student-centered teaching patterns in the context of computaional thinking problem solving processes

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Studentcentered teaching pa0erns in the context of computa6onal thinking problem solving processes Bernhard Standl

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Page 1: Student-centered teaching patterns in the context of computaional thinking problem solving processes

Student-­‐centered  teaching  pa0erns  in  the  context  of  

computa6onal  thinking  problem  solving  processes  

Bernhard  Standl  

Page 2: Student-centered teaching patterns in the context of computaional thinking problem solving processes

Main  Goal  

 

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

 

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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  

Page 4: Student-centered teaching patterns in the context of computaional thinking problem solving processes

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  

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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.  

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

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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  

Page 8: Student-centered teaching patterns in the context of computaional thinking problem solving processes

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.  

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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.    

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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).  

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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.  

 

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Pa0erns  in  der  Unterrichtspraxis  

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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.  

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Beispiel  

Page 19: Student-centered teaching patterns in the context of computaional thinking problem solving processes

Unterrichtsforschung  

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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  

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Forschungssenng   forward(100) left(90)forward(100)left(90)forward(100)left(90)forward(100)

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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  

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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  

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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  

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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  

Page 26: Student-centered teaching patterns in the context of computaional thinking problem solving processes

Ergebnisse  SchülerInnenvorstellungen  Koopera6on  

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Ergebnisse  Interak6onsanalyse  

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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  

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2  coThink  Projekt  Problemlösungsstrategien  

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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  

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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.”  

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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.’”  

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

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coThink  Project  

Pa0erns  +  Computa4onal  Thinking  =  coThink  Projekt  

www.cothink-­‐project.com  

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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  

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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).  

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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.  

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coThink  Problemlösungsprozess  Das  Problem  …  

…verstehen  

…zerlegen  

…abstrahieren  

Eine  Lösung  …  

…entwerfen  

…evaluieren  

…generalisieren  

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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)    

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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?  

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Forschungsdesign  

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Forschungsdesign  

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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  

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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  

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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.  

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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.  

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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.,  &  Yadav,  A.  (2015).  Computa6onal  thinking  in  compulsory  educa6on:  Towards  an  agenda  for  research  and  prac6ce.  Educa(on  and  Informa(on  Technologies.  Yadav,  A.,  Zhou,  N.,  Mayfield,  C.,  Hambrusch,  S.,  &  Korb,  J.  T.  (2011).  Introducing  computa6onal  thinking  in  educa6on  courses.  Educa(onal  Studies,  (2),  465–470.    

l  Weinberg,  A.  E.  (2013).  Computa(onal  thinking :  an  inves(ga(on  of  the  exis(ng  scholarship  and  research.  Colorado  State  University.  

l  Weintrop,  D.,  Behesh6,  E.,  Horn,  M.,  Orton,  K.,  Jona,  K.,  Trouille,  L.,  &  Wilensky,  U.  (2013).  Defining  Computa6onal  Thinking  for  Science  ,  Technology  ,  Engineering  ,  and  Math.