👂🎴 🕸️ ⊞
Each
student
randomly
picks
up
one
sticker
.
To
each
sticker
type
''
point
value
is
associated
.
You
have
15
minutes
to
do
whatever
You
want
(
e
.
g
.
deception
''
corruption
''
seduction
etc
.
*).
After
15
minutes
''
the
person
(
or
student
group
)
which
managed
to
collect
stickers
with
highest
sum
of
points
will
be
allowed
to
establish
one
rule
which
the
whole
class
will
follow
during
rest
of
semester
.<
div
><
br
>
div
><
div
>*
the
only
thing
prohibited
is
violence
div
>
All
those
who
have
a
UdK
account
''
log
in
here
*:<
br
/><
br
/><
a
href
=
„
https
://
medienhaus
.
udk
-
berlin
.
de
/
login
“
target
=
„
blank
“
rel
=
„
noopener
“
>
https
://
medienhaus
.
udk
-
berlin
.
de
/
login
a
><
br
/> <
br
/>
and
subsequently
join
the
course
(#
edu
-
intelligence
)
room
:<
br
/> <
br
/><
a
href
=
„
https
://
medienhaus
.
udk
-
berlin
.
de
/
classroom
/#/
room
/#
edu
-
art
-
cognition
:
medienhaus
.
udk
-
berlin
.
de
“
target
=
„
blank
“
rel
=
„
noopener
“
>
https
://
medienhaus
.
udk
-
berlin
.
de
/
classroom
/#/
room
/#
edu
-
art
-
cognition
:
medienhaus
.
udk
-
berlin
.
de
a
><
br
/><
br
/>(
or
install
matrix
client
apps
like
Element
or
Fluffychat
and
put
„
medienhaus
.
udk
-
berlin
.
de
“
as
homeserver
)
<
p
class
=
„
fragment
“
>
who
am
I
p
><
p
class
=
„
fragment
“
>
who
are
You
p
><
p
class
=
„
fragment
“
>
is
this
a
course
for
You
?
p
><
p
class
=
„
fragment
“
>
credits
(
2
ECTS
for
>
75
%
attendance
''
+
1
for
referat
/
Congress
contribution
''
+
2
Hausarbeit
)
p
><
p
class
=
„
fragment
“
>
Leistungsnachweis
p
><
p
class
=
„
fragment
“
>
signature
-
related
issues
p
><
p
class
=
„
fragment
“
>
Feedback
box
p
><
p
class
=
„
fragment
“
>
Congress
p
>
Please
answer
(
anonymously
)
on
the
piece
of
paper
at
least
one
among
following
questions
:<
p
class
=
„
fragment
“
>
1
.
What
did
You
learn
?
p
><
p
class
=
„
fragment
“
>
2
.
What
did
You
like
?
p
><
p
class
=
„
fragment
“
>
3
.
What
did
disturb
You
?
p
><
p
class
=
„
fragment
“
>
4
.
What
did
You
not
like
?
p
>
and
throw
it
into
Feedbackbox
.
<
p
class
=
„
fragment
“
>
TAKEN
Chapter
3
(
Babies
'
Invisible
Knowledge
)
and
4
(
The
birth
of
a
brain
)
from
Dehaene
'
s
„
How
we
Learn
“
p
><
p
class
=
„
fragment
“
>
TAKEN
Chapter
5
(
Nurture
'
s
Share
)
and
6
(
Recycle
Your
Brain
)
from
Dehaene
'
s
„
How
we
Learn
“
p
><
p
class
=
„
fragment
“
>
Chapter
7
(
Attention
)
and
8
(
Active
Engagement
)
from
Dehaene
'
s
„
How
we
Learn
“
p
><
p
class
=
„
fragment
“
>
Chapter
9
(
Error
Feedback
)
and
10
(
Consolidation
)
from
Dehaene
'
s
„
How
we
Learn
“
p
><
p
class
=
„
fragment
“
>
AI
unplugged
activity
-
Classification
with
Decision
Trees
p
><
p
class
=
„
fragment
“
>
AI
unplugged
activity
-
#
deeplearning
p
><
p
class
=
„
fragment
“
>
AI
unplugged
activity
-
Reinforcement
learning
p
><
p
class
=
„
fragment
“
>
Non
-
human
learning
(
plants
''
animals
etc
.)
p
><
p
class
=
„
fragment
“
>
Un
-
learning
&
„
altered
“
learning
.
p
>
„
Man
is
a
'
homo
discens
,'
a
learning
being
.
