The document discusses the use of learning analytics dashboards to provide feedback to students in higher education. It describes two case studies at KU Leuven: LISSA, which provides analytics to advisors to support conversations with students, and a learning skills dashboard that gives students individualized feedback on skills like time management and concentration based on survey results. Evaluation found LISSA helped advisors guide students and increased student reflection. The skills dashboard was used by most students and improved perceptions of receiving feedback. The document provides best practices for learning analytics including starting small, involving stakeholders, and ensuring feedback is actionable.

1. https://mfeldstein.com/purdue-course-signals-data-issue-

2. Learning dashboards
for first-year students
the (non)sense of chances of success and predictive models
Tinne De Laet
Tinne.DeLaet@kuleuven.be
@TinneDeLaet

3. “Learning analytics is
about collecting traces
that learners leave
behind and using
those traces to
improve learning.”
- Erik Duval
Learning Analytics and Educational Data Mining, Erik Duval’s Weblog, 30 January 2012, https://erikduval.wordpress.com/2012/01/30/learning-analytics-and-educational-data-mining/ 3
Learning Analytics?

4. Learning Dashboards?
4Dashboard Confusion, Stephen Few, Intelligent Enterprise, March 20, 2004
“A dashboard is a visual display
of the most important
information needed to achieve
one or more objectives;
consolidated and arranged on a
single screen so the information
can be monitored at a glance.”
- Stephen Few

5. Successful Transition from secondary to higher
Education using Learning Analytics
enhance a successful transition from
secondary to higher education by means of
learning analytics
 design and build analytics dashboards,
 dashboards that go beyond identifying at-risk
students, allowing actionable feedback for all
students on a large scale.
Achieving Benefits from Learning Analytics
research strategies and practices for using
learning analytics to support students during
their first year at university
 developing the technological aspects of
learning analytics,
 focuses on how learning analytics can be used
to support students.
5
www.stela-project.eu
@STELA_project
2015-1-UK01-KA203-013767
www.ableproject.eu
@ABLE_project_eu
562167-EPP-1-2015-1-BE-EPPKA3-PI-FORWARD

6. STELA ♥ ABLE
6
actionable feedback
student-centered
program level
inclusive
first-year experience
institution-wide
Learning Analytics
actual implementation

7. [!] Feedback must be “actionable”.
7
Warning!
Male students have
10% less probability to
be successful.
You are male.
Warning!
Your online activity is
lagging behind.
action?
?
action?
?


8. 8
awareness
(self-)reflection
sensemaking
impact
data
questions
answers
behavior change
new meaning
Verbert K, Duval E, Klerkx J; Govaerts S, Santos JL (2013) Learning analytics dashboard applications. American Behavioural Scientist, 10 pages. Published online February 2013.
[!] Feedback must be “actionable”.

9. learning dashboards @KU Leuven
interaction
self-reflection
LISSA
STUDENT
ADVISOR
STUDENT LASSI –
learning skills
REX - scoresPOS – future
students

10. [!] Start with the available data.
Lots of data may eventually become
available in the future …
…. already start with what is available
10
(*)
(*) Zarraonandia, T., Aedo, I., Díaz, P., & Montero, A. (2013). An augmented lecture feedback system to support learner and teacher communication.
British Journal of Educational Technology, 44(4), 616-628.

11. Case study
dashboard interaction student – study advisor

12. Study advisor – student conversations
12
Should I consider
another program?
Can I still finish the
bachelor in 3 years?
How should I compose
my program for next
year?
What is the personal
situation?
How can I help?
What is the best
next step?

13. [!] Use all available expertise.
13
visualization experts
practitioners / end-users
researchers LA
researchers first-year
study success
Charleer S., Vande Moere A., Klerkx J., Verbert K., De Laet T. (2017). Learning Analytics Dashboards to Support Adviser-Student Dialogue.
In IEEE Transactions on Learning Technology (http://ieeexplore.ieee.org/document/7959628/).

