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Sven Charleer
Designing and Evaluating 

Student-facing Learning Dashboards

Lessons Learnt
Public PhD Defence
July 6, 2017
Augment

Lab
2
https://www.apple.com2
3
3
LEARNING ANALYTICS
4
“The measurement, collection, analysis, and reporting of data about learners
and their contexts, for purpose of understanding and optimising learning and
the environments in which it occurs”
J. L. Santos. Learning Analytics and Learning Dashboards: a Human- Computer Interaction Perspective. PhD dissertation, KU Leuven, 2015.
G. Siemens. “Learning analytics: envisioning a research discipline and a domain of practice”. Proceedings of the 2nd
International Conference on Learning Analytics and Knowledge . ACM. 2012, pp. 4–8.
session
course
degree
year
Microlevel
intro
AUTOMATE AUGMENT
Engelbart, D. C. (2001). Augmenting human intellect: a
conceptual framework (1962). PACKER, Randall and
JORDAN, Ken. Multimedia. From Wagner to Virtual Reality.
New York: WW Norton & Company, 64-90.
VS
5
Arnold, Kimberly E., and Matthew D. Pistilli. Course Signals at Purdue:
Using learning analytics to increase student success. Proceedings of
the 2nd International Conference on Learning Analytics and Knowledge.
ACM, 2012.
LEARNING DASHBOARDS
6
“A Learning Dashboard is a single display that aggregates different indicators
about learner(s), learning process(es) and/or learning context(s) into one or
multiple visualisations.”
B. A. Schwendimann, M. J. Rodríguez-Triana, A. Vozniuk, L. P. Prieto, M. S. Boroujeni, A. Holzer, D. Gillet, and P. Dillenbourg. Understanding learning at a glance: An overview of learning
dashboard studies. In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge, pages 532–533. ACM, 2016.
K. Verbert, E. Duval, J. Klerkx, S. Govaerts, and J. L. Santos. Learning Analytics Dashboard Applications. American Behavioral Scientist, 57(10):1500–1509, 2013.
Perceived benefits
Design guidelines
intro
awareness
(self) reflection
sense making
impact
data
questions
answers
behavior
7
DESIGN-BASED RESEARCHintro
External Input through collaboration with participants:
Teachers
Students
Experts
8
DESIGN-BASED RESEARCHintro
Multiple dependent variables
9
DESIGN-BASED RESEARCHintro
Rapid prototyping
10
CONTEXTintro
ch2
ch3
ch4
Santos, J.L., Verbert, K., Govaerts, S., Duval, E.: Addressing learner issues with stepup!: an evaluation. In:
Proceedings of the Third International Conference on Learning Analytics and Knowledge, ACM (2013) 14–22
CREATING EFFECTIVE LEARNING DASHBOARDSch2
11
PATH OF EFFORT
OUTCOMES
INFLUENCES
12 https://www.flickr.com/photos/lockechrisj/
LEARNER PATHSch2
RQ1: How should we visualise learner data to support students to
explore the path from effort to outcomes?
RQ2: How can we promote students, inside and outside the
classroom, to actively explore this effort to outcomes path?
CREATING EFFECTIVE LEARNING DASHBOARDSch2
13
14
EVALUATIONSch2
TwitterBlogs
15
EVALUATIONSch2
Inquiry-Based Learning16
EVALUATIONSch2
Abstract the LA data
Provide access to the artefacts
Augment the abstracted data
Provide access to teacher and peer feedback
17
RESULTS
RQ1: What are relevant learning traces, and how should we visualise these
data to support students to explore the path from effort to outcomes?
ch2
18
RESULTS
RQ2: How can we promote students, inside and outside the classroom, to
actively explore this effort to outcomes path?
Visualise the learner path
Integrate LA into the workflow
Facilitate collaborative exploration of the LA data
ch2
19
ABSTRACT / AUGMENTch2
20
INTEGRATE / AUGMENTch2
21
FEEDBACK / ARTEFACTSch2
21
22
VISUALISE LEARNER PATHch2
23
Geoff’s hypothesis
Geoff’s data collection
Geoff’s conclusion
Angela’s data collection
Angela activities Geoff’s activities
VISUALISE LEARNER PATHch2
24
Geoff’s hypothesis
Geoff’s data collection
Geoff’s conclusion
Angela’s data collection
Angela activities Geoff’s activities
VISUALISE LEARNER PATHch2
25
Geoff’s hypothesis
Geoff’s data collection
Geoff’s conclusion
Angela’s data collection
Angela activities Geoff’s activities
VISUALISE LEARNER PATHch2
26
Geoff’s hypothesis
Geoff’s data collection
Geoff’s conclusion
Angela’s data collection
Angela activities Geoff’s activities
VISUALISE LEARNER PATHch2
27
Geoff’s hypothesis
Geoff’s data collection
Geoff’s conclusion
Angela’s data collection
Angela activities Geoff’s activities
VISUALISE LEARNER PATHch2
28
Geoff’s hypothesis
Geoff’s data collection
Geoff’s conclusion
Angela’s data collection
Angela activities Geoff’s activities
VISUALISE LEARNER PATHch2
29
Geoff’s hypothesis
Geoff’s data collection
Geoff’s conclusion
Angela’s data collection
Angela activities Geoff’s activities
VISUALISE LEARNER PATHch2
30
Geoff’s hypothesis
Geoff’s data collection
Geoff’s conclusion
Angela’s data collection
Angela activities Geoff’s activities
VISUALISE LEARNER PATHch2
31
COLLABORATIONch2
32
CONTRIBUTIONSch2
Guidelines published at European Conference on Technology Enhanced Learning

