Successful learning can take place when the learner is addressed at all levels of learning instead of limiting teaching to knowledge transfer but also involving an emotional and skills level. Considering this as the student-centered approach, we designed, carried out and revised in practice computer science lessons in 9th grade classrooms. During these real classroom experiences we identified certain successful scenarios when such learning was effective. We subsequently transformed scenarios to a more abstract representation and obtained as a result 24 patterns, which uniformly describe how student-centered lessons in computer science can be carried out. The patterns don’t specify detailed instructions for the teacher but still hold all the information necessary to be coherent with the pedagogical approach in the context of computer science. Instead of providing a detailed description of lesson plans and exact scenarios, the patterns describe how different teaching procedures can be approached alongside the student-centered approach. The advantage of this representation is, that it leaves the freedom of individual implementation to the teacher. In order to prove the concept of the patterns four case studies in classrooms were carried out with the design-based research approach as driving force combined with mixed methods as questionnaires, classroom meetings, and audio recordings. Outcomes showed, that these patterns have impact on students’ perception of the teacher’s attitudes. Furthermore, we identified detailed aspects of students’ communication characteristics during problem solving processes. In a next step, these patterns were further applied during a research visit in the United States in the context of computational thinking problem solving tasks. Assuming that problem solving processes can be found in everyday occurrences, computational thinking problem solving skills affect everyone and should be part of a general knowledge every person should have these days. Therefore, we combined the patterns with computer science algorithms in the context of everyday life settings and designed lesson scenarios for four high school classes. These classroom activities were accompanied with the mixed research approach and case studies. First results of this study showed, that students improved required skills for computational problem solving.

1. Student-­‐centered
teaching
pa0erns
in
the
context
of
computa6onal
thinking
problem
solving
processes
Bernhard
Standl

2. Main
Goal
„How
can
computer
science
lessons
be
designed
and
carried
out
for
inspiring
students
to
experience
computer
science
as
an
exci6ng
subject?“

3. Field
l Thesis
lays
in
the
intersec6on
of
different
domains
l Each
domain
contributes
to
the
thesis
and
the
thesis
contributes
to
each
domain.
l This
field
can
be
iden6fied
as
„Subject
Didac6cs“
Computer
Science
Research
in
CS
Educa6on
Pedagogy
CS
Classroom
Prac6ce
Field

4. Reusability
Common
cartridge
standard
Website
Research
Design-­‐based
approach
Case
studies
Mixed
methods
Abstrac6on
of
scenarios
to
pa0erns
Pa0ern
form
UML
class
diagram
Iden6fica6on
of
successful
lesson
scenarios
Ac6vity
diagrams
Teacher
reflec6on
Defini6on
of
pedagogical
approach
&
content
Person-­‐centered
approach
CS
curriculum
9th
grade
Process
l From
general
pedagogical
theories
to
a
subject
didac6cs
for
computer
science
at
9th
grade
l From
classroom
prac6ce
to
pa0erns
for
a
be0er
reusability

5. Pedagogical
Background
l If
the
teacher
holds
certain
interpersonal
quali6es
and
students
perceive
them
at
least
to
a
certain
degree,
learning
can
be
more
likely
significant.
è Learning
can
be
more
significant,
if
the
learner
is
addressed
at
all
levels
of
learning
(Rogers,
1983).
Knowledge
and
social
skills
are
integral
parts
of
the
learning
process
but
the
fundament
is
built
by
the
level
of
personality
and
rela6onships.
The
aproach
chosen
for
this
thesis
is
the
Person-­‐centered
approach.

6. 1
Pädagogische
Lehrpa0erns
für
den
Informa6kunterricht
Person-­‐zentrierter
Ansatz
nach
Carl
Rogers

7. Iden6fica6on
of
successful
scenarios
l A
lesson
plan
is
improved
in
prac6ce
through
a
process
of
applica6on,
capturing
and
refinement.
è
The
teacher
iden6fies
in
a
itera6ve
process
of
planning-­‐
enactment-­‐refinement
successful
scenarios
Students‘
feedback
and
the
teacher‘s
self
reflec6on
lead
to
a
selec6on
of
successful
scenarios.
Ini6al
plan
Carry
out
Capture
Scenario
Pedagogy
CS
Subject
Content
Computer
Science
Lesson

8. Pa0ern
Development
Pa0ern
Scenarios
Lesson
plans
&
theory
Teacher’s
reflec6ons
&
experience
è
A
pa0ern
emerges
from
ini6al
lesson
plans,
experiences,
student
feedback
and
tes6ng
in
prac6ce.
For
example
was
the
pa0ern
on
group
work
developed
based
on
different
experiences
made
in
prac6ce.

