chapter 12: synthesis & exam preparation · key ideas in behaviorism ① psychology is becoming...

CS-411 : Digital Education & Learning Analytics

ÉC OLE POL Y TEC H NIQ U EFÉ DÉRA LE D E LA USANNE

Pierre Dillenbourg, Patrick Jermann & Łukasz Kidziński

Chapter 12:

Synthesis & Exam preparation

Behavorism

Constructivism

Socio-cultural theory

DataViz

Learning Analytics

Orchestration Graphs

Behavorism

Constructivism

DataViz

Learning Analytics

Burrhus Frederic Skinner (1904-‐1990), Operant Conditonning

h>ps://sites.google.com/a/adams12.org/harp-‐mrhs-‐home-‐v1/06-‐powerpoint-‐secHon/skinner-‐s-‐operant-‐condiHoning

STIMULUS

Behaviour

Association

How do people learn ? By condiHonning

p (B | S) analytics

Key ideas in behaviorism

①  Psychology is becoming more scientific

②  The brain is a black box; the focus is on behaviors

③  Learning is « engineered »

④  Association results from immediate feedback

⑤  The learner is permanently active

⑥  Small steps increase the probability of positive feedback è Programmed instruction

⑦  Then it moved to larger step: modular instruction è mastery learning

a1 a2..1 a2.2

Pre-requisites test

Remediation

Pre-test

Post-Test

Int. Test

Redo [a4-a5]

Skip [a4-a5]

Item Order

Uphill strategy: go up until he fails

Downhill strategy: go down until he succeeds

Discriminant strategy: increase/decrease difficulty based on success – cut the space in 2

Modular Instruction Pre-requisite test: Does the learner has the pre-requisite to start the course ? Pre-test: Should the learner skip some modules ? Intermediate-test: Didthe learner reach the objectives of this module ? Post-test: Did the learner reach the objectives of this course?

Adaptive Testing

h>p://arrowthroughthe

sun.blogspot.ch/2012/07/if-‐ph

ilosoph

y-‐ne

twork-‐is-‐based-‐on

Instructional Design

Knowledge space

Digital courses

Example of exam question

Which learning mechanism(s) benefit from immediate

feedback ? Why ?

①  Elicitation

②  Proceduralisation

③  Association

④  Metacognition

For which learning objectives would you use a behaviorist

approach ? How could it work ?

①  Identifying categories of clouds (cumulus, stratus,..)

②  Identifying the bug in a piece of code

③  Identifying the best business plan among 3 proposals

④  Identifying a fossile on a stone

Behavorism

Constructivism

DataViz

Learning Analytics

experience behaviour

Constructivism

How do people learn ? By construcHng cogniHve structures from experience (trial & error)

Reflection

Trial & Error

h>p://fr.slideshare.net/susanhansen1460/une-‐psy250-‐session-‐7-‐ist-‐2-‐years-‐language-‐piaget

Dr Susan Hansse, University of New England

Cognitive Conflict as key learning mechanism

(1) I wanted to get this

(2) I got that

(3) The problem is here

define "house1 [[ ] [forward 100 right 45 forward 60 right 120 forward 60 right 45 forward 100 right 90 forward 60 ]]

METACOGNITION

Cognitive Conflict as key learning mechanism

•  Learning from experience •  Learning by doing •  Learning from failure •  Discovery learning

Conditions: 1.  The conflict is detected 2.  The learner finds how to solve it

Role of the environment (sequence of projets / teacher / peer)

Constructivism

Constructionism Guided Discovery

Microworlds

•  microworlds •  Simulations •  Modelling

Radical Quest for effectiveness

Learning for simulations is difficult Hypothetico Deductive Reasoning

1.  (Raise a question)

2.  Generate an hypothesis

3.  Design an experiment

4.  Run/simulate the experiment

5.  Interpret results

Manipulating real or virtual objects ?

Summary: From Constructivism to Augmented Reality

1.  People don’t learn by being taught but by adapting their knowledge structures through interaction with artefacts. Educational philosophy: from telling students what to do to letting them invent things

2.  In practice, this approach does not work very well without external support and requires talented teachers. Learning from simulation requires inquiry skills. Training these transversal skills are key goals of any education

3.  Evolution of pedagogical methods from building mental schemes to building concrete objects. Digital artefacts offer rich interactions but digital education is not limited to virtual object. Tangible interfaces and augmented reality open it to physical

manipulation.

How does assimilation and accomodation occur when

learning from a simulation ?

For which learning objectives would you use a microworld ?

What would learners do in this microworld

①  Learning to express clearly one’s idea

②  Learning to decompose a problem

③  Learning to learn from one’s own mistakes

④  Learning to take the viewpoint of someone

Behavorism

Constructivism

DataViz

Learning Analytics

Social Interaction

Thinking

We internalise social interaction because thinking is a dialogue with oneself .

