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Ruhr-‐Universität Bochum

Example-‐Based Learning

May 16th, 2014 Prof. Dr. Nikol Rummel InsGtute of EducaGonal Research Ruhr-‐Universität Bochum, Germany


Instances of Learning by Example?

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Instances of Learning by Example

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Math course book: example of how to solve equaGon for ‚y‘

Watching YouTube tutorial on how to play a song on guitar

Observing host using cutlery in fancy restaurant

Reading manual to figure out how the new camera works

Learning how to Cha-‐Cha in dancing lesson

Watching mother make pancakes


Lines of Research -‐ Two Paradigms

Social-‐Cogni+ve Theory (Bandura)

Cogni+ve Load Theory (Sweller)

Learning from observing a model‘s problem-‐solving behavior

Learning from studying worked examples

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Both perspec=ves share: construcGng appropriate cogniGve representaGons in order to

show the behavior later Difference:

nature of aZenGon processes


ObservaGonal Learning (OL) -‐ IntroducGon •  Learner observes model performing task •  Model shows how problem is solved •  Model can behave naturally or didacGcally

•  PresentaGon: FtF, video, screen recording, animaGon

•  Danger: Transient informaGon in modeling examples!

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ObservaGonal Learning -‐ Research Example: Rocky Experiment (Bandura 1965)

•  Children (age: 3-‐6) watched video of an adult male (Rocky) and his behavior towards a Bobo-‐Doll

•  the model showed –  novel aggressive behavior –  novel verbal aggression

Exp. Phase

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•  Children (age: 3-‐6) watched video of an adult male (Rocky) and his behavior towards a Bobo-‐Doll

•  the model showed –  novel aggressive behavior –  novel verbal aggression

•  Model experiences consequences –  Cond. 1: reward (drinks, sweets, praise) –  Cond. 2: punishment (scolding, threat) –  Cond. 3: no consequences

•  Children play in the same room à spontaneous imitaGon •  Children are asked to reproduced observed behavior; receive reward

à reinforced imitaGon of behavior

Exp. Phase

Test Phase I

Test Phase II

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•  Test Phase 1: spontaneous imitaGon of observed behavior; –  model punished: less imitaGon

–  model rewarded: more imitaGon à observing consequences for others has effect

•  Test phase 2: reinforced imitaGon –  imitaGon same in all condiGons

à observing a model leads to learning

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ObservaGonal Learning -‐ Learning Process (Bandura)

1.  AZenGon 2.  RetenGon 3.  ReproducGon 4.  MoGvaGon

Bandura ,1986

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AcquisiGon

Performance


ObservaGonal Learning -‐ Research Example: (Rummel, Spada & Hauser, 2009)

•  Students observe scenes of a well-‐structured, successful

collaboraGon •  model seing resembles the collaboraGon seing

•  delivered as mulGmedia presentaGon on the computer screen •  audio recordings of dialog supplemented by animated slide-‐

clips


-‐ Research Example: (Rummel, Spada & Hauser, 2009)

!" Scenes of the model collaboraGon are presented via audio

Symbolic images of the collaboraGng model partners

Shared text editor with joint model soluGon.


ObservaGonal Learning -‐ Research Example: (Rummel, Spada & Hauser, 2009)

•  Observing model leads to beZer collaboraGon and beZer

joint soluGon in subsequent own problem-‐solving as compared to –  ScripGng first collaboraGon –  Unsupported first collaboraGon

•  prompGng of self-‐explanaGon acGviGes further enhanced effect of model


ObservaGonal Learning -‐ Design Principles •  Model-‐observer similarity •  Mastery modeling vs. coping model •  PersonalizaGon and voice principle •  Image principle •  AZenGon guidance

Van Gog & Rummel, 2010

Renkl, 2014

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ObservaGonal Learning -‐ EffecGveness: advantages & boundaries •  No trial learning vs. complexity of behaviour

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ObservaGonal Learning -‐ Categorizing Influences of Model (Bandura)

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Response Facilita=on

•  Behavior already acquired •  Shown unconsciously

Inhibi=on •  Behavior has already been acquired à observing consequences: effect on frequency

Acquisi=on of new behaviour by observa=on

•  New behavior, parts are already available (configuraGon)

Social Promp=ng •  Observing (socially accepted) alternaGve behavior being rewarded leads to alternaGon of own behavior

Changing Emo=onal State •  EmoGonal state is transferred from model to observer (acquisiGon by repeGGon)


Worked Examples (WE) -‐ IntroducGon •  Contain problem and worked-‐out soluGon steps leading to

final soluGon •  Demonstrate didacGcally how to solve a problem •  Focus learner‘s aZenGon on problem states, operators, and

applicaGon of operators •  PresentaGon: wriZen account (usually)

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Worked Examples -‐ Example: Problem

Renkl et al. 1998: 100

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Worked Examples -‐ Example: Problem

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Worked Examples -‐ Worked-‐Example Effect •  PosiGve effects of studying worked examples

compared to problem-‐solving •  Self-‐explanaGon acGviGes are paramount to

success

•  Robust effect: learning from WE compared to learning with ITS

Atkinson, Derry, Renkl & Wortham, 2000

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Sweller, Van Merrienboer & Paas, 1998