14. LISSA dashboard
https://able.cs.kuleuven.be/demo-september/2016/2

15. [!] Wording matters.
15
73% chance of success
73% of students of earlier
cohorts with the same
study efficiency obtained
the bachelor degree
http://blog.associatie.kuleuven.be/tinnedelaet/the-nonsense-of-chances-of-success-and-predictive-models/

16. LISSA dashboard
16
Three examination periods
observations, interviews,
questionnaires
pilot with two engineering programs
Charleer S., Vande Moere A., Klerkx J., Verbert K., De Laet T. (2017). Learning Analytics Dashboards to Support Adviser-Student Dialogue. In IEEE Transactions on Learning Technology

17. LISSA: evaluation – observations
17
15 observations
insights
(-) factual
(+) interpretative
(!) reflective
Charleer S., Vande Moere A., Klerkx J., Verbert K., De Laet T. (2017). Learning Analytics Dashboards to Support Adviser-Student Dialogue. In IEEE Transactions on Learning Technology

18. Evaluation – interviews
“When students see the numbers, they are
surprised, but now they believe me.
Before, I used my gut feeling, now I feel
more certain of what I say as well”.
“It’s like a main thread
guiding the
conversation.”
“I can talk about what to do with the results,
instead of each time looking for the data and
puzzling it together.”
“Students don’t know where to look during the
conversation, and avoid eye contact.
The dashboard provides them a point of focus”.
“A student changed her
study method in June and
could now see it paid off.”
LISSA supports a personal dialogue.
 the level of usage depends on the experience
and style of the study advisors
 fact-based evidence at the side
 narrative thread
 key moments and student path help to
reconstruct personal track
“I can focus on the
student’s personal
path, rather than on
the facts.”
“Now, I can blame
the dashboard and
focus on
collaboratively looking
for the next step to
take.”
18

19. LISSA: status
19
26 programs >4500 students
114 student advisors
training of study advisors
http://blog.associatie.kuleuven.be/tinnedelaet/lissa-learning-dashboard-supporting-student-advisers-in-traditional-higher-education/
Millecamp M., Gutiérrez F., Charleer S., Verbert K., De Laet T.# (2018). A qualitative evaluation of a learning dashboard to support advisor-student
dialogues. Proceedings of the 8th International Learning Analytics & Knowledge Conference. LAK. Sydney, 5-9 March 2018 (pp. 1-5) ACM.
dashboards for three examination
periods

20. LISSA: evaluation – student
questionnaires
20
26 programs @KU Leuven
291 student questionnaires
first examination period
“Confronting, but
useful”
“I want to use this
dashboard at home.”
“Also show the sub-grades
for labs, … ”
“How can I know the data is
trustworth?”
“Can’t these visualizations be
send to students?” “Crisp and clear.”

21. 21
0
0
1
1
1
1
4
2
1
4
4
3
29
21
36
37
49
42
176
112
156
132
141
169
80
155
93
116
92
72
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
1. The dashboard is clarifying and surveyable.
2. The shown information regarding my study
situation is correct.
3. The shown position with respect to my fellow
students (histograms per exam and global…
4. A conversation with my student advisors helped
me to gain insight in my study trajectory.
5. The visualisation is of added value to the
conversation with the student advisor.
6. The shown information provide me insight in
my current situation.
Student questionnaire January 2018 (N=291)
Strongly Disagree Disagree Neither Agree or Disagree Agree Strongly Agree

22. [!] Do not oversimplify. Show
uncertainty.
22
• reality is complex
• measurement is limited
• individual circumstances
• need for nuance
• trigger reflection
http://blog.associatie.kuleuven.be/tinnedelaet/the-nonsense-of-chances-of-success-and-predictive-models/

23. [!] Be careful with predictive
algorithms.
23
http://blog.associatie.kuleuven.be/tinnedelaet/the-nonsense-of-chances-of-success-and-predictive-models/
• reality is complex
• measurement is limited
• individual circumstances
• need for nuance
• trigger reflection

24. Case study
student-facing dashboards

25. [!] Start with the available data.
25
data already available?
administrative (examples)
student records course grades
systems (examples)
LMS access logs advisor meetings
)
Broos T., Verbert K., Van Soom C., Langie G., De Laet T.# (2018). Small data as a conversation starter for learning analytics: exam results dashboard for first-year students in higher
education. Journal of Research in Innovative Teaching & Learning, , 1-14.

