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https://mfeldstein.com/purdue-course-signals-data-issue-explainer/
Learning dashboards for first‐year students
the (non)sense of chances of success and predictive models
Tinne De [email protected]
@TinneDeLaet
“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?
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
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_project2015‐1‐UK01‐KA203‐013767
www.ableproject.eu
@ABLE_project_eu
562167‐EPP‐1‐2015‐1‐BE‐EPPKA3‐PI‐FORWARD
STELA ♥ ABLE
6
actionable feedbackstudent-centered
program level
inclusive
first-year experience
institution-wide
Learning Analytics
actual implementation
[!] 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
awareness
(self-)reflection
sensemaking
impact
data
questions
answers
behavior changenew 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”.
learning dashboards @KU Leuven
interaction
self-reflection
LISSA
STUDENTADVISOR STUDENT LASSI –
learning skills
REX - scoresPOS – futurestudents
[!] 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.
Case studydashboard interaction student – study advisor
Study advisor – student conversations
12
Should I consideranother program?
Can I still finish the bachelor in 3 years?
How should I composemy program for next year?
What is the personalsituation?
How can I help?
What is the best next step?
[!] 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/).
LISSA dashboardhttps://able.cs.kuleuven.be/demo‐september/2016/2
[!] 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/
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
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
Evaluation – interviews
“When students see the numbers, they are surprised, but now they believe me.
Before, I used my gut feeling, now I feelmore 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 blamethe dashboard and
focus on collaboratively looking
for the next step to take.”
18
LISSA: status
19
26 programs >4500 students114 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
LISSA: evaluation – student questionnaires
20
26 programs @KU Leuven291 student questionnaires
first examination period
“Confronting, but useful”
“I want to use thisdashboard at home.”
“Also show the sub‐gradesfor labs, … ”
“How can I know the data is trustworth?”
“Can’t these visualizations besend to students?” “Crisp and clear.”
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 studysituation is correct.
3. The shown position with respect to my fellowstudents (histograms per exam and global…
4. A conversation with my student advisors helpedme to gain insight in my study trajectory.
5. The visualisation is of added value to theconversation with the student advisor.
6. The shown information provide me insight inmy current situation.
Student questionnaire January 2018 (N=291)
Strongly Disagree Disagree Neither Agree or Disagree Agree Strongly Agree
[!] 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/
[!] Be careful with predictive algorithms.
23http://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
Case studystudent-facing dashboards
[!] 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.
[!] Think beyond the obvious data.
26
• Don’t think too traditional.
• Many institutions are collecting survey data for educational research.
[!] 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
[!] 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.
[!] 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
Does my concentrationmatter?
How is my time management?
I feel uncertain.Is this normal?
How can I improve my concentration?
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
~ 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
Dashboard learning skills
33
students complete LASSI questionnaire
students received personalized emailwith invitation for dashboard
4367 students in 26 programsin 9 faculties @KU Leuven
demo: https://learninganalytics.set.kuleuven.be/lassi-1718/ (KU Leuven login)
2 programs @TU Delft
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
3. How does this relates to others?
2. How am I doing?
1. What is this about?
@studyProgram@
@yourScore@
4. Why is this relevant?
5. What can I do about it?
36
37
5. What can I do about it?
Response
38
3868 students (89%) used dashboard
Student feedback?
39http://blog.associatie.kuleuven.be/tinnedelaet/learning-dashboard-for-actionable-feedback-on-learning-and-studying-skills/
How CLEAR is this info?
stars stars
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
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
[!] Give students “the key”.
42
• Student has the key to own data.
• Student takes initiative to share/discuss own data.
• GDPR as opportunity!
Dashboard positioning test
43https://feedback.ijkingstoets.be/ijkingstoets-10-ir/index.html (10ir0demo)
[!] Acceptance precedes impact.
44
• Involve stakeholders from the start and value their input!
COmmunicationCOoperation
• Demonstrate usefulness.
• Take care of ethics and privacy.
• Best scenario: students & study advisors as ambassadors
CO
CO
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
Impact?
During the first year I received sufficient information regarding my academic achievements.
46
Engineering Science (p<0.001)
Impact?
The information I received helped to position myself with respect to my peers.
47
Engineering Science (p<0.001)
Impact?
48
The information I received made me reflect.
The information I received made me adapt my behaviour.
[!] 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
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?
Future?
51
Continue and extend dashboards @KU Leuven?
Transfer to other universities?
extension?
Project team @
52
Sven CharleerAugmentHCI, Computer Science department
PhD researcher ABLE
Katrien VerbertAugmentHCI, Computer Science department
Copromotor of STELA & ABLE
Carolien Van SoomLeuven Engineering and Science Education CenterHead of Tutorial Services of ScienceCopromotor of STELA & ABLE
Greet Langie Leuven Engineering and Science Education Center
Vicedean (education) faculty of Engineering TechnologyCopromotor of STELA & ABLE
Tinne De LaetLeuven Engineering and Science Education CenterHead of Tutorial Services of Engineering ScienceCoordinator of STELAKU Leuven coordinator of ABLE
Francisco GutiérrezAugmentHCI, Computer Science departmentPhD researcher ABLE
Tom BroosLeuven Engineering and Science Education CenterAugmentHCI, Computer Science departmentPhD researcher STELA
Martijn MillecampAugmentHCI, 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
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 currentstudy situation.
8. The dashboard makes me forecast the differentpossibilities in my future study trajectory.
9. The dashboard helps me to reflect on my past andcurrent study behaviour or study trajectory.
10. The dashboards stimulates me to adapt my approachin my studies for the future (study behaviour or study…
11. If I will have a new conversation after one of the nextexamination 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
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)