Private speech (Vygostky) Egocentric speech (Piaget)

Collaborative learning

1.  Collaborative learning is often effective, but not systematically.

2.  It is effective when rich interactions occur such as explanation, argumentation, mutual regulation

3.  To make it more effective, the technology or the script increases the necessity for students to produce these interactions

4.  The theory behind emphasizes that cognition is inherently social because thinking mostly relies on language.

Social Cognition

The hardware is individual

But the software is social

There are 400 architectures students in an online course on urbanism. You would like to apply the JIGSAW script to design their team project. The project concerns the positioning of car parks in the city. What would the script do?

For which learning objectives would you collaborative

learning methods ?

①  Solving problems when there is a single clear solution

②  Solving problems when there a several solutions, but

some are better than others.

③  Solving problems where the goal is to find many

solutions.

Behavorism

Constructivism

Learning Theory

Lesson (eLearning)

Exploratory environment

Collaboration Tools

Learning Technology

Behavorism

Constructivism

DataViz

Learning Analytics

400'000+%Users%%

195'000+%Users%%

50'000+%Users%%3'200+%Users%%

Watched%Video(s)% Solved%Exercise(s)% Passed%the%Course% Paid%$50%for%CerFficate%

%"of"R

egistered"Users"

55’000 X 4 ECTS ≈ 800 * 300 ECTS

MOOC BA+MA

The Gartner Cycle of Technology Adop8on

MOOC > VIDEOS

A good MOOC

includes rich activities

h>p://128.178.27.98:8082/LHE1.html

Fluid Dynamics (Gallaire & Ancey)

StaHcs (Mu>oni & Burdet)

Behavorism

Constructivism

DataViz

Learning Analytics

Données par canton Eléments visuels

•  Canton •  # inhabitants •  % foreigners •  % votes

•  Label •  Disk size •  Position Y •  Position X •  Coulor >50%

A visualisation grammar is a mapping between information pieces and visual elements

3D trap !

Janvier" Février" Mars"" Avril"" May"" Juin" Juillet"

Geneve%

Geneve"

Janvier"Février"

Mars""Avril""

May""Juin"

Juillet"

Geneve%

Design Principles

#3. Check graphical integrity (Tufte)

visual ~ data?

1 2 3 4 5

1 2 3 4 50

1 2 3 4 5

Design Principles #3. Optimize the“data ink ratio” (Tufte)

Which % of pixels provide information ?

h>p://med

iceanalyHcs.com/im

ages/smallm

les1.png

Design Principles

Use“small multiple”

(Tufte)

Design Error 4

Let R generate the scale automatically !!!!!!!!!

2014 Geneve Vaud Valais0 Fribourg Neuchatel 2015 Geneve Vaud Valais0 Fribourg NeuchatelJanvier 100 100 100 100 100 Janvier 50 50 50 50 50Février 80 39 140 110 120 Février 40 19.5 70 55 60Mars0 100 50 120 90 120 Mars0 50 25 60 45 60Avril0 100 100 100 100 100 Avril0 50 50 50 50 50May0 100 120 100 100 100 May0 50 60 50 50 50Juin 70 145 100 100 90 Juin 35 72.5 50 50 45Juillet 50 20 120 110 150 Juillet 25 10 60 55 75

600 574 780 710 780 3444 300 287 390 355 390 1722

2014 2015

Janvier0 Février0 Mars00 Avril00 May00 Juin0 Juillet0

Geneve0

Valais00

Fribourg0

Neuchatel0

Janvier0 Février0 Mars00 Avril00 May00 Juin0 Juillet0

Geneve0

Valais00

Fribourg0

Neuchatel0

Humanities

Management

Basic SciencesLife sciences

Engineering

HumanitiesManagementBasic SciencesLife sciencesEngineering

Design Erreur 10

‘split attention effect’

Vertical / Horizontal: 5:1

Vertical / Horizontal: 1:1

Is there a split a>enHon effect on this figure ? Is there any other concern ?

Behavorism

Constructivism

DataViz

Learning Analytics

Independent Variable Variable

Dependent

Solo / Team Test Score

How to prove the effectiveness of X ?

Summary •  Research Question To be answered by the experiment •  Hypothesis Expected results ( A > B); an affirmation •  Independent variables What one varies between the conditions (or Factor) •  Modality Value of a factor •  Condition Set of (factor, modality) per group of subjects •  Control group The reference against which one will compare •  Dimension Number of factors •  Dependent variables How does one measure the effects ? •  Controlled variables Things you try to keep constant or to randomize •  Intermediate variables Explain the link from Independent to Dependent Variables •  “Significant” difference Probably (<5%) not due to sampling error •  Interaction effect The effect of one IV on the DV depends upon another IV •  Between/Within subject Do subjects pass in one or several conditions ? •  Counterbalancing Inverting the order of conditions for within-subject plans

Design an experiment that compares the effectiveness of constructionism versus constructivism. Verify if the effect depends upon the age of students. What would be the independent, controlled, intermediate and dependent variables? Describe a potential interaction effect. Design an experiment that measures the effectiveness of immediate feedback from learning vocabulary. Verify if the effect varies with the level of prior knowledge of the participants. What would be the independent, controlled, intermediate and dependent variables ? Describe a potential interaction effect.