26. [!] Think beyond the obvious data.
26
• Don’t think too traditional.
• Many institutions are collecting survey
data for educational research.

27. [!] Not all data is usable.
27
example data from a traditional course with “VLE as a file system”
test scores
activity/week (#days)
weeks of the year

28. [!] Not all data is usable.
28
example data from a course with flipped classroom & blended learning
exam scores
activity (# of modules used)
Not a single student
using less than 10
modules passed the
course.
Most of the successful
students used 15
modules or more.

29. [!] Keep Learning Analytics in
mind when designing learning
activities.
29
Learning
Analytics
Learning Design
INFORM
ENABLE
If LA indeed contributes to improved
learning design…
… don’t make it an afterthought

30. 30
Does my concentration
matter?
How is my time
management?
I feel uncertain.
Is this normal?
How can I improve
my concentration?

31. data already available?
administrative (examples)
student records course grades
[!] Think beyond the obvious data.
31
systems (examples)
LMS access logs advisor meetings
surveys (examples)
quality insurance LASSI

32. ~ 30 LASSI questions
(shortened version)
“Learning Skills”
Example: When preparing for an
exam, I create questions that I
think might be included.
Example: I find it difficult to
maintain my concentration
while doing my coursework.
Example: I find it hard to stick
to a study schedule.
raw scores
(selected 5 out of 10)
CONCENTRATION
MOTIVATION
FAILURE ANXIETY
TEST STRATEGY
TIME MANAGEMENT
norm scores
(in Flemish HE context)
Example: STRONG
Example: AVERAGE
Example: LOW
Example: VERY STRONG
Example: VERY WEAK
32
 Meta cognitive abilities
Pinxten, M., Van Soom, C., Peeters, C., De Laet, T., Langie, G., At-risk at the gate: prediction of study success of first-year science and engineering students in an
open-admission university in Flanders—any incremental validity of study strategies? Eur J Psychol Educ (2017).
readySTEMgo Erasmus+ project https://iiw.kuleuven.be/english/readystemgo

33. Dashboard learning skills
33
students complete LASSI
questionnaire
students received personalized email
with invitation for dashboard
4367 students in 26 programs
in 9 faculties @KU Leuven
demo:
https://learninganalytics.set.kuleuven.be/lassi-1718/ (KU Leuven login)
2 programs @TU Delft

34. Feedback model
1. What is this about?
2. How am I doing?
3. How does this relates to
others?
4. Why is this relevant?
5. What can I do about it?
34

35. 35
3. How does this relates to
others?
2. How am I doing?
1. What is this about?
@studyProgram@
@yourScore@

36. 4. Why is this relevant?
5. What can I do about it?
36

37. 37
5. What can I do about it?

38. Response
38
3868 students (89%)
used dashboard

39. Student feedback?
39
http://blog.associatie.kuleuven.be/tinnedelaet/learning-dashboard-for-actionable-feedback-on-learning-and-studying-skills/
How CLEAR is this info?
stars stars

40. Students that click through
Broos, T., Peeters, L., Verbert, K., Van Soom, C., Langie, G., & De Laet, T. (2017, July). Dashboard for Actionable Feedback on Learning Skills: Scalability and Usefulness.
In International Conference on Learning and Collaboration Technologies (pp. 229-241). Springer, Cham.
40
 better learning skills

41. More intense users
Broos, T., Peeters, L., Verbert, K., Van Soom, C., Langie, G., & De Laet, T. (2017, July). Dashboard for Actionable Feedback on Learning Skills: Scalability and Usefulness.
In International Conference on Learning and Collaboration Technologies (pp. 229-241). Springer, Cham.
41
 worse learning skills

42. [!] Give students “the key”.
42
• Student has the key to own
data.
• Student takes initiative to
share/discuss own data.
• GDPR as opportunity!