Van Gog & Rummel, 2010

McLaren, Lim & Koedinger, 2008


Worked Examples -‐ Worked-‐Example Effect •  WE more effecGve parGcularly for novices •  Problem solving of novices:

weak strategies à high cogniGve load à less learning

•  With worked-‐out steps: student can spend cogniGve capacity on understanding soluGon

Sweller et al. 1998

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Worked Examples -‐ Research Example (Sweller & Cooper 1985)

Experiment 3: Algebra •  Students (year 9 High School) learning 4 categories

of Algebra problems: solving for a

•  WE condiGon: example – problem pairs •  PS condiGon: problems to solve

•  4 Problems to solve

•  WE spent less Gme in acquisGon phase •  WE required less Gme to solve test problems +

made less math. errors

Acquis.Phase

Test Phase

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Worked Examples -‐ Design Principles •  Avoid split aZenGon •  Avoid redundancy

•  SegmenGng/ explicit subgoals

•  Prompt self-‐explanaGons •  Example-‐problem pairs •  CompleGon Problems •  MulGple examples •  Erroneous examples

For Overview see: van Gog & Rummel 2010

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Worked Examples -‐ EffecGveness: advantages & boundaries •  LiZle cogniGve load vs. need for self-‐explanaGon •  EffecGve vs. not at any stage of the learning process •  Efficient vs. liZle transfer of schemas

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IntegraGon of PerspecGves -‐ CommonaliGes and differences of WE and OL

•  Discuss in your groups and try to sort the examples in the google file: – Which examples are instances of OL, which are instances of WE?

– Why? What are commonaliGes and differences of OL and WE?

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Instances of example-‐based learning Worked Example or Observa=onal Learning?

Math course book: how to solve equaGon for ‚y‘ Worked Example

Observing host using cutlery in fancy restaurant Observa=onal Learning

Learning how to Cha-‐Cha in dancing lesson Observa=onal Learning

Watching YouTube tutorial on how to play a song on guitar

Observa=onal Learning/Worked Example

Reading manual to figure out how the new camera works

Worked Example

Watching mother make pancakes Observa=onal Learning


IntegraGon of PerspecGves -‐ CommonaliGes of WE and OL

•  Providing example cases for learning is effecGve and efficient

•  ParGcularly in iniGal stages of learning •  Goal: Schema-‐AcquisiGon •  Important:

•  ConnecGng examples to underlying principles •  FacilitaGng example comparison •  FacilitaGng learner acGviGes to connect example(s) and principles

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IntegraGon of PerspecGves -‐ Phases of Skill AcquisiGon 1.  Principle encoding:

•  acquiring declaraGve knowledge about domain

2.  Relying on analogs •  to solve problem via analog (AR)

3.  Forming declaraGve rules: •  learners form “if-‐then”: how to act to solve

the problem 4.  fine tuning: automaGon and flexibilizaGon.

•  chunking and automaGng soluGon steps •  adapGng skill to new problems

Renkl 2014

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Literature •  Anderson, J. R. (1993). Rules of the mind. Hillsdale, NJ [u.a.]: Erlbaum.

•  Bandura, A. (1965). Influence of models reinforcement conGngencies on the acquisiGon of imitaGve

response. Journal of Personality and Social Psychology, 1, 589-‐595.

•  Bandura, A. (1986). Social foundaGons of thought and acGon: A social cogniGve theory. Englewood Cliffs,NJ: PrenGce Hall.

•  Renkl, A. (2014), Toward an InstrucGonally Oriented Theory of Example-‐Based Learning. CogniGve Science, 38: 1–37. doi: 10.1111/cogs.12086

•  Renkl, A., Stark, R., Gruber, H. & Mandl, H. (1998): Learning from Worked-‐Out Examples: The Effects of Examples Variability and Elicited Self-‐ExplanaGons. Contemporary EducaGonal Psychology 23, 90-‐108.

•  Sweller, J., Cooper, G. (1985). The Use of Worked Examples as a SubsGtute for Problem Solving in Learning Algebra. CogniGon and InstrucGon, 1985, 2 (1) 59-‐89.

•  Sweller, J., van Merriënboer, Jeroen J.G. & Paas, Fred G.W. C. (1998). CogniGve Architecture and InstrucGonal Design. Educa+onal Psychology Review, 10 (3), 251–296.

•  van Gog, T. & Rummel, N. (2010). Example-‐Based Learning: IntegraGng CogniGve and Social-‐CogniGve Research PerspecGves. Educa+onal Psychology Review, 22 (2), 155–174.

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Other Sources •  Slide 07:

– hZp://www.moguul.de/papil/wp-‐content/uploads/2008/02/bobo-‐doll-‐experiment.jpg [05/03/2014]

•  Slide 14,25, 32: – hZp://3.bp.blogspot.com/-‐aWbxYuniqH0/UM3B3CitwXI/AAAAAAAAAqI/iiwZ82CtjDM/s1600/quesGon2.jpg [05/04/2014]

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