9. Pa0erns
zur
Unterrichtsplanung
l Pa0erns
entstanden
im
Beratungsfeld
von
Informa6kern,
Fachdidak6kern,
Pädagogen
und
Psychologen
l Ein
Pa0ern
als
eine
bewährte,
generische
Lösung
für
ein
immer
wiederkehrendes
Problem,
das
in
bes6mmten
Situa6onen
aueri0.

10. Pa0erns
l Alexander‘s
approach
l Has
a
strong
emphasis
on
the
network
context
l Idea
from
smaller
and
bigger
pa0erns
l Derntl‘s
approach
l Is
aimed
at
eLearning
at
ter6ary
level
l Includes
also
dynamic
elements
as
ac6vity
diagrams
l Intruduces
idea
of
including
UML
è
A
pa0ern
is
a
abstract
descrip6on
of
a
solu6on
for
a
problem
of
an
aspect
of
a
student-­‐centered
computer
science
lesson.
The
pa0ern
approach
used
for
this
work
rests
on
the
form
of
C.
Alexander
(1977)
with
ideas
of
M.
Derntl
(2006).

11. Pa0erns
Parts
of
a
pa0ern
l Intent
l Dependencies
l Problem
l Forces
l Solu6on
l UML
Class
Diagram
l Example
for
classroom
prac6ce
è
The
pa0ern
structure
as
chosen
for
this
work
is
aimed
at
a
high
reusability
for
other
teachers.

16. Pa0erns
in
der
Unterrichtspraxis

17. Von
Pa0erns
zur
Unterrichtseinheit
1. Ausgangspa0erns
sind
Management
und
Mo4va4on
2. Darauf
aukauend
wird
eine
Tabelle
mit
den
Spalten
Zeit,
Inhalt,
Ak4on
und
Pa0ern
erstellt.
3. Zuerst
werden
die
ersten
drei
Spalten
wie
gewohnt
ausgefüllt.
Die
einzige
Auflage
ist,
dass
die
Anordnung
den
Pa0erns
Management
und
Mo6va6on
entspricht
4. Danach
wird
jeder
Zeile
ein
passender
Pa0ern
zugeordnet.

18. Beispiel

19. Unterrichtsforschung

20. Instrumente
l Fragebogen
l Haltungen
des
Lehrers
à
deskrip6ve
Auswertung
l Feedbacksheets
l Erhebung
von
Vorstellungen
zu
Koopera6on
à
Inhaltsanalyse
und
Frequenzzählung
l Interak6onsanalyse
l Audioaufzeichnung
à
Inhaltsananalyse
und
Kategorisierung
l Classroom
Mee6ng
l Protokoll
à
qualita6ve
Auswertung
der
Reflexion
Research
approach
Research
design
Research
instruments

21. Forschungssenng
forward(100)
left(90)
forward(100)
left(90)
forward(100)
left(90)
forward(100)

22. Ergebnisse
Haltungen
des
Lehrers
2.70
3.32
3.64
2.48
2.32
2.39
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
5.00
October
February
June

23. Case
Study:
Mo6va6on
for
Computer
Science
Results
è
This
case
study
inves6gates
the
impact
of
the
person-­‐
centered
classroom
organiza6on
on
students'
mo6va6on
for
computer
science.
l Students
were
asked
with
ques6onnaires
in
a
pre-­‐post
research
senng,
enhanced
by
qualita6ve
feedbacksheets
at
the
end
of
the
year.
1:
low
mo6va6on
for
computer
science
5:
very
high
mo6va6on
for
computer
science
2.20
2.23
2.60
1.59
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
5.00
Interven6on
Control
Pre
Post

24. CS
Results
2.31
2.25
2.21
2.10
1.64
2.18
2.59
2.36
2.20
2.28
2.11
1.68
2.34
3.18
2.22
3.56
3.27
3.44
2.57
2.66
2.31
1.33
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
5.00
1)
I
know
how
to
operate
a
computer
2)
I
spend
much
6me
on
the
computer
3)
I
am
interested
in
working
with
technical
equipment
4)
I
think
working
on
the
computer
is
no
stress
5)
I
have
experience
with
a
programming
language
6)
I
think
that
computer
science
is
an
important
subject
7)
I
think
the
computer
science
lessons
are
exci6ng
8)
I
like
to
solve
problems
9)
I
like
to
think
in
a
structural
way
10)
I
think
I
have
to
learn
a
lot
for
computer
science
11)
I
think
computer
science
is
a
boring
subject
Pre
Post
1:
do
not
agree
at
all
...
5:
fully
agree