43. Dashboard positioning test
43https://feedback.ijkingstoets.be/ijkingstoets-10-ir/index.html (10ir0demo)

44. [!] Acceptance precedes impact.
44
• Involve stakeholders from the start and
value their input!
COmmunication
COoperation
• Demonstrate usefulness.
• Take care of ethics and privacy.
• Best scenario:
students & study advisors as ambassadors
COCO

45. Impact?
survey before intervention
 2nd year students 2016-2017
 experiences first-year feedback
 41 vragen, 5-point Likert scale
 pen & paper
dashboards
 LISSA
 LASSI (learning skills)
 3 x REX (grades)
Survey after intervention
 2nd year students 2017-2018

46. Impact?
During the first year I received sufficient information regarding my academic achievements.
46
Engineering Science (p<0.001)

47. Impact?
The information I received helped to position myself with respect to my peers.
47
Engineering Science (p<0.001)

48. Impact?
48
The information I received made me reflect.
The information I received made me adapt my behaviour.

49. [!] Context matters!
• available data
• national and institutional regulations
and culture
• educational vision
• educational system, size of population ..
• …
Don’t just copy existing LA solutions!
49

50. Summary
case studies 11 findings/recommendations
[!] Use all available expertise.
[!] Start with the available data.
[!] Look beyond the obvious data.
[!] Not all data is usable.
[!] Wording matters.
[!] Don’t oversimplify. Show uncertainty.
[!] Beware of predictive algorithms.
[!] Keep Learning Analytics in mind when designing
learning activities.
[!] Give students “the key” to their data.
[!] Acceptance precedes impact.
[!] Context matters!
 humble approach
 small data
 involvement of stakeholders, especially practitioners
 actionable feedback
 scalability
 traditional university settings
Is this Learning Analytics?

51. Future?
51
Continue and extend dashboards
@KU Leuven?
Transfer to other universities?
extension?

52. Project team @
52
Sven Charleer
AugmentHCI, Computer Science department
PhD researcher ABLE
Katrien Verbert
AugmentHCI, Computer Science department
Copromotor of STELA & ABLE
Carolien Van Soom
Leuven Engineering and Science Education Center
Head of Tutorial Services of Science
Copromotor of STELA & ABLE
Greet Langie
Leuven Engineering and Science Education Center
Vicedean (education) faculty of Engineering Technology
Copromotor of STELA & ABLE
Tinne De Laet
Leuven Engineering and Science Education Center
Head of Tutorial Services of Engineering Science
Coordinator of STELA
KU Leuven coordinator of ABLE
Francisco Gutiérrez
AugmentHCI, Computer Science department
PhD researcher ABLE
Tom Broos
Leuven Engineering and Science Education Center
AugmentHCI, Computer Science department
PhD researcher STELA
Martijn Millecamp
AugmentHCI, Computer Science department
PhD researcher ABLE
Special thanks to study advisors for their cooperation, advice, feedback, and support!
Jasper, Bart, Riet, Hilde, An, Katrien, …
♥

53. 53
2
3
1
7
2
1
10
44
23
36
3
11
64
97
81
74
36
29
150
115
126
110
119
91
61
30
56
59
128
157
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
7. The dashboard makes me more aware on my current
study situation.
8. The dashboard makes me forecast the different
possibilities in my future study trajectory.
9. The dashboard helps me to reflect on my past and
current study behaviour or study trajectory.
10. The dashboards stimulates me to adapt my approach
in my studies for the future (study behaviour or study…
11. If I will have a new conversation after one of the next
examination periods, I hope that the visualisation will be…
12. I would like to consult the information on my own.
Student questionnaire January 2018 (N=291)
Strongly Disagree Disagree Neither Agree or Disagree Agree Strongly Agree

54. How to determine
thresholds for different
groups?
LISSA dashboard
54
 upper and lower group: clear message
 middle group as small as possible
 Do not overfit! (nuance)