(25% acceptance)

S. Charleer, J. Klerkx, E. Duval, T. De Laet, and K. Verbert. Creating effective learning analytics dashboards: Lessons learnt.
Adaptive and Adaptable Learning: 11th European Conference on Technology Enhanced Learning, EC-TEL 2016, Lyon,
France, September 13-16, 2016, Proceedings, pages 42–56, Cham, 2016. Springer International Publishing
14 papers

collaboration with

Stanford University - University of Technology Sidney - University of the Basque
Country - Murdoch University Perth - Curtin University Perth
Program Committees

EC-TEL, LAK, Cross-LAK, ARTEL, EC-TEL Doctoral Consortium
European Commission for the weSPOT project
33
CONTRIBUTIONSch2
Austria, Bulgaria, Netherlands, Slovenia, United Kingdom
33
34
CONTRIBUTIONSch2
34
BALANCED DISCUSSION IN THE CLASSROOMch3
over- and under-participation
35
BALANCED DISCUSSION IN THE CLASSROOMch3
RQ3: What are the design challenges for ambient Learning
Dashboards to promote balanced group participation in
classrooms, and how can they be met?
RQ4: Are ambient Learning Dashboards effective means for
creating balanced group participation in classroom settings?
36
EVALUATION SETUPch3
case study 1
# participants 12 students
deployment
1 3h session with dashboard
1 3h session without dashboard
evaluation
class discussion

questionnaires (perceived distraction/
awareness/usefulness)

activity/quality logging
case study 2
# participants 19 students
deployment
half 3h session without dashboard

half 3h session with dashboard
evaluation
questionnaires (perceived importance
feedback/motivation)
activity/quality logging
37
Visualise balance in an abstract and neutral way
Add the qualitative dimension to the visualisation
Create a realistic picture of the classroom situation
38
RESULTS
RQ3: What are the design challenges for ambient LDs to promote balanced
group participation in classrooms, and how can they be met?
ch3
Ambient dashboards as support for teacher/presenter
Ambient dashboards raise awareness of the invisible
Ambient feedback information can activate students
39
RESULTS
RQ4: Are ambient LDs effective means for creating balanced group
participation in classroom settings?
ch3
40
ABSTRACT / NEUTRALch3
Group 1 Group 2
Group 5 Group 3
Group 4
CONVERGENCEch3
-0.2
0
0.2
0.4
0.6
0.8
%distancefromaverage
-0.2
0
0.2
0.4
0.6
0.8
0 10.5
% of time of feedback session passed
%ofdistancefromaverage
without visualisation
with visualisation
group 1
group 2
group 3
group 4
group 5
group 6
41
42
CONTRIBUTIONS
Published Special Issue on Awareness and Reflection in
Technology-Enhanced Learning, IJTEL

(8/21 submissions accepted)