People
learn
as
long
as
they
live
.
Life
is
inseparably
connected
with
learning
.
“
Horst
Siebert
<
br
>
<
div
>
From
Proto
-
Italic
*
diskō
''
from
earlier
*
dikskō
''
from
Proto
-
Indo
-
European
*
di
-
dḱ
-
ské
-
ti
''
derived
from
the
root
root
*
deḱ
-
(“
t
<
strong
>
o
take
strong
>”).
From
the
same
root
as
doceō
and
discipline
unrelated
to
discipulus
.
div
><
br
><
div
> 
div
>
<
p
class
=
„
fragment
“
>
What
did
You
learn
today
?
p
><
p
class
=
„
fragment
“
>
What
was
the
most
important
thing
You
learned
this
year
?
p
>
Implicit
learning
is
the
process
of
acquiring
knowledge
or
skills
unconsciously
''
without
intentional
effort
or
explicit
awareness
of
what
is
being
learned
.
It
typically
occurs
through
repeated
exposure
to
patterns
''
stimuli
''
or
behaviors
''
allowing
individuals
to
internalize
rules
or
structures
without
being
able
to
articulate
them
directly
.
Observation
in
the
context
of
learning
refers
to
the
process
of
acquiring
knowledge
''
skills
''
or
behaviors
by
perceiving
the
actions
of
others
or
the
dynamics
within
an
environment
.
This
learning
occurs
without
direct
engagement
but
through
experiencing
of
external
stimuli
''
events
''
or
behaviors
.
<
br
>
Unsupervised
learning
is
a
type
of
machine
learning
where
a
model
is
trained
on
data
without
labeled
outcomes
.
The
system
analyzes
and
identifies
patterns
or
structures
in
the
data
''
such
as
clustering
or
associations
''
without
explicit
guidance
on
what
to
look
for
.
It
is
often
used
for
tasks
like
anomaly
detection
''
clustering
''
and
dimensionality
reduction
.<
br
>
<
p
class
=
„
fragment
“
><
strong
>
No
direct
guidance
strong
>
::
Learner
L
must
identify
patterns
or
relationships
from
raw
input
on
her
own
.
p
><
p
class
=
„
fragment
“
><
strong
>
Pattern
discovery
strong
>
::
Observation
often
involves
recognizing
and
understanding
patterns
in
the
environment
''
which
mirrors
what
happens
in
unsupervised
learning
.
p
><
p
class
=
„
fragment
“
><
strong
>
Learning
from
the
environment
strong
>
::
L
derives
insights
from
the
world
or
data
without
external
labels
or
supervision
.
p
>
Imitation
in
humans
''
especially
in
children
''
is
the
process
of
learning
by
observing
and
replicating
the
actions
''
behaviors
''
or
expressions
of
others
.
It
is
a
fundamental
mechanism
for
acquiring
social
''
cognitive
''
and
motor
skills
''
allowing
children
to
mimic
gestures
''
language
''
or
problem
-
solving
techniques
.
Through
imitation
''
children
learn
cultural
norms
''
communication
patterns
''
and
even
complex
tasks
without
explicit
instruction
.
It
is
crucial
for
early
development
''
as
it
helps
children
integrate
into
their
social
environment
and
build
understanding
by
modeling
behaviors
they
see
in
parents
''
peers
''
and
others
.
Learning
is
building
A
model
of
THE
world
.
Experiential
learning
is
a
process
of
learning
through
direct
experience
''
where
individuals
engage
in
activities
''
reflect
on
their
actions
''
and
apply
what
they
’
ve
learned
to
new
situations
.
