st
VISLA15:
1
international
workshop
on
Visual
Aspects
of
Learning
Analytics
organized
at
the
5th
international
Learning
Analytics
and
Knowledge
conference
(LAK15)
The
use
of
visualization
techniques
for
learning
is
not
new.
For
instance,
visualizations
have
been
used
in
maps
and
drawings
for
thousands
of
years.
In
a
learning
analytics
context,
the
application
of
information
visualization
techniques
can
help
both
teachers
and
learners
to
explore
and
understand
relevant
user
traces
that
are
collected
in
various
(online)
environments
and
to
improve
(human)
learning.
The
goal
of
our
workshop
is
to
build
a
strong
research
capacity
around
visual
approaches
to
learning
analytics.
The
longer
term
goal
is
to
improve
the
quality
of
learning
analytics
research
that
relies
on
information
visualization
techniques.
Each
contribution
to
the
workshop
explicitly
addressed
the
following
items:
1. What
kind
of
data
is
being
visualized?
What
tools
were
used
to
clean
up
the
data
(if
any)?
2. For
whom
are
the
visualizations
intended
(learner,
teacher,
manager,
researcher,
other)?
3. How
is
data
visualized?
Which
interaction
techniques
are
applied?
What
tools,
libraries,
data
formats,
etc.
are
used
for
the
technical
implementations?
What
workflow
and
recipe
was
used
to
develop
the
visualization?
4. Why
are
the
chosen
visual
approaches
applied
(i.e.
rationale
behind
the
application
of
a
visualization)?
5. How
has
the
approach
been
evaluated
or
how
could
it
be
evaluated?
6. What
were
the
encountered
problems
and
pitfalls
during
the
visualization
process?
The
workshop
is
intended
for
anyone
who
is
using,
or
is
interested
in
visualization
techniques
to
support
learning
analytics.
The
goal
of
our
workshop
is
to
build
a
strong
research
capacity
around
visual
approaches
to
learning
analytics.
The
longer
term
goal
is
to
improve
the
quality
of
learning
analytics
research
that
relies
on
information
visualization
techniques.
During
our
1-‐day
workshop,
we
aimed
to
facilitate
a
very
interactive
and
engaging
event
where
we
wanted
to
avoid
death
by
powerpoint
by
all
means
and
promote
discussion
activities
over
presentational
ones.
In
the
first
half
of
the
workshop,
we
therefore
asked
participants
to
shortly
present
the
work
of
another
submission
and
to
relate
it
back
to
their
own
work.
During
the
second
half
of
the
workshop,
we
invited
the
participants
to
share
their
tools,
workflows
and
recipes
in
a
hands-‐on
discussion
session
so
that
they
could
benefit
from
each
others’
knowledge,
apply
their
visual
approaches
on
either
their
own
dataset
or
on
a
dataset
that
we
provided.
Finally,
we
moved
the
discussion
to
the
final
topic
of
the
workshop,
which
is
the
development
of
the
equivalent
of
the
VAST
challenge
for
learning[1],
which
was
linked
back
with
the
LAK14
and
LAK15[2]
data
challenge:
“The
annual
Visual
Analytics
Science
and
Technology
(VAST)
challenge
provides
Visual
Analytics
researchers,
developers,
and
designers
an
opportunity
to
apply
their
best
tools
and
techniques
against
invented
problems
that
include
a
realistic
scenario,
data,
tasks,
and
questions
to
be
answered.
Submissions
are
processed
much
like
conference
papers,
contestants
are
provided
reviewer
feedback,
and
excellence
is
recognized
with
awards.
A
day-‐long
VAST
Challenge
workshop
takes
place
each
year
at
the
IEEE
VAST
conference
to
share
results
and
recognize
outstanding
submissions.”
The
VISLA15
organizers
Erik
Duval,
Joris
Klerkx,
Katrien
Verbert,
KU
Leuven,
Belgium
Martin
Wolpers,
Fraunhofer-‐Institute
for
Applied
Information
Technology
FIT,
Germany
Abelardo
Pardo,
University
of
Sydney,
Australia
Sten
Govaerts
&
Denis
Gillet,
EPFL,
Switzerland
Xavier
Ochoa,
ESPOL,
Ecuador
Denis
Parra,
PUC,
Chile
[1]
K.
Cook,
G.
Grinstein,
and
M.
Whiting.
The
vast
challenge:
history,
scope,
and
outcomes:
An
introduction
to
the
special
issue.
Information
Visualization,
13(4):301–312,
2014.
[2]
H
.
Drachsler,
S.
Dietze,
E.
Herder,
M.
d’Aquin,
and
D.
Taibi.
The
learning
analytics
&
knowledge
(lak)
data
challenge
2014.
In
Proceedings
of
the
Fourth
International
Conference
on
Learning
Analytics
And
Knowledge,
LAK
’14,
pages
289–290,
New
York,
NY,
USA,
2014.
ACM
Table
of
contents
Visualizing
Uncertainty
in
the
Prediction
of
Academic
Risk
4
Xavier
Ochoa
Using
Sentence
Compression
to
Develop
Visual
Analytics
for
Student
Responses
11
to
Short
Answer
Questions
Aneesha
Bakharia
and
Shane
Dawson
Getting
a
Grasp
on
Tag
Collections
by
Visualising
Tag
Clusters
Based
on
Higher-‐ 14
order
Co-‐occurrences
Katja
Niemann,
Maren
Scheffel,
Sarah
León
Rojas,
Martin
Wolpers,
Hendrik
Drachsler
and
Marcus
Specht
A
Network
Based
Approach
for
the
Visualization
and
Analysis
of
Collaboratively
19
Edited
Texts
Tobias
Hecking
and
H.
Ulrich
Hoppe
INSIGHT:
a
Semantic
Visual
Analytics
for
Programming
Discussion
Forums
24
Piyush
Awasthi
and
I-‐Han
Hsiao
Exploring
Inquiry-‐Based
Learning
Analytics
through
Interactive
Surfaces
32
Sven
Charleer,
Joris
Klerkx
and
Erik
Duval
Uncovering
Learning
Processes
Using
Competence-‐based
Knowledge
36
Structuring
and
Hasse
Diagrams
Michael
Kickmeier-‐Rust,
Christina
M.
Steiner
and
Dietrich
Albert
LAK
Explorer
–
A
Fusion
of
Search
Tools
41
Mike
Sharkey,
Mohammed
Ansari
and
Andy
Nguyen
Discovering
Learning
Antecedents
in
Learning
Analytics
Literature
45
Vladimer
Kobayashi,
Stefan
Mol
and
Gábor
Kismihók