Charleer, S., Klerkx, J., Duval, E., De Laet, T. and Verbert, K. (2017) ‘Towards balanced
discussions in the classroom using ambient information visualisations’, Int. J. Technology Enhanced
Learning, Vol. 9, Nos. 2/3, pp.227–253.
Basis for new research collaboration

with the University of Sidney
ch3
SUPPORTING ADVISER-STUDENT DIALOGUEch4
43
lack of data-based feedback
SUPPORTING ADVISER-STUDENT DIALOGUEch4
RQ5: What are the design challenges for creating a Learning
Dashboard to support study advice sessions, and how can they be
met?
RQ6: How does such a Learning Dashboard contribute to the role
of the adviser, student, and dialogue?
44
EVALUATION SETUPch4
design
# participants
17 study advisers (preliminary feedback)

5 study advisers (iterative feedback)
approach
brainstorms/observations

iterative design
evaluation
# participants 5 study advisors
deployment
Engineering Science, Engineering Science:
Architecture

97 sessions (15-30min per session)
evaluation
15 sessions observed
questionnaires perceived usefulness
45
Data Confidence
Collaboration
Adviser’s role
46
RESULTS
RQ6: How does such a Learning Dashboard contribute to the role of the
adviser, student, and dialogue?
ch4
RQ5: What are the design challenges for creating a Learning Dashboard to
support study advice sessions, and how can they be met?
Authorship
Visual Encoding
Ethics
RESULTSch4
47
RESULTSch4
48
RESULTSch4
S. Claes, N. Wouters, K. Slegers, and A. V. Moere. Controlling In-the-Wild Evaluation Studies of Public Displays. pages 81–84, 2015.
49
50
CONTRIBUTIONSch4
Published in IEEE Transactions on Learning Technologies

11% acceptance rate, IF 2.267

S. Charleer, A. Vande Moere, J. Klerkx, K. Verbert, and T. De Laet. Learning analytics dashboards
to support adviser-student dialogue. IEEE Transaction on Learning Technologies, 18 pages
Deployed at

Engineering Science, Engineering Science: Architecture, Maths,
Biology, Physics, Geology, Geography, Biochemistry, Informatics,
Bio-engineering, Engineering Technology (3 campuses)
15 study advisers during 165 sessions
BEYOND THE TEXTxtra
Assistent Bachelor/Master Engineering Science courses
Advisor Master Theses
3 European Projects
51
52
PROBLEEMOPLOSSEN & ONTWERPEN 3xtra
52
53
PROBLEEMOPLOSSEN & ONTWERPEN 3xtra
53
MASTER THESESxtra
54
55
MASTER THESESxtra
55
Discover, interact Personal Space
(Mendeley)
V. Alvarez, J. Klerkx, S. Charleer, E. Duval, and D. Moor. Science 2.0 and visual data exploration using augmented reality. In Proceedings of the Workshop on Collaboration Meets
Interactive Surfaces: Walls, Tables, Tablets and Phones (CMIS), ACM International Conference on Interactive Tabletops and Surfaces (ITS), Dresden, D, October 2014, pages 1–6,
Oct. 2014
MASTER THESESxtra
56
57
EUROPEAN PROJECTSxtra
57
S. Charleer, J. Klerkx, E. Duval, T. De Laet, and K. Verbert. Faceted Search on Coordinated Tablets and Tabletop: a Comparison. In Proceedings of the 8th
ACM SIGCHI Symposium on Engineering Interactive Computing Systems, Engineering Interactive Computing Systems, Brussels, Belgium, 21-24 June 2016,
pages 165–170. ACM, June 2016
FACETED SEARCH TABLETS vs TABLETOPxtra
58
59
S. Charleer, J. Klerkx, and E. Duval. PoPI: Glyph Designs for Collaborative Filtering on Interactive Tabletops. In E. Bertini, J. Kennedy, and E. Puppo, editors, Eurographics Conference on
Visualization (EuroVis) - Short Papers. The Eurographics Association, 2015
GLYPHS FOR COLLABORATIONxtra
59
60
SUMMARYwrp
3 learning settings
7 dashboards
100+ students, 20 instructors, 17 study advisers
19 publications
61
SUMMARYwrp
62
Citation indices Since 2012
# citations 120
h-index 7
i10-index 4
CITATIONSwrp
62
63
FUTURE (ONGOING) WORKwrp
Ground work for long-term evaluations/deployments
Leiden University
Student union requests faculty deployment
KU Leuven has shown interest in dashboard university-wide
ȡ
leerkracht callcenter verkoper administratie management
actionable
non-actionable
Opleiding: MS Excel
Opleiding: MS PowerPoint
studies
8.9%
7.2%
Engels
Frans
Duits
talen
5.1%
2.4%
Antwerpen
Brussel
regio
3.9%
2.2%
leeftijd dagen werkloos
0 20
18 65 0 800
0 10 0 10
FUTURE (ONGOING) WORKwrp
64
65
FUTURE (ONGOING) WORKwrp
65
66
66
Designing and Evaluating 

