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IMPROVING EDUCATION BY
LEARNING ANALYTICS
Tinne De Laet, KU Leuven
LEARNING GOALS OF THIS
PRESENTATION
• You know what learning analytics is.
• You can provide at least three examples of learning analytics
interventions.
• You can look-up the 8 recommendations from this presentation.
OUTLINE
1. Who am I? Why I am here?
2. What is learning analytics?
3. Experiences & recommendations
WHO AM I? WHY I AM
HERE?
WHO AM I? WHY I AM HERE?
woman
engineer
Head Tutorial Services
Engineering Science KU
Leuven
.
Tinne De
Laet
associate
professor
WHO AM I? WHY I AM HERE?
Coordinator of STELA Erasmus+
forward-looking cooperation
project
•Forward-looking cooperation project:
562167-EPP-1-2015-1-BE-EPPKA3-PI-
FORWARD
•Successful Transition from secondary to
higher Education using Learning Analytics
•KU Leuven (Belgium), TU Delft (Netherlands),
TU Graz (Austria), Nottingham Trent
University (UK), European Society of
Engineering Education (SEFI)
• http://stela-project.eu/
KU Leuven promotor of ABLE
Erasmus+ strategic partnership
project
•Strategic Partnership: 2015-1-UK01-KA203-
013767
•Achieving Benefits from Learning Analytics
•Nottingham Trent University (UK), KU Leuven
(Belgium),
Leiden University (Netherlands)
• http://www.ableproject.eu/
WHAT IS LEARNING
ANALYTICS?
WHAT IS LEARNING ANALYTICS?
no universally agreed definition
8
“the measurement, collection, analysis and reporting of data about
learners and their contexts, for purposes of understanding and
optimizing learning and the environments in which it occurs” [1]
[1] Learning and Academic Analytics, Siemens, G., 5 August
2011, http://www.learninganalytics.net/?p=131
[2] What is Analytics? Definition and Essential Characteristics, Vol. 1, No. 5. CETIS Analytics Series,
Cooper, A., http://publications.cetis.ac.uk/2012/521
“the process of developing actionable insights through problem
definition and the application of statistical models and analysis against
existing and/or simulated future data” [2]
WAT IS LEARNING ANALYTICS?
9
[3] 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/
“learning analytics is about
collecting traces that learners
leave behind and using those
traces to improve learning”
[Erik Duval, 3]
† 12 March 2016
no universally agreed definition
IS IT ABOUT INSTITUTIONAL DATA?
•high-level figures:
provide an overview for internal and external reports;
used for organisational planning purposes.
•academic analytics:
figures on retention and success, used by the institution to assess
performance.
•educational data mining:
searching for patterns in the data.
•learning analytics:
use of data, which may include ‘big data’,
to provide actionable intelligence for learners and teachers.
[4] Learning analytics FAQs, Rebecca Ferguson, Slideshare,
http://www.slideshare.net/R3beccaF/learning-analytics-fa-qs
EXPERIENCES &
RECOMMENDATIONS
SO ….
data
actionable
insights
improve
learning
SO …. DATA
data
actionable
insights
improve
learning
• A lot of data COULD be available
• What IS available?
• academic performance
• strong relation to study success
• widely available
• digital traces of behavior
• card swipes, in-class polls, lab attendance
• virtual learning environment
• survey data
• strong body of knowledge in pedagogic research
REC 1:
FOCUS
ON DATA
THAT IS
AVAILABL
E
SO …. DATA - THREE LEVELS
any institute
-
academic
performance
ABLE@ KU
Leuven
14 programs
12 study
counsellors
>1000 students
position students
in peer group
impact: how similar
profiles did in the
past
guided planning of
future study
pathway
name student
SO …. DATA - THREE LEVELS
specific context
-
academic
engagement
ABLE@NTU
>25.000
students
SO …. DATA - THREE LEVELS
specific context
-
online courses
(MOOCs-SPOCS)
STELA@
TU Delft
>2.000 students
Learning
Tracker
What is my
activity? What was the
activity of
successful
students in the
past?
