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Data analytics to support awareness and recommendation
Katrien Verbert WISE research group Department of Computer Science [email protected] 27/03/14
Data analytics
Src: Steve Schoettler
Healthcare Learning analytics
Applications
Overview research topics
4
Overview research topics
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Student Activity Meter (SAM)
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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.
http://bit.ly/I7hfbe
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, ...
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
Evaluation results
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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
Overview research topics
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Recommender systems
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User-based CF
A
B
C
A
B
C
Item-based CF
similarity measures
¤ Cosine similarity
¤ Pearson correlation
¤ Tanimoto or extended Jaccard coefficient
similarity measures
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MAE of item-based collaborative filtering based on different similarity metrics
algorithms
MAE of user-based, item-based and slope-one collaborative filtering
data dimensions
Challenges
¤ context acquisition
¤ standardized representation of contextual data
¤ evaluation
¤ user interfaces
Overview research topics
22
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
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Conference Navigator
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Interrelations agents – users - tags
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Interrelations agents – users
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Interrelations agents - tags
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TalkExplorer
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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
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Summary results
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information visualisation - information retrieval - information (data) mining
32 http://www.youtube.com/watch?v=9LwSx1V6Yxk
Combining information mining and visualization
Core objectives: • make mining results comprehensible for users • enable users to steer the information mining process