Student-facing Learning Dashboards

Lessons Learnt
Sven Charleer
Public PhD Defence
July 6, 2017
(╯°□°)╯︵ ┻━┻

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Designing and Evaluating Student-facing Learning Dashboards: Lessons Learnt (Public PhD Defence)

  • 1. Sven Charleer Designing and Evaluating 
 Student-facing Learning Dashboards
 Lessons Learnt Public PhD Defence July 6, 2017 Augment
 Lab
  • 3. 3 3
  • 4. LEARNING ANALYTICS 4 “The measurement, collection, analysis, and reporting of data about learners and their contexts, for purpose of understanding and optimising learning and the environments in which it occurs” J. L. Santos. Learning Analytics and Learning Dashboards: a Human- Computer Interaction Perspective. PhD dissertation, KU Leuven, 2015. G. Siemens. “Learning analytics: envisioning a research discipline and a domain of practice”. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge . ACM. 2012, pp. 4–8. session course degree year Microlevel intro
  • 5. AUTOMATE AUGMENT Engelbart, D. C. (2001). Augmenting human intellect: a conceptual framework (1962). PACKER, Randall and JORDAN, Ken. Multimedia. From Wagner to Virtual Reality. New York: WW Norton & Company, 64-90. VS 5 Arnold, Kimberly E., and Matthew D. Pistilli. Course Signals at Purdue: Using learning analytics to increase student success. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge. ACM, 2012.
  • 6. LEARNING DASHBOARDS 6 “A Learning Dashboard is a single display that aggregates different indicators about learner(s), learning process(es) and/or learning context(s) into one or multiple visualisations.” B. A. Schwendimann, M. J. Rodríguez-Triana, A. Vozniuk, L. P. Prieto, M. S. Boroujeni, A. Holzer, D. Gillet, and P. Dillenbourg. Understanding learning at a glance: An overview of learning dashboard studies. In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge, pages 532–533. ACM, 2016. K. Verbert, E. Duval, J. Klerkx, S. Govaerts, and J. L. Santos. Learning Analytics Dashboard Applications. American Behavioral Scientist, 57(10):1500–1509, 2013. Perceived benefits Design guidelines intro awareness (self) reflection sense making impact data questions answers behavior
  • 7. 7 DESIGN-BASED RESEARCHintro External Input through collaboration with participants: Teachers Students Experts
  • 11. Santos, J.L., Verbert, K., Govaerts, S., Duval, E.: Addressing learner issues with stepup!: an evaluation. In: Proceedings of the Third International Conference on Learning Analytics and Knowledge, ACM (2013) 14–22 CREATING EFFECTIVE LEARNING DASHBOARDSch2 11
  • 12. PATH OF EFFORT OUTCOMES INFLUENCES 12 https://www.flickr.com/photos/lockechrisj/ LEARNER PATHSch2
  • 13. RQ1: How should we visualise learner data to support students to explore the path from effort to outcomes? RQ2: How can we promote students, inside and outside the classroom, to actively explore this effort to outcomes path? CREATING EFFECTIVE LEARNING DASHBOARDSch2 13
  • 17. Abstract the LA data Provide access to the artefacts Augment the abstracted data Provide access to teacher and peer feedback 17 RESULTS RQ1: What are relevant learning traces, and how should we visualise these data to support students to explore the path from effort to outcomes? ch2
  • 18. 18 RESULTS RQ2: How can we promote students, inside and outside the classroom, to actively explore this effort to outcomes path? Visualise the learner path Integrate LA into the workflow Facilitate collaborative exploration of the LA data ch2
  • 23. 23 Geoff’s hypothesis Geoff’s data collection Geoff’s conclusion Angela’s data collection Angela activities Geoff’s activities VISUALISE LEARNER PATHch2
  • 24. 24 Geoff’s hypothesis Geoff’s data collection Geoff’s conclusion Angela’s data collection Angela activities Geoff’s activities VISUALISE LEARNER PATHch2