SO …. DATA
data
actionable
insights
improve
learning
• combining data that IS available might provide useful insights
• data from educational sciences and psychology
• self-reported (small data)
• standardized tests
• example of feedback on learning skills
• data from university data warehouse (academic performance)
• data from research on skills important for student success
REC 1B:
THINK
BEYOND
THE
OBVIOUS
SO …. DATA
What is my
position with
respect to my
peers?
STELA@KU Leuven
>1.600 students
SO …. DATA
How well did
similar students
do in the past?
STELA@KU Leuven
>1.600 students
SO …. ACTIONABLE INSIGHTS
data
actionable
insights
improve
learning
REC 2:
FEEDBAC
K
SHOULD
ALWAYS
BE
ACTIONA
BLE
“Female students are more successful in
higher education than male students”
70%
successf
60%
successf
so
?
SO …. ACTIONABLE INSIGHTS
engage more
with program!
engage more
with MOOC!
work on your
learning and studying skills
SO …. ACTIONABLE INSIGHTS
If Learning Analytics has the potential of improving learning,
→ (re)design learning such that learning analytics will realize
potential
data
actionable
insights
improve
learning
TAKE
LEARNIN
G
ANALYTIC
S INTO
ACCOUN
T WHEN
REDESIG
NING
LEARNIN
SO …. ACTIONABLE INSIGHTS
None of the
students that
accessed less then
1o online modules
passed the exam.
Most successful
students finish at
least 15 online
modules.
relation
score course
-
virtual learning
environment
activity
STELA@KU
Leuven
SO …. IMPROVE LEARNING REC 4:
USE ALL
AVAILABL
E
EXPERTI
SE
•integrate expertise DURING development
•educational scientists
•computer scientists & IT experts
•visualization experts
•PRACTITIONERS!!! (study advisors, tutors, etc.)
•students & teachers
•How to evaluate tools and resources?
•scalability
data
actionable
insights
improve
learning
SO …. IMPROVE LEARNING
data
actionable
insights
improve
learning
•integrate expertise DURING development
•How to evaluate tools and resources?
•Do they improve learning? How can you measure impact?
•Is perceived usefulness enough?
Is retention the only thing that matters?
•scalability
REC 5:
DEVELOP
CHECKLI
ST FOR
EVALUATI
NG
TOOLS
AND
RESOUR
CES
SO …. IMPROVE LEARNING
students like it …. So?
students who entered the learning dashboard obtain higher study
results?
student
feedback on
STELA learning
skills
dashboard
STELA@KU
Leuven
SO …. IMPROVE LEARNING
impact of
learning tracker
STELA@TU Delft
WaterX
C 1,268 160
MOOC COND. N # PASS PASS RATE
T 1,251 188
12.6%
15.0%
UrbanX
C 771 136
T 746 165
17.6%
22.1%
BusinessX
C 164 46 28.0%
WaterX
C 1,268 160
MOOC COND. N # PASS PASS RATE
T 1,251 188
12.6%
15.0%
UrbanX
C 771 136
T 746 165
17.6%
22.1%
BusinessX
C 164 46
T 160 54
28.0%
33.8%
OVERALL
C 2,203 342
T 2,157 407
15.5%
18.9%
**
learning tracker
improves
course
completion!
SO …. IMPROVE LEARNING
data
actionable
insights
improve
learning
•integrate expertise DURING development
•How to evaluate tools and resources?
•scalability
• impact: small scale – big effect = large scale – small effect
• beyond a single course & beyond online context (MOOCs/SPOCs)
• choice of data
• stimulate flexible software solutions
• open source solutions that can be integrated within existing systems
• reproducible blueprints preferred over highly flexible solutions
• data & software solutions
REC 6:
FOCUS
ON
SCALABI
LITY
STELA@KU Leuven
>1.600 students
SOME CLOSING REMARKS
•Be wary of out-of-the-box commercial solutions!
• no one-size-fits-all solution
• jeopardizes acceptance of students and staff
• What is underlying the recommendation?
→ actionable!
→ transparency!
•Ethics AND privacy are big issues!
• Ethics: involve practitioners and experts
• Privacy regulations can be hurdle
might be opportunity for learning analytics
→ overview and insight in data that IS gathered!
