Successful transition from secondary to higher education using learning analytics

Successful transition from
secondary to higher
education using learning
analytics
TINNE DE LAET, TOM BROOS (KU LEUVEN)
JAN-PAUL VAN STAALDUINEN (TU DELFT)
PHILIPP LEITNER, MARTIN EBNER (TU GRAZ)
1
STELA - Erasmus+ Project
562167-EPP-1-2015-1-BE-EPPKA3-PI-FORWARD

Welcome!
Who are we?
 Tinne De Laet ( KU Leuven)
 Head of Tutorial Services of Engineering Science
promotor STELA
background = mechanical engineer
 Tom Broos (KU Leuven)
 Doctoral researcher STELA
 Philipp Leitner (TU Graz)
 Doctoral researcher STELA
2

Welcome
 Would you please introduce yourself to the people on your table
 Who are you?
 How familiar are you with learning analytics?
 Is your institute applying learning analytics?
3

Agenda for workshop
 Welcome
 What is learning analytics? (10 minutes)
 STELA project:
 Presentation of project goal & challenges (2 minutes)
 Group discussion: how could learning analytics help? (10 minutes)
 STELA project:
 Presentation of project ideas (10 minutes)
 Group discussion on project ideas (10 minutes)
 Learning analytics technology (10 minutes)
 Groups present their remarks, suggestions, … (10 minutes)
 Wrap up, goodbye (3 minutes)
4

Agenda for workshop
…. so what we expect from you
 participate in discussion in groups
 each group gathers 3 ideas, thoughts, remarks, suggestions, ..
 present them to each other at end of the workshop
5

What is learning analytics?
6

What is Learning Analytics?
 no universally agreed definition
“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]
7

 no universally agreed definition
[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
8

How is learning analytics different from institutional data? [4]
 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
9

Different level of analytics
adapted from http://www.slideshare.net/gsiemens/learning-analytics-educause
level beneficiaries
course-level learners, teachers, faculties
aggregate learners, teachers, tutors, counsellors, faculties
institutional administrators, funders, marketing
regional administrators, funders, policy makers
national and
international
national and international governments, policy
makers
10

data visualization versus predictive analytics
 is showing data enough?
 how to show data to create sense-making and impact?
 is predicting study success/drop out the only thing that matters?
 can both be combined?
11

learning analytics process model
[Verbert et al. 2013] Verbert K, Duval E, Klerkx J; Govaerts S, Santos JL (2013) Learning analytics dashboard
applications. American Behavioural Scientist, 10 pages. Published online February 201, doi:
10.1177/0002764213479363
12

how to evaluate learning analytics?
 is perceived usefulness enough?
 is increased self-awareness enough? How will you measure this?
 is increased sense-making enough? How will you measure this?
 how could impact be measured?
13

six critical dimensions of learning analytics
[Greller, W., & Drachsler, H. (2012). Translating Learning into Numbers: A Generic Framework for Learning
Analytics. Educational Technology & Society, 15 (3), 42–57. http://ifets.info/journals/15_3/4.pdf ]
14

six critical dimensions of learning analytics
Tutors
Policy makers
15

 data subjects
 here: students
(could also be teachers)
 data clients
 students
 tutors
 academic administrators
 policy makers
16

 what are the objectives?
 is it about reflection or prediction?
 is showing data enough?
 how to show data to create self-awareness, sense-making
and impact?
17

 which data is available?
 is it open? protected?
 is it ethical to use the data?
18

 Technology, algorithms, theories are at the basis of
learning analytics
 open source or as close as possible to (proprietary)
university systems?
 pedagogic theories for supporting students
19

• conventions: ethics, personal
privacy, and similar socially
motivated limitations
• norms: restricted by laws or specific
mandated policies or standard
ethics and privacy IS a big issue
20

• competences: application of
learning analytics requires new
higher-order competences to
enable fruitful exploitation in
learning and teaching
• acceptance: acceptance factors
can further influence the
application or decision making that
follows an analytics process
21

Agenda for workshop
 Welcome
 STELA project:
 Group discussion: how could learning analytics help? (10 minutes):
22

STELA project
Project goal & challenges
23

STELA project
 focus: transition from secondary to higher education
(summer before entering university + 1st year experience)
 goal: applying learning analytics to support transition
 student-centered
 additional focus to tutors, student counsellors
1. beyond identifying at-risk students
 inclusive approach: feedback for every student
2. beyond single course
 entire program
3. provide actionable feedback
 ability to remediate based on feedback
24

STELA project
Group discussion: how could
learning analytics help?
(10 minutes)
25

Group discussion: how could learning
analytics help? (10 minutes)
 What are challenges in the transition from secondary to higher
education?
 Which actionable feedback could support students in the
transition?
 How could learning analytics help in this matter?
26

Agenda for workshop
 Welcome
 STELA project:
27

STELA project: project ideas
four areas of focus
1. academic performance
2. academic engagement
3. academic skills
4. academic well-being
How?
 position students with respect to peers
 show impact on future study pathway
→ show how students with similar profile did in the past
28

(1/4) academic performance
29
data (often) easily available
present at all universities
after the facts
strong relation performanceT-1 ~ performance
Source: ABLE project (2015-1-UK01-KA203-013767)

(2/4) academic engagement
30
depends on context
• e.g. card swipes at Nottingham Trent University,
• learning platform activity at KU Leuven.
relation engagement ~ performance not always
straightforward.
data not always available

(3/4) academic skills
31
Number of peers:
Motivation Score
Very high
PerformanceAnxiety
High
Average
Low
Very Low
Very weak Weak Average High Very High
Recommendations
First aid for stress workshop at Student Health Center
Talk to a student counselor.
often self-reported
tests and questionnaires available form pedagogy
and psychology
• Time management, motivation, persistence, …
opportunity to move from information to
recommendation

(4/4) academic well-being
32
How do you feel about this course now?
Last week, others felt like this:
opportunity to complement with continuous, bi-
directional feedback
tests and questionnaires available form pedagogy
and psychology

Agenda for workshop
 Welcome
 STELA project:
33

Learning analytics
technology
34

Learning Analytics technology
Challenges
 multiple and different kind of data-sources
 lots of data
 need to be combined and analyzed
 scalable
 real-time?
 visualized
 ...
35

Learning Analytics technology 36
Concept

(1/3) Data Collection: Logstash¹
37
 flexbile and open source (Apache 2 Open Source License)
 data collection, enrichtment and transportation pipeline
 efficiently process log, event and unstructured data sources
¹) https://www.elastic.co/products/logstash

(2/3) Search and Analytics Engine: Elasticsearch²
38
 distributed, open source search and analytics engine
 scalability, reliability, and easy management
 developer-friendly query language covering structured,
unstructured, and time-series data
²) https://www.elastic.co/de/products/elasticsearch

(3/3) Visualization: Kibana³ or Grafana4
39
3
) https://www.elastic.co/products/kibana
4
) http://grafana.org/
 open source data visualization platform
 interact with data through powerful graphics
 easy extensibility and a variety of panels

Agenda for workshop
 Welcome
 STELA project:
40

Groups present their
remarks, suggestions, …
41

Wrap up, goodbye
42

Successful transition from secondary to higher education using learning analytics

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Successful transition from secondary to higher education using learning analytics