  • 25. 25 Geoff’s hypothesis Geoff’s data collection Geoff’s conclusion Angela’s data collection Angela activities Geoff’s activities VISUALISE LEARNER PATHch2
  • 26. 26 Geoff’s hypothesis Geoff’s data collection Geoff’s conclusion Angela’s data collection Angela activities Geoff’s activities VISUALISE LEARNER PATHch2
  • 27. 27 Geoff’s hypothesis Geoff’s data collection Geoff’s conclusion Angela’s data collection Angela activities Geoff’s activities VISUALISE LEARNER PATHch2
  • 28. 28 Geoff’s hypothesis Geoff’s data collection Geoff’s conclusion Angela’s data collection Angela activities Geoff’s activities VISUALISE LEARNER PATHch2
  • 29. 29 Geoff’s hypothesis Geoff’s data collection Geoff’s conclusion Angela’s data collection Angela activities Geoff’s activities VISUALISE LEARNER PATHch2
  • 30. 30 Geoff’s hypothesis Geoff’s data collection Geoff’s conclusion Angela’s data collection Angela activities Geoff’s activities VISUALISE LEARNER PATHch2
  • 32. 32 CONTRIBUTIONSch2 Guidelines published at European Conference on Technology Enhanced Learning
 (25% acceptance)
 S. Charleer, J. Klerkx, E. Duval, T. De Laet, and K. Verbert. Creating effective learning analytics dashboards: Lessons learnt. Adaptive and Adaptable Learning: 11th European Conference on Technology Enhanced Learning, EC-TEL 2016, Lyon, France, September 13-16, 2016, Proceedings, pages 42–56, Cham, 2016. Springer International Publishing 14 papers
 collaboration with
 Stanford University - University of Technology Sidney - University of the Basque Country - Murdoch University Perth - Curtin University Perth Program Committees
 EC-TEL, LAK, Cross-LAK, ARTEL, EC-TEL Doctoral Consortium European Commission for the weSPOT project
  • 35. BALANCED DISCUSSION IN THE CLASSROOMch3 over- and under-participation 35
  • 36. BALANCED DISCUSSION IN THE CLASSROOMch3 RQ3: What are the design challenges for ambient Learning Dashboards to promote balanced group participation in classrooms, and how can they be met? RQ4: Are ambient Learning Dashboards effective means for creating balanced group participation in classroom settings? 36
  • 37. EVALUATION SETUPch3 case study 1 # participants 12 students deployment 1 3h session with dashboard 1 3h session without dashboard evaluation class discussion
 questionnaires (perceived distraction/ awareness/usefulness)
 activity/quality logging case study 2 # participants 19 students deployment half 3h session without dashboard
 half 3h session with dashboard evaluation questionnaires (perceived importance feedback/motivation) activity/quality logging 37
  • 38. Visualise balance in an abstract and neutral way Add the qualitative dimension to the visualisation Create a realistic picture of the classroom situation 38 RESULTS RQ3: What are the design challenges for ambient LDs to promote balanced group participation in classrooms, and how can they be met? ch3
  • 39. Ambient dashboards as support for teacher/presenter Ambient dashboards raise awareness of the invisible Ambient feedback information can activate students 39 RESULTS RQ4: Are ambient LDs effective means for creating balanced group participation in classroom settings? ch3
  • 40. 40 ABSTRACT / NEUTRALch3 Group 1 Group 2 Group 5 Group 3 Group 4
  • 41. CONVERGENCEch3 -0.2 0 0.2 0.4 0.6 0.8 %distancefromaverage -0.2 0 0.2 0.4 0.6 0.8 0 10.5 % of time of feedback session passed %ofdistancefromaverage without visualisation with visualisation group 1 group 2 group 3 group 4 group 5 group 6 41
  • 42. 42 CONTRIBUTIONS Published Special Issue on Awareness and Reflection in Technology-Enhanced Learning, IJTEL
 (8/21 submissions accepted)
 Charleer, S., Klerkx, J., Duval, E., De Laet, T. and Verbert, K. (2017) ‘Towards balanced discussions in the classroom using ambient information visualisations’, Int. J. Technology Enhanced Learning, Vol. 9, Nos. 2/3, pp.227–253. Basis for new research collaboration
