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Michael E. CaspersenWorkshop #3 @ SIGCSE 2011

Pedagogical Progressions for Teaching OOD

Workshop #3 @ SIGCSE 2011

Pedagogical Progressions for Teaching Object-Oriented

Design

Learning Theory Background

and an example of

pedagogical progression for teaching OOD to novices

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Agenda

Brief introduction to learning and our cognitive architecture

Cognitive Load Theory (CLT), Worked Examples (WE), and CLT Effects

An example of pedagogical progression for teaching OOD to novices intro programming course @ Aarhus University (7

weeks)

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Reading

et a lg nimo cs ire pa psw e ngni nr ome ht

the morning newspaper is coming late

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Reading

Expert Knows 100.000s of words and

phrases Patterns and pattern matching

Novice Knows few (if any) words and

phrases; perhaps hardly the letters

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Chess

Expert Knows 100.000s of board

configurations and their associated moves

Patterns and pattern matching!

Novice Knows few (if any) board

configurations and their associated moves

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Expert Versus Novice

Expert schemas: many, at many different levels of abstraction automation: masters many automated process which

can be carried out with minimal conscious thought

Novice schemas: none, need to be built automation: no automated processes; all processes

require conscious thought, i.e. load on working memory

Education (pattern-oriented instruction)

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Cognitive Learning Processes

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Our Cognitive Architecture

Long-Term Memory:Schemas

Working Memory:Integration

encoding decoding

recoding/chunking

(unconscious)

decision(conscious)

Senses Muscles

Environment

New information is processed in WM to form

new knowledge structures called schemas stored in

LTM.

Learning

Memory structures that permit us to treat a large amount of information as

one unit.

Schemas

Automated processes which can be carried out with minimal conscious

thought.(Procedural schemas.)

Automation

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Cognitive Load Theory(1982-)

The fundamental axiom of CLT: learning outcome is optimized when cognitive load fully utilizes the capacity of working memory with elements that

allow for optimal schema acquisition.

• Intrinsic cognitive load (I)– cognitive load intrinsic to the problem that

cannot be reduced without reducing understanding

• Extraneous cognitive load (E)– a non-intrinsic cognitive load caused by

instructional procedures that interfere with rather than contribute to learning

• Germane cognitive load (G)– a non-intrinsic cognitive load that

contributes to, rather than interfere with, learning by supporting schema acquisition (pattern-oriented instruction)

Learning outcome

Cognitive load

L = I + E + G

Optimize learning:minimize E, maximize G

John Sweller

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Pre CLT Dogma

Cognitive PsychologyIn the early 1970s, problem solving became one of the central fields of cognitive

psychology.

Assumption and AdviceProblem solving is the best form of learning.

The more, the merrier!

Cognitive Load TheoryNo!

A misguided advice!

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Worked Examples

A Worked example (WE) is an instructional device that provide a problem solution for a learner to study.

WE include– a problem statement– a procedure for solving the problem

WE are meant to illustrate how other similar problems might be solved (prototypical examples).

The WE approach is particularly relevant to programs of instruction that seek to promote skills acquisition, e.g. music, chess, programming, problem solving, etc.

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Selected CLT Effects

Worked Examples Effect (1985)Alternation of WEs and problems increase learning outcome and transfer.

Example Completion Effect (1992)Partially completed problems presented to learners are as effective as worked

examples and better than full problems.

Expertise-Reversal Effect (2003)Effects gradually disappear as students develop expertise. With further

expertise, effects reverse, i.e. learning outcome is reduced.

Guidance-Fading Effect (2003)Complete examples followed by partially completed examples followed by full

problems is superior to any of the three in isolation.

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WE with Faded Guidance

represent “atomic” activities in the process of solving a task

represents a model approach to a solution

represents a novice’s attempt at a solution

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Music Composition

Learning goal: Compose a piece of music in three “voices”

Give the melody, ask for the two additional voices.

...

Give the melody and most of the two additional voices; ask for the missing bar(s).