•Keep on funding European projects!
• European collaboration is not always easy, but very stimulating!
• Partners learn from each other and push progress
in their own nations.
Examples:
• ABLE: Leiden pushes for data access to support study advisors
• STELA: Graz pushes for feedback to students
NEED
FOR
CLEAR
NATIONA
L AND
EUROPE
AN
POLICIES
FOR
LEARNIN
G
SOME CLOSING REMARKS
•Be wary of out-of-the-box commercial solutions!
• no one-size-fits-all solution
• jeopardizes acceptance of students and staff
• What is underlying the recommendation?
→ actionable!
→ transparency!
•Ethics AND privacy are big issues!
• Ethics: involve practitioners and experts
• Privacy regulations can be hurdle
might be opportunity for learning analytics
→ overview and insight in data that IS gathered!
•Keep on funding European projects!
• European collaboration is not always easy, but very stimulating!
• Partners learn from each other and push progress
in their own nations.
Examples:
• ABLE: Leiden pushes for data access to support study advisors
• STELA: Graz pushes for feedback to students
NEED
FOR
CLEAR
NATIONA
L AND
EUROPE
AN
POLICIES
FOR
LEARNIN
G
REC 8:
KEEP ON
FUNDING
EUROPE
AN
PROJECT
S
CLOSING
CONCLUSIONS
Learning Analytics
•has GREAT potential
•… but is not an easy-
win!
RECOMMENDATIONS
REC 1: focus on data that is available & think beyond the obvious
REC 2: feedback should always be actionable
REC 3: take learning analytics into account when (re)designing
learning
REC 4: use ALL available expertise
REC 5: provide checklist for evaluating tools and resources
REC 6: focus on scalability
REC 7: need for clear national and European policies for learning
analytics
REC 8: keep on funding European projects
THANK YOU!
•Forward-looking cooperation project:
562167-EPP-1-2015-1-BE-EPPKA3-PI-
FORWARD
•Successful Transition from secondary to
higher Education using Learning Analytics
•KU Leuven (Belgium), TU Delft (Netherlands),
TU Graz (Austria), Nottingham Trent
University (UK), European Society of
Engineering Education (SEFI)
• http://stela-project.eu/
•Strategic Partnership: 2015-1-UK01-KA203-
013767
•Achieving Benefits from Learning Analytics
•Nottingham Trent University (UK), KU Leuven
(Belgium),
Leiden University (Netherlands)
• http://www.ableproject.eu/

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Improving Education by Learning Analtyics (EADTU-EU Summit 2017)

  • 1. IMPROVING EDUCATION BY LEARNING ANALYTICS Tinne De Laet, KU Leuven
  • 2. LEARNING GOALS OF THIS PRESENTATION • You know what learning analytics is. • You can provide at least three examples of learning analytics interventions. • You can look-up the 8 recommendations from this presentation.
  • 3. OUTLINE 1. Who am I? Why I am here? 2. What is learning analytics? 3. Experiences & recommendations
  • 4. WHO AM I? WHY I AM HERE?