 with the University of Sidney ch3
  • 44. SUPPORTING ADVISER-STUDENT DIALOGUEch4 RQ5: What are the design challenges for creating a Learning Dashboard to support study advice sessions, and how can they be met? RQ6: How does such a Learning Dashboard contribute to the role of the adviser, student, and dialogue? 44
  • 45. EVALUATION SETUPch4 design # participants 17 study advisers (preliminary feedback)
 5 study advisers (iterative feedback) approach brainstorms/observations
 iterative design evaluation # participants 5 study advisors deployment Engineering Science, Engineering Science: Architecture
 97 sessions (15-30min per session) evaluation 15 sessions observed questionnaires perceived usefulness 45
  • 46. Data Confidence Collaboration Adviser’s role 46 RESULTS RQ6: How does such a Learning Dashboard contribute to the role of the adviser, student, and dialogue? ch4 RQ5: What are the design challenges for creating a Learning Dashboard to support study advice sessions, and how can they be met? Authorship Visual Encoding Ethics
  • 49. RESULTSch4 S. Claes, N. Wouters, K. Slegers, and A. V. Moere. Controlling In-the-Wild Evaluation Studies of Public Displays. pages 81–84, 2015. 49
  • 50. 50 CONTRIBUTIONSch4 Published in IEEE Transactions on Learning Technologies
 11% acceptance rate, IF 2.267
 S. Charleer, A. Vande Moere, J. Klerkx, K. Verbert, and T. De Laet. Learning analytics dashboards to support adviser-student dialogue. IEEE Transaction on Learning Technologies, 18 pages Deployed at
 Engineering Science, Engineering Science: Architecture, Maths, Biology, Physics, Geology, Geography, Biochemistry, Informatics, Bio-engineering, Engineering Technology (3 campuses) 15 study advisers during 165 sessions
  • 51. BEYOND THE TEXTxtra Assistent Bachelor/Master Engineering Science courses Advisor Master Theses 3 European Projects 51
  • 56. Discover, interact Personal Space (Mendeley) V. Alvarez, J. Klerkx, S. Charleer, E. Duval, and D. Moor. Science 2.0 and visual data exploration using augmented reality. In Proceedings of the Workshop on Collaboration Meets Interactive Surfaces: Walls, Tables, Tablets and Phones (CMIS), ACM International Conference on Interactive Tabletops and Surfaces (ITS), Dresden, D, October 2014, pages 1–6, Oct. 2014 MASTER THESESxtra 56
  • 58. S. Charleer, J. Klerkx, E. Duval, T. De Laet, and K. Verbert. Faceted Search on Coordinated Tablets and Tabletop: a Comparison. In Proceedings of the 8th ACM SIGCHI Symposium on Engineering Interactive Computing Systems, Engineering Interactive Computing Systems, Brussels, Belgium, 21-24 June 2016, pages 165–170. ACM, June 2016 FACETED SEARCH TABLETS vs TABLETOPxtra 58
  • 59. 59 S. Charleer, J. Klerkx, and E. Duval. PoPI: Glyph Designs for Collaborative Filtering on Interactive Tabletops. In E. Bertini, J. Kennedy, and E. Puppo, editors, Eurographics Conference on Visualization (EuroVis) - Short Papers. The Eurographics Association, 2015 GLYPHS FOR COLLABORATIONxtra 59
  • 60. 60 SUMMARYwrp 3 learning settings 7 dashboards 100+ students, 20 instructors, 17 study advisers 19 publications
  • 62. 62 Citation indices Since 2012 # citations 120 h-index 7 i10-index 4 CITATIONSwrp 62
  • 63. 63 FUTURE (ONGOING) WORKwrp Ground work for long-term evaluations/deployments Leiden University Student union requests faculty deployment KU Leuven has shown interest in dashboard university-wide
  • 64. ȡ leerkracht callcenter verkoper administratie management actionable non-actionable Opleiding: MS Excel Opleiding: MS PowerPoint studies 8.9% 7.2% Engels Frans Duits talen 5.1% 2.4% Antwerpen Brussel regio 3.9% 2.2% leeftijd dagen werkloos 0 20 18 65 0 800 0 10 0 10 FUTURE (ONGOING) WORKwrp 64
  • 66. 66 66
  • 67. Designing and Evaluating 
 Student-facing Learning Dashboards
 Lessons Learnt Sven Charleer Public PhD Defence July 6, 2017 (╯°□°)╯︵ ┻━┻