1. Data analytics to support
awareness and recommendation
Katrien Verbert
WISE research group
Department of Computer Science
katrien.verbert@vub.ac.be 27/03/14

2. Data analytics
Src: Steve Schoettler

3. Healthcare Learning analytics
Applications

4. Overview research topics
4

5. Overview research topics
5

6. Student Activity Meter (SAM)
6
Govaerts, S., Verbert, K., Duval, E., & Pardo, A. (2012, May). The student activity meter for
awareness and self-reflection. In CHI'12 EA (pp. 869-884). ACM.

7. http://bit.ly/I7hfbe

8. Design Based Research Methodology
¤ Rapid prototyping
¤ Evaluate Ideas in short iteration cycles of Design,
Implementation & Evaluation
¤ Focus on Usefulness & Usability
¤ Think-aloud evaluations, SUS (System Usability Scale)
surveys, usability lab, ...

9. demographics
tool
deployed
tracking tools
data tracked
#cgiar
19 teachers
SAM
LMS
resource use,
communication,
time spent
#lak11
12 participants
SAM
LMS
resource use,
communication,
time spent
#uc3m
11 teachers
SAM
Virtual machine
resource use,
programming errors,
debugging, time
spent; artefacts
produced
#thesis11
13 students
Step-Up!
Twitter, Tinyarm,
blogs
resource use,
artefacts produced
#thesis11-
sup
5 teachers
Step-Up!
Twitter, Tinyarm,
blogs
resource use,
artefacts produced
#peno3
10 students
Step-Up!
Toggl
Time spent, resource
and application use
#chikul
30 students
Step-Up!
Toggl, twitter,
blogs
twitter, blogs, time
spent, resource use

10. Evaluation results
10
Verbert, K., Govaerts, S., Duval, E., Santos, J. L., Van Assche, F., Parra,
G., & Klerkx, J. Learning dashboards: an overview and future research
opportunities. Personal and Ubiquitous Computing, 1-16.
http://link.springer.com/article/10.1007/s00779-013-0751-2

11. Overview research topics
11

12. Recommender systems
12

13. User-based CF
A
B
C

14. A
B
C
Item-based CF

15. similarity measures
¤ Cosine similarity
¤ Pearson correlation
¤ Tanimoto or extended Jaccard coefficient

16. similarity measures
16
MAE of item-based collaborative filtering based on different
similarity metrics

17. algorithms
MAE of user-based, item-based and slope-one collaborative filtering

18. data dimensions

21. Challenges
¤ context acquisition
¤ standardized representation of contextual data
¤ evaluation
¤ user interfaces

22. Overview research topics
22

23. Problem statement
¤ Complexity prevents users from comprehending results
¤ Trust issues when recommendations fail
¤ Aggravated with contextual recommendation
¤ The black box nature of RS prevents users from providing feedback
¤ Algorithms typically hard-wired in the system code
¤ generate a list of top-N recommendations
¤ little research has been done to study more flexible approaches
23

24. Conference Navigator
24

25. Interrelations agents – users - tags
25

26. Interrelations agents – users
26

27. Interrelations agents - tags
27

28. TalkExplorer
28

29. effectiveness
How frequently a specific
combination type produced
a display that was used to
bookmark at least one
interesting item
Dimensions of relevance are
not equal
The more aspects of
relevance are used, the
more effective it is
Especially effective are
fusions across relevance
dimensions
29

30. Summary results
30

31. 31
information visualisation - information retrieval - information (data) mining

32. 32http://www.youtube.com/watch?v=9LwSx1V6Yxk

33. Combining information mining and visualization
Core objectives:
• make mining results comprehensible for users
• enable users to steer the information mining process

34. Thank you!
Questions?
34
katrien.verbert@vub.ac.be
@katrien_v