  • 5. WHO AM I? WHY I AM HERE? woman engineer Head Tutorial Services Engineering Science KU Leuven . Tinne De Laet associate professor
  • 6. WHO AM I? WHY I AM HERE? Coordinator of STELA Erasmus+ forward-looking cooperation project •Forward-looking cooperation project: 562167-EPP-1-2015-1-BE-EPPKA3-PI- FORWARD •Successful Transition from secondary to higher Education using Learning Analytics •KU Leuven (Belgium), TU Delft (Netherlands), TU Graz (Austria), Nottingham Trent University (UK), European Society of Engineering Education (SEFI) • http://stela-project.eu/ KU Leuven promotor of ABLE Erasmus+ strategic partnership project •Strategic Partnership: 2015-1-UK01-KA203- 013767 •Achieving Benefits from Learning Analytics •Nottingham Trent University (UK), KU Leuven (Belgium), Leiden University (Netherlands) • http://www.ableproject.eu/
  • 8. WHAT IS LEARNING ANALYTICS? no universally agreed definition 8 “the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs” [1] [1] Learning and Academic Analytics, Siemens, G., 5 August 2011, http://www.learninganalytics.net/?p=131 [2] What is Analytics? Definition and Essential Characteristics, Vol. 1, No. 5. CETIS Analytics Series, Cooper, A., http://publications.cetis.ac.uk/2012/521 “the process of developing actionable insights through problem definition and the application of statistical models and analysis against existing and/or simulated future data” [2]
  • 9. WAT IS LEARNING ANALYTICS? 9 [3] 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/ “learning analytics is about collecting traces that learners leave behind and using those traces to improve learning” [Erik Duval, 3] † 12 March 2016 no universally agreed definition
  • 10. IS IT ABOUT INSTITUTIONAL DATA? •high-level figures: provide an overview for internal and external reports; used for organisational planning purposes. •academic analytics: figures on retention and success, used by the institution to assess performance. •educational data mining: searching for patterns in the data. •learning analytics: use of data, which may include ‘big data’, to provide actionable intelligence for learners and teachers. [4] Learning analytics FAQs, Rebecca Ferguson, Slideshare, http://www.slideshare.net/R3beccaF/learning-analytics-fa-qs
  • 13. SO …. DATA data actionable insights improve learning • A lot of data COULD be available • What IS available? • academic performance • strong relation to study success • widely available • digital traces of behavior • card swipes, in-class polls, lab attendance • virtual learning environment • survey data • strong body of knowledge in pedagogic research REC 1: FOCUS ON DATA THAT IS AVAILABL E
  • 14. SO …. DATA - THREE LEVELS any institute - academic performance ABLE@ KU Leuven 14 programs 12 study counsellors >1000 students position students in peer group impact: how similar profiles did in the past guided planning of future study pathway name student
  • 15. SO …. DATA - THREE LEVELS specific context - academic engagement ABLE@NTU >25.000 students
  • 16. SO …. DATA - THREE LEVELS specific context - online courses (MOOCs-SPOCS) STELA@ TU Delft >2.000 students Learning Tracker What is my activity? What was the activity of successful students in the past?
  • 17. SO …. DATA data actionable insights improve learning • combining data that IS available might provide useful insights • data from educational sciences and psychology • self-reported (small data) • standardized tests • example of feedback on learning skills • data from university data warehouse (academic performance) • data from research on skills important for student success REC 1B: THINK BEYOND THE OBVIOUS
  • 18. SO …. DATA What is my position with respect to my peers? STELA@KU Leuven >1.600 students
  • 19. SO …. DATA How well did similar students do in the past? STELA@KU Leuven >1.600 students
  • 20. SO …. ACTIONABLE INSIGHTS data actionable insights improve learning REC 2: FEEDBAC K SHOULD ALWAYS BE ACTIONA BLE “Female students are more successful in higher education than male students” 70% successf 60% successf so ?
  • 21. SO …. ACTIONABLE INSIGHTS engage more with program! engage more with MOOC! work on your learning and studying skills
  • 22. SO …. ACTIONABLE INSIGHTS If Learning Analytics has the potential of improving learning, → (re)design learning such that learning analytics will realize potential data actionable insights improve learning TAKE LEARNIN G ANALYTIC S INTO ACCOUN T WHEN REDESIG NING LEARNIN
  • 23. SO …. ACTIONABLE INSIGHTS None of the students that accessed less then 1o online modules passed the exam. Most successful students finish at least 15 online modules. relation score course - virtual learning environment activity STELA@KU Leuven
  • 24. SO …. IMPROVE LEARNING REC 4: USE ALL AVAILABL E EXPERTI SE •integrate expertise DURING development •educational scientists •computer scientists & IT experts •visualization experts •PRACTITIONERS!!! (study advisors, tutors, etc.) •students & teachers •How to evaluate tools and resources? •scalability data actionable insights improve learning
  • 25. SO …. IMPROVE LEARNING data actionable insights improve learning •integrate expertise DURING development •How to evaluate tools and resources? •Do they improve learning? How can you measure impact? •Is perceived usefulness enough? Is retention the only thing that matters? •scalability REC 5: DEVELOP CHECKLI ST FOR EVALUATI NG TOOLS AND RESOUR CES
  • 26. SO …. IMPROVE LEARNING students like it …. So? students who entered the learning dashboard obtain higher study results? student feedback on STELA learning skills dashboard STELA@KU Leuven
  • 27. SO …. IMPROVE LEARNING impact of learning tracker STELA@TU Delft WaterX C 1,268 160 MOOC COND. N # PASS PASS RATE T 1,251 188 12.6% 15.0% UrbanX C 771 136 T 746 165 17.6% 22.1% BusinessX C 164 46 28.0% WaterX C 1,268 160 MOOC COND. N # PASS PASS RATE T 1,251 188 12.6% 15.0% UrbanX C 771 136 T 746 165 17.6% 22.1% BusinessX C 164 46 T 160 54 28.0% 33.8% OVERALL C 2,203 342 T 2,157 407 15.5% 18.9% ** learning tracker improves course completion!
