Adaptive Learning
Educational Data Mining
Technology Enhanced Learning
Lifelong Learning

3.
1.MOOCs
The new generation of Distance Learning

4.
MOOCs ?
Image from: http://cdn2.hubspot.net/hub/685689/file-3231367982-jpg/blog-files/massiveopenonline_620x400.jpg?t=1462361738877

5.
To Summarize:
You can attend courses
from prestigious universities
for free

6.
To Summarize:
You can attend courses
from prestigious universities
for free

7.
platform...

8.
Issues of
MOOCs
Dropout
Motivation and
Engagement
Interactions
Didactical
design
Quiz
assessment
Seriousness

9.
Issues of
MOOCs
Dropout
Motivation and
Engagement
Interactions
Didactical
design
Quiz
assessment
Seriousness

10.
Khalil, M., & Ebner, M. (2016). What Massive Open Online Course (MOOC) Stakeholders Can Learn from Learning Analytics?. Learning, Design,
and Technology. Springer International Publishing. (pp. 1-30).

11.
Can we Control or
decrease Dropout?

12.
Can we Control or
decrease Dropout?
Yes! Through
Learning Analytics

13.
2. Learning Analytics
It is a data analytics...but in educational systems

14.
2. Learning Analytics
It is a data analytics...but in educational systems
like...MOOCs

15.
“Collecting traces that learners leave behind and
using those traces to improve learning.
-Erik Duval
a Simple Definition

16.
Data Collection
Quizzes
Forums Reading Logins
Forums Writing

17.
Behavior Comparison between Completed and Dropout students

18.
Assigning Weights
Quizzes
Forums Reading Logins
Forums Writing
W
1
W3
W2 W4

19.
Assigning Weights
Quizzes
Forums Reading Logins
Forums Writing
W
1
W3
W2 W4
W1 > W2 > W3 > W4

20.
Assigning Weights
Quizzes
Forums Reading Logins
Forums Writing
W
1
W3
W2 W4
W1 > W2 > W3 > W4
Success Rate (SR) = W1.Readings +
W2.Quiz_Attempts + W3.Login_Frequency +
W4.Writings

21.
Prediction based on
Indicators Weight

22.
Prediction based on
Indicators Weight
● Low numbers
indicate dropout
● High numbers
indicate completion

23.
Awareness
Or
Feedback
What can we do to enhance retention and learning?

24.
Student get notification:
Gamification Element
like water bottle

25.
EdMedia Vancouver,
Graz University of Technology
EDUCATIONAL TECHNOLOGY
Graz University of Technology
Martin Ebner
http://elearning.tugraz.at
martin.ebner@tugraz.at
http://elearningblog.tugraz.at
m ebn er
Slides available at:
This work is licensed under a
Creative Commons Attribution
4.0 International License.