25. CS
Results
2.24
2.42
2.42
2.14
1.36
2.15
2.86
2.45
2.18
2.21
2.07
1.03
2.35
1.06
2.30
1.73
1.08
2.13
1.23
1.44
2.30
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
5.00
1)
I
know
how
to
operate
a
computer
2)
I
spend
much
6me
on
the
computer
3)
I
am
interested
in
working
with
technical
equipment
4)
I
think
working
on
the
computer
is
no
stress
5)
I
have
experience
with
a
programming
language
6)
I
think
that
computer
science
is
an
important
subject
7)
I
think
the
computer
science
lessons
are
exci6ng
8)
I
like
to
solve
problems
9)
I
like
to
think
in
a
structural
way
10)
I
think
I
have
to
learn
a
lot
for
computer
science
11)
I
think
computer
science
is
a
boring
subject
Pre
Ctrl
Post
Ctrl
1:
do
not
agree
at
all
...
5:
fully
agree

26. Ergebnisse
SchülerInnenvorstellungen
Koopera6on

27. Ergebnisse
Interak6onsanalyse

28. Ergebnisse
Zusammenfassung
l SchülerInnen
nahmen
die
schüler-­‐zentrierten
Haltungen
des
Lehrers
verstärkt
über
das
Schuljahr
wahr
l Verwendete
Jugendsprache
während
Problemlösungsprozessen
wirkt
von
außen
kontraproduk6v,
wird
aber
von
SchülerInnen
als
förderlich
im
Prozess
gesehen
l Koopera6on
und
ein
gutes
Klima
werden
als
wich6ge
Faktoren
für
das
gelingen
von
Problemlösungsprozessen
im
Team
gesehen

29. 2
coThink
Projekt
Problemlösungsstrategien

30. Computa6onal
Thinking
l “I
think
we
must
be
careful
to
not
teach
coding
as
just
a
voca6onal
skill.”
(Booch)
l Prinzip:
Informa6sches
Denken
vor
informa6schem
Handeln
l Problemlösungsstrategien
als
Grundlage
l Informa6kisches
Denken
als
Grundlage
für
die
weitere
Bildung
in
Medientechnik
und
Lernen
mit
elektronischen
Medien

31. Lee
&
Mar6n,
CSTA
Voice
(2016)
“CT
refers
to
the
human
ability
to
formulate
problems
so
that
their
solu6ons
can
be
represented
as
computa6onal
steps
or
algorithms
to
be
carried
out
by
a
computer.”

32. Cuny,
Snyder,
and
Wing
(2011)
“CT
takes
place
when
students
are
‘looking
at
a
real-­‐world
problem
in
a
way
that
a
computer
can
be
instructed
to
solve
it.’”

33. Computa(onal
Thinking
ist
nicht
die
Kompetenz
zu
denken
wie
ein
Computer
sondern
in
Problemlösungsstrategien
zu
denken
die
für
den
Computer
entwickelt
wurden.

34. coThink
Project
Pa0erns
+
Computa4onal
Thinking
=
coThink
Projekt
www.cothink-­‐project.com

35. coThink
Project
Ziele
l Defini6on
einer
Problemlösungsstrategie
im
Kontext
“Computa6onal
Thinking”
l Design
von
Unterrichtseinheiten
mit
Lebensweltbezug
der
SchülerInnen
l Anwendung
von
Forschungsdesign
für
Unterrichtsforschung
l Durchführung
und
Forschung
in
Schulklassen
(9.-­‐11.
Schulstufe)
l Evalua6on
und
Spezifika6on
der
coThink
Problemlösungsstrategie

37. l Auf
Grundlage
von
(J.
M.
Wing,
2006),
bieten
rezentere
Beiträge
von
(Shuchi
Grover
&
Pea,
2013)
und
(Garneli,
Giannakos,
&
Chorianopoulos,
2015)
einen
Überblick
zu
den
vergangenen
Beiträgen
seither.
l In
Bezug
auf
die
Integra6on
von
Computa6onal
Thinking
im
Primar
und
Sekundarbereich
wird
in
(Yadav,
Zhou,
Mayfield,
Hambrusch,
&
Korb,
2011)
ein
Unterrichtsmodul
beschrieben.
l Computa6onal
Thinking
in
Bezug
auf
Programmieren
mit
LOGO
beschreiben
(Voogt,
Fisser,
Good,
Mishra,
&
Yadav,
2015).

38. l In
(Weintrop
et
al.,
2013,
2014)
wird
eine
Übersicht
zur
Implemen6erung
von
Computa6onal
Thinking
in
Science
Unterricht
gegeben
und
in
(Curzon,
Dorling,
Selby,
&
Woollard,
2014)
werden
Unterrichtsmethoden
dazu
beschrieben.
l Eine
prägnante
Zusammenfassung,
worum
es
bei
Computa6onal
Thinking
im
Unterricht
geht,
beschreiben
(D.
Barr,
Harrison,
&
Conery,
2010).
l Die
Disserta6on
(Weinberg,
2013)
ist
eine
geeignete
Referenzquelle
und
Überblickswerk.