  • 28. SO …. IMPROVE LEARNING data actionable insights improve learning •integrate expertise DURING development •How to evaluate tools and resources? •scalability • impact: small scale – big effect = large scale – small effect • beyond a single course & beyond online context (MOOCs/SPOCs) • choice of data • stimulate flexible software solutions • open source solutions that can be integrated within existing systems • reproducible blueprints preferred over highly flexible solutions • data & software solutions REC 6: FOCUS ON SCALABI LITY
  • 29. STELA@KU Leuven >1.600 students SOME CLOSING REMARKS •Be wary of out-of-the-box commercial solutions! • no one-size-fits-all solution • jeopardizes acceptance of students and staff • What is underlying the recommendation? → actionable! → transparency! •Ethics AND privacy are big issues! • Ethics: involve practitioners and experts • Privacy regulations can be hurdle might be opportunity for learning analytics → overview and insight in data that IS gathered! •Keep on funding European projects! • European collaboration is not always easy, but very stimulating! • Partners learn from each other and push progress in their own nations. Examples: • ABLE: Leiden pushes for data access to support study advisors • STELA: Graz pushes for feedback to students NEED FOR CLEAR NATIONA L AND EUROPE AN POLICIES FOR LEARNIN G
  • 30. SOME CLOSING REMARKS •Be wary of out-of-the-box commercial solutions! • no one-size-fits-all solution • jeopardizes acceptance of students and staff • What is underlying the recommendation? → actionable! → transparency! •Ethics AND privacy are big issues! • Ethics: involve practitioners and experts • Privacy regulations can be hurdle might be opportunity for learning analytics → overview and insight in data that IS gathered! •Keep on funding European projects! • European collaboration is not always easy, but very stimulating! • Partners learn from each other and push progress in their own nations. Examples: • ABLE: Leiden pushes for data access to support study advisors • STELA: Graz pushes for feedback to students NEED FOR CLEAR NATIONA L AND EUROPE AN POLICIES FOR LEARNIN G REC 8: KEEP ON FUNDING EUROPE AN PROJECT S
  • 32. CONCLUSIONS Learning Analytics •has GREAT potential •… but is not an easy- win!
  • 33. RECOMMENDATIONS REC 1: focus on data that is available & think beyond the obvious REC 2: feedback should always be actionable REC 3: take learning analytics into account when (re)designing learning REC 4: use ALL available expertise REC 5: provide checklist for evaluating tools and resources REC 6: focus on scalability REC 7: need for clear national and European policies for learning analytics REC 8: keep on funding European projects
  • 34. THANK YOU! •Forward-looking cooperation project: 562167-EPP-1-2015-1-BE-EPPKA3-PI- FORWARD •Successful Transition from secondary to higher Education using Learning Analytics •KU Leuven (Belgium), TU Delft (Netherlands), TU Graz (Austria), Nottingham Trent University (UK), European Society of Engineering Education (SEFI) • http://stela-project.eu/ •Strategic Partnership: 2015-1-UK01-KA203- 013767 •Achieving Benefits from Learning Analytics •Nottingham Trent University (UK), KU Leuven (Belgium), Leiden University (Netherlands) • http://www.ableproject.eu/