Behavorism

Constructivism

DataViz

Learning Analytics

Pairs of students with conflicting viewpoints, probably get higher learning gains (than …), because conflict increases argumentation intensity and opportunities for decentration.

Learning Theory (behaviorism, constructivis, socio-cultural)

prediction explanation

How to form conflicting pairs among 20, 200 and 2’000 students ?

G= (V, E) where E= V X V

V = {ai} | ai: ts, te, π, object, product, {c}, traces, {metadata}

E = { eij} | eij: (ai, aj, {operators}, {controls}, label, weight, elasticity)

An orchestration graph is a weighted directed geometric graph.

Library of Edge Labels Why is ai a condition for aj ?

The preparation edges connect two activities when the learner has a higher probability of succeeding at aj if he carried out ai before ai.

The set edges connect two activities when the skills or contents addressed in ai and aj are in relationship with each other; for example, subset/superset, whole/part, and siblings. (UP / DOWN)

The translation edges connect two activities in which the same content is addressed under different formats, representations, notations, or viewpoints. Learners therefore have to translate the representation used in ai into the representation used in aj.

The generalization edges introduce variations of the content or skills across the space of generalization, namely introducing the student to more general, less general, or analogical contexts from ai to aj. (UP / DOWN)

Library of Edge Labels Why is ai a condition for aj ?

Preparation Set Translation Generalization

(P) Prerequisite (S+) Aggregation (T) Proceduralization (G+) Induction

(P) ZPD (S+) Expansion (T) Elicitation (G+) Deduction

(P) Adv. organizer (S–) Decomposition (T) Alternate (G+) Extraction

(P) Motivation (S–) Selection (T) Reframe (G+) Synthesis

(P) Anticipation (S=) Juxtaposition (T) Reverse (G=) Analogy

(P) Logistics (S=) Contrast (T) Repair (G=) Transfer

(P) Data collection (S=) Identity (T) Teach (G–) Restriction

Library of Edge Operators

How data collected in ai are processed for aj ?

Aggregation operators gather data for subsequent activities, generally located on a higher plane

Distribution operators split data for subsequent activities, generally located on a lower plane

Social operators modify the social structure of activities. They rely on social distance criteria

Back-office operators enrich data with external information, including information manually provided by human actors

Library of Graph Operators

Aggregation Distribution Social BackOffice (A) Listing (D) Broadcasting (S) Group

formation (B) Grading

(A) Classifying (D) User selection (S) Class Split (B) Feedback (A) Sorting (D) Sampling (S) Role assignment (B) Anti-plagiarism (A) Synthesizing (D) Splitting (S) Role rotation (B) Rendering (A) Visualizing (D) Conflicting (S) Group rotation (B) Translating (D) Adapting (S) Drop out

management (B) Summarizing

(S) Anonymisation (B) Converting (B) Updating

Learner Modelling

Text entered Item selected (button, menu,…) Area clicked Line drawn with mouse. Response time … … Number of pauses Mouse path Gaze path Facial expressions Gestures …

From the learner’s behaviours, infer his/her learner’s knowledge state

Behaviours

Behavioural ‘Dust’ (fragments of behaviour that do not

have an explicit semantic value)

« Social Signal Processing »

X0(s) X1(s) X3(s)

Dropped Out

Active Lost

.05 Fine

.17 .27

.39 .39

.35 .20

The weight of edges

Plane of Activity π1 π2 π3

Xi (s) Xi (s1) Xi (s1) Active / Passive Social loafing With-me

On-Leave / Drop-

Out / Late-Comer

Free-rider Central

Disoriented Leader Isolated

Linear rigidity On/Off Role Bridge

Impasse

Trapped

Over/Under

generalization

Deep/surface

Gaming

Xi (s1,s2,s3,… ) Xi (s1,s2,s3,… ) Under/Over Sized Cluster

Cognitive/Emotional

Conflict

Misunderstanding

Groupthink

Distributed

Xi (S) Good/Bad Spirit

A pedagogical scenario starts with a test on english vocabulrary. Then, the high score students have a lecture on the construction of negative sentences, while the low score do indivdiual exercies to acquire more vocabulary. Then, students work in pair, one low score and one high score on negative sentence construction exercices Describe the graphs and specify the label and oeprators on edges.

Invent a graph with an edge ‘alternate’ and 2 operators, aggergation and distribution.

xi(sb)

bi(sa)

xi(sa)

bi(sa) bi-1(sa)

Diagno

sis xi-1(sb)

xi-1(sa)

The diagnosis cube

chapter 12: synthesis & exam preparation · key ideas in behaviorism ① psychology is becoming...

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