Presentation of the learning dashboard developed by KU Leuven within the ABLE project (http://www.ableproject.eu/).
Learning dashboard supported by learning analytics, showing off the use of technology for learning in higher education, for the transition of secondary to higher education in particular. The dashboard is developed for the interaction between study advisor and student. More information in our journal paper http://ieeexplore.ieee.org/document/7959628/
4. WHO AM I? WHY I AM HERE?
woman
engineer
Head Tutorial Services
Engineering Science
KU Leuven, Belgium
.
Tinne De Laet
associate
professor
5. WHO AM I? WHY I AM HERE?
Head of Tutorial Services of Engineering
Science KU Leuven
• Daily experiences of challenges in transition from
secondary to higher education
• looking for opportunities for cross-fertilization
between “first-year experience” and “engineering”
•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/
KU Leuven promotor of ABLE
Erasmus+ strategic partnership project
7. WHAT IS LEARNING ANALYTICS?
no universally agreed definition
7
“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]
8. WAT IS LEARNING ANALYTICS?
8
[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
9. 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
11. SPECIFIC CONTEXT
• open admission in the Flemish (Belgium) higher education system
→ a substantial part of first-year students enters without the right qualifications
→ bachelor drop-out rate of around 40% in the Faculties of Science & Technology at KU
Leuven.
• university invests in advising students before and throughout the first-year
• study career guidance by study advisors assigned to particular program
• study advisors are professionals, trained in coaching students
• BUT study advisors too often “drive blind”
hard to gather data that could support the advising session
13. METHODOLOGY
1. Development
• need analysis with five study advisors and brainstorm sessions
• user-centred, rapid-prototyping design approach
• six iterations: four digital mock-ups and two functional dashboards
• two visualisation experts & head of the Tutorial Services of Engineering Science
2. Result = LISSA
→ Learning dashboard for Insights and Support during Study Advice
3. Evaluation with students & study advisors
22. THE EVALUATION
Presentations to 17 study advisors for preliminary
feedback
• data-supported evidence
• LISSA should not be used without a study advisor’s guidance.
• LISSA should remain secondary to the experience and expertise of
study advisor
• decrease in workload: no need to “look for data” and
manual preparations
• data transparancy
• open and honest approach
• some concern around student protesting againts difficulty of a course
“with the facts visualised it is easier to
convince the student”
“certain myths exist with students,
like how some people
manage to successfully complete
seven exams during re-sits”.
23. THE EVALUATION
97 sessions, interview of study advisors,
15 sessions observed
LISSA supporting a personal dialogue
• the level of usage depends on the experience of the
study advisors
• fact-based evidence at the side
• narrative thread
• key moments and student path help to reconstruct
personal track
“When I show them the number of students that
succeed seven or eight exams, 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”.
“I have changed my study method in June and
now see it paid off.”
26. DISCUSSION AND LESSONS LEARNT
• Learning Analytics data
• only data that is available at any higher education institute is used
• now easily available to study advisors
• Visualisation
• minimal design without animations as LISSA is a mere supportive tool, allowing focus on the contextualisation and not on the
dashboard features itself
• visual encoding of information provide fast and easy overview (e.g. color coding of failed courses)
• Study advisor
• The role of the study advisor is key: critical and reflective interpretation, coaching
• LISSA leaves room for personal opinion and tacit experience
• Transparancy
• LISSA uses data transparently
• not always desired to use with student with really bad results (demotivation), but can also motivate students
• comparison to peers is not considered desired by all study advisors
• senstive nature of histograms of courses
27. CONCLUSION AND REFLECTIONS
• study advisors and student “like” LISSA
• LISSA is a mere supportive tool
• expertise of study advisors is key
Future
• repeat interventions and extend (KU Leuven and Leiden University)
• extend dashboard with other student data
looking how student background data can be integrated in an ethical manner
• qualitative analysis using focus groups and structured interviews with students