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WE and Faded Guidance in Programming

3. In a lab session, the students use, modify, and extend both examples.

1. In a video, we present development of a program with two classes, Playlist and Track. P T*

2. In a lecture, we present (a partial) development of a similar example, say Account and Transaction.

A T*

4. In an exercise, the students extend the Playlist-Track example by adding an Image class.

P T*

I

*

5. In an assignment, the students implement a similar system of three classes, say Notebook, Note, and Keyword.

Nb N*

K

*

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Generic Schema for Use of WEs

3. Let the students use, modify and extend one or both WEs in a lab session.

1. Present a complete WE in a pod- or pencast.

2. Present and discuss a partial WE in a lecture.

4. Let the students make a more elaborate extension to one of the WEs in a follow-up exercise.

5. Let the students solve a similar problem (same structure and problem solving approach, but different cover story) in a mandatory assignment.

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The Three Takeaways

Worked examples are effective pedagogical tools

Pattern-oriented instruction as germane cognitive load

Use, modify, create is an effective generic principle of progression for instructional designs can be used simultaneously at different levels

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Groupwork

Choose a technique or concept that the students must learn

Discuss how WE and faded guidance can be used to present key aspects of the technique

What could be appropriate stepping stones in the learning process?

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Workshop #3 @ SIGCSE 2011

Pedagogical Progressions for Teaching Object-Oriented

Design

Learning Theory Background

and an example of

pedagogical progression for teaching OOD to novices

Bonus slid

es

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Pattern-Oriented InstructionIn Programming

MotivationPatterns capture chunks of programming knowledge and skills.

Reinforces schema creation when cognitive load is “controlled” (increased germane cognitive load).

Design patternsAssociationObserverStrategy...

Algorithmic patternsfindOnefindAllfindBest (min, max, ...)...

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import java.util.*;

bs = new ArrayList<B>();

private List<B> bs;

public void add(B b) { bs.add(b); } public void remove(B b) { bs.remove(b); }

Design Pattern: Association

A B*

class B { ...}

class A { ... // A fields public A() { ...

}

... // A methods

}

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Associations All Over

Inbox * Email

Player * Playlist

Playlist * Track

Track * Picture

Album * Photo

Calendar * Appointment

DNA register * DNA string

DNA string * AminoAcidResidue

Presentation * Slide

Slide * SlideObjects

Animation * Picture

...

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An Emerging Algorithmic Pattern

public List<Track> findAllByArtist(String artist) { List<Track> res= new ArrayList<Track>(); for ( Track t: tracks ) if ( t.getArtist().contains(artist) ) res.add(t); return res;}

public List<Photo> findAllAfter(Date d) { List<Photo> res= new ArrayList<Photo>(); for ( Photo e: photos ) if ( e.getDate().isLaterThan(d) ) res.add(e); return res;}

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Algorithmic Pattern: findAll

public List<T> methodName(List<T> lst, additional params) { List<T> result= new ArrayList<T>(); for ( T e : lst ) { if ( criteria(e) ) { result.add(e); } } return result;}

1. Substitute concrete type for T2. Choose proper method name3. Choose appropriate additional parameters for the concrete criteria

Instantiation of pattern findAll

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Time for Practicing...

“Make the students feel the pain”Erich Gamma, OOPSLA 2003

Sometimes a good motivation, but practice it with care…

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Generic Method: findAll

public static <T>List<T> findAll(List<T> lst, Criteria<T> c) { List<T> result= new ArrayList<T>(); for ( T e : lst ) { if ( c.satisfiedBy(e) ) { result.add(e); } } return result;}

interface Criteria<T> { boolean satisfiedBy(T e);}

List<Person> persons= new ArrayList<Person>();class SeniorCriteria implements Criteria<Person> { ... }

findAll(persons, new SeniorCriteria());

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Example of ProgressionAt several levels

First level Use, modify and extend various A -->* B problems

(practicing the association pattern with lists and iterations)

Algorithmic patterns: findAll, findBest, ...

Second level Use generic methods min/max to realize findBest Present generic method for findAll Modify findAll... Create generic methods for other algorithmic patterns

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Algorithmic Pattern: findBestUsing Generic MethodsFinding a “best” object in a collection/list

• best rated track• shortest track• ...

Collections:- T min(Collection<T> c)- T max(Collection<T> c)

Natural order...

interface Comparator<T> { int compare(T a, T b);}

List<Track> tracks= new ArrayList<Track>();class RatingComparator implements Comparator<Track> { ... }

Collections.max(tracks, new RatingComparator());

An optimization problem

anden information michael e. caspersen workshop #3 @ sigcse 2011 pedagogical progressions for...

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