Data analytics to support awareness and recommendation

Katrien Verbert WISE research group Department of Computer Science katrien.verbert@vub.ac.be 27/03/14

Data analytics

Src: Steve Schoettler

Healthcare Learning analytics

Applications

Overview research topics

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Overview research topics

5

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

Thank you!

Questions?

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katrien.verbert@vub.ac.be

@katrien_v