Rather
than
solely
reading
or
listening
''
learners
actively
participate
''
often
experimenting
''
making
mistakes
''
and
adapting
.<
br
>
In
learning
and
problem
-
solving
''
an
„
error
“
is
the
difference
between
what
we
aimed
to
achieve
and
the
actual
result
.
It
shows
how
far
we
are
from
the
desired
outcome
and
helps
us
understand
what
needs
adjusting
.
Errors
aren
’
t
failures
;
they
’
re
valuable
feedback
''
guiding
us
to
make
improvements
in
each
new
attempt
.
By
identifying
and
reducing
errors
through
practice
and
adjustments
''
we
gradually
get
closer
to
the
goal
.
In
this
way
''
errors
are
essential
to
learning
''
as
they
highlight
what
doesn
’
t
work
and
point
us
toward
what
might
.
In
learning
and
problem
-
solving
''
a
„
trial
“
is
a
single
attempt
or
effort
to
reach
a
goal
or
find
a
solution
.
Each
trial
involves
testing
an
idea
or
making
a
change
''
then
observing
the
result
.
If
the
trial
doesn
’
t
succeed
''
adjustments
are
made
based
on
what
was
learned
''
and
a
new
trial
begins
.
This
process
''
called
„
trial
and
error
''
“
continues
until
the
desired
outcome
is
achieved
.
Trials
are
key
in
learning
because
they
allow
us
to
explore
''
adapt
''
and
improve
with
each
attempt
''
reducing
mistakes
and
moving
closer
to
success
over
time
.
In
learning
''
skill
-
building
''
and
habit
formation
''
repetition
is
the
process
of
repeatedly
practicing
or
performing
a
task
.
This
continuous
repetition
reinforces
memory
''
builds
familiarity
''
and
over
time
''
transforms
skills
into
habits
or
reflexes
''
making
actions
more
automatic
and
effortless
.
Through
repetition
''
connections
in
the
brain
are
strengthened
''
allowing
tasks
to
be
completed
with
less
conscious
effort
. 
Game
provides
a
closed
system
whereby
we
are
allowed
to
commit
errors
(&
learn
from
them
)
without
major
consequences
for
real
life
.<
p
class
=
„
fragment
“
>
What
is
Your
most
favorite
game
/
way
of
playing
?
p
><
p
class
=
„
fragment
“
>
Any
game
You
would
like
to
bring
&
play
with
Your
colleagues
during
the
Congress
?
p
>
Supervised
learning
is
a
type
of
machine
learning
where
a
model
is
trained
on
labeled
data
to
learn
the
mapping
between
input
features
and
corresponding
outputs
.
The
goal
is
to
enable
the
model
to
make
accurate
predictions
or
classifications
on
unseen
data
by
minimizing
the
error
between
its
predictions
and
the
true
labels
.
Common
tasks
include
regression
(
predicting
continuous
values
)
and
classification
(
assigning
categories
).
Supervised
learning
relies
on
a
training
dataset
with
known
inputs
and
outputs
and
evaluates
performance
using
a
separate
test
dataset
.
Examples
include
spam
email
detection
''
image
recognition
''
and
speech
-
to
-
text
systems
.
Features
are
observable
and
measurable
properties
or
characteristics
used
to
describe
data
in
both
machine
learning
and
human
experience
. <
div
><
br
>
div
><
div
>
In
ML
''
features
are
input
variables
—
raw
(
e
.
g
.''
pixel
intensities
''
audio
waveforms
)
or
engineered
(
e
.
g
.''
embeddings
''
statistical
summaries
)—
that
models
use
to
make
predictions
. <
div
><
br
>
div
><
div
>
In
human
experience
''
features
represent
sensory
or
cognitive
details
like
color
''
texture
''
pitch
''
or
emotional
tone
''
helping
interpret
and
navigate
the
world
.
div
>
div
>
Classifiers
Training
Validating
Testing
Reinforcment
learning
Social
learning
Peer
learning
Machine
Learning
Human
-
Machine
Peer
Learning
Teaching
''
Pedagogy
''
Didactics
Artificial
Teacher
Avatars
Educational
Systems
Extended
Educational
Environments
The
Congress
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