39. coThink
Problemlösungsprozess
Das
Problem
…
…verstehen
…zerlegen
…abstrahieren
Eine
Lösung
…
…entwerfen
…evaluieren
…generalisieren

40. coThink
Haltungen
l Sicherheit
im
Umgang
mit
Komplexität
l Beständigkeit
im
Umgang
mit
Problemen
l Offenheit
für
Mehrdeu6gkeit
l Fähigkeit
Probleme
ohne
eindeu4ge
Lösung
anzunehmen
l Kompetenz
den
Prozess
mit
anderen
zu
kommunizieren
(D.
Barr
et
al.,
2010)

45. 3
Unterrichtsforschung
Topic
ID
Ques4on
Understanding
RQ1
How
understand
students
the
problem?
Abstrac4on
RQ2
How
abstract
students
the
problem?
Decomposing
RQ3
How
decompose
students
the
problem?
Solving
RQ4
How
create
students
a
solu6on?
Evalua4on
RQ5
How
evaluate
students
the
algorithm?
Generaliza4on
RQ6
How
generalize
students
the
solu6on?
Topic
ID
Ques4on
Complexity
RQ7
Are
students
confident
in
working
with
complexity?
Persistence
RQ8
Is
it
difficult
for
students
to
show
persistence
in
working
with
problems?
Tolerance
RQ9
Are
students
tolerant
for
ambiguity?
Problems
RQ10
Do
students
deal
with
open-­‐ended
problems?
Communicate
RQ11
Do
students
communicate
and
work
with
others
to
achieve
a
common
goal
or
solu6on?

46. Forschungsdesign

47. Forschungsdesign

48. Ergebnisse
l Qualita6v
l Aufgabenblä0er
und
Feedback-­‐Sheets
geben
Einblicke
in
den
Prozess,
blieben
aber
von
der
Datendichte
hinter
den
Erwartungen
l Beobachtungen:
Als
effek6ver
wurde
eine
unmi0elbare
Reflexion
danach
erachtet
l Quan6ta6v
l Fragebogen
zu
Haltungen
brachten
die
Erkenntnis,
das
sich
die
Einstellung
der
SchülerInnen
über
den
doch
kurzen
Zeitraum
änderte

49. Gesamt
3.81
4.10
4.17
4.38
4.10
4.33
4.23
4.50
4.68
4.15
0.00
1.00
2.00
3.00
4.00
5.00
6.00
RQ7
RQ8
RQ9
RQ10
RQ11
Haltungen
Pre-­‐/Post-­‐Test
PRE
POST

50. Zusammenfassung
l Pädagogische
“Pa0erns”
für
den
Informa6kunterricht
als
Rahmen
l Forschungsdesign
im
“mixed
methods”
Ansatz,
strukturiert
mit
Design-­‐
based
research
und
Case
Studies
l Computa4onal
Thinking
als
Erweiterung
bzw.
inhaltsgebendes
Unterrichtsthema
in
Verbindung
mit
Pa0erns
l Ausblick:
Computa6onal
Thinking
+
Pa0erns
in
Verbindung
mit
informa6schen
Anwendungen
auf
einfachem
Eins6egsniveau
für
Wiener
AHS
5.
Klasse
z.B.
mit
MIT
App
Inventor
oder
Raspberry
PI
und
Evalua6on.

51. Referenzen
(Auswahl)
l J.
M.
Wing,
“Computa6onal
thinking,”
Commun.
ACM,
vol.
49,
no.
3,
p.
33,
2006.
l C.
Rogers,
Freedom
to
Learn
for
the
80’s.
Columbus,
Ohio:
Charles
E.
Merrill
Publishing
Company,
1983.
l J.
Cornelius-­‐White
and
A.
P.
Harbaugh,
Learner-­‐Centered
Instruc(on:
Building
Rela(onships
for
Student
Success.
London:
Sage
Publica6ons,
Inc,
2009.
l L.
Cohen,
L.
Manion,
and
K.
Morrison,
Research
Methods
in
Educa(on.
Routledge,
2013.
l R.
K.
Yin,
Case
Study
Research:
Design
and
Methods.
London:
Sage
Publica6ons,
2008.
l R.
Motschnig
and
B.
Standl,
“Person-­‐centered
technology
enhanced
learning:
Dimensions
of
added
value,”
Comput.
Human
Behav.,
vol.
28,
2012.
l B.
Standl,
“Conceptual
Modeling
and
Innova6ve
Implementa6on
of
Person-­‐centered
Computer
Science
Educa6on
at
Secondary
School
Level,”
University
of
Vienna,
2014.

52. Referenzen
(Auswahl)
l Barr,
V.,
&
Stephenson,
C.
(2011).
Bringing
computa6onal
thinking
to
K-­‐12.
ACM
Inroads,
2(1),
48.
l Voogt,
J.,
Fisser,
P.,
Good,
J.,
Mishra,
P.,
&
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