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JTEL Summer School WorkshopPsycho-pedagogical theories have a usually underestimated high impact on adaptive education. In the ROLE project, a primary goal is to identify functional and non-functional requirements specifications, with the aim to integrate them into a psycho-pedagogically sound framework as a basis for the development of a highly responsive open learning platform.The results of behavioural and cognitive psychology show that humans make mistakes predictably and this knowledge can be harnessed to support them. Various biases emerge from the interplay between the automatic and reflective system driving our thinking processes. Apparently there are opportunities for choice architecture to influence options in a way that will support choosers to act in their own interest. The main challenge here is to offer nudges that will most likely help and least likely inflict harm, preserving freedom of choice.In this workshop we demonstrate some of human cognitive biases and ask participants to elaborate in collaborative and interactive way on possible consequences for requirements specification of adaptive and recommender learning systems. Our aim is to raise awareness of some outcomes from behavioral and cognitive psychology that can be relevant for the design of future responsive learning solutions.
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Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. Jarke
M. KravcikR. Klamma
Z. Petrushyna
JTEL Summer School
June 2010Slide 1
How can Behavioral and Cognitive Psychology inform Design of Learning Experiences?
WorkshopOhrid, June 2010
Milos Kravcik, Ralf Klamma, Zinayida Petrushyna
Chair for Information Systems and Databases,RWTH Aachen University, Germany
Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. Jarke
M. KravcikR. Klamma
Z. Petrushyna
JTEL Summer School
June 2010Slide 2
OverviewIntroduction (30 min)
Collaboration:• discussion in groups of 4 (15 min)• presentation of outcomes (15 min)• clustering of outcomes (15 min)
Summary:• discussion & feedback (15 min)
Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. Jarke
M. KravcikR. Klamma
Z. Petrushyna
JTEL Summer School
June 2010Slide 3
Agenda
Motiv
ation
Heur
istics
&
Bias
es
Surve
y Re
sults
Choic
e Ar
chite
cture
Prop
osed
So
lution
Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. Jarke
M. KravcikR. Klamma
Z. Petrushyna
JTEL Summer School
June 2010Slide 4
Motivation
Illusions:• Optical• Cognitive
Rashomon (Akira Kurosawa, 1950)The story of human communication
Awareness TestCount how many times the white team passes the ball
Right Brain vs Left Brain TestDo you see the dancer turning clockwise or anti-clockwise?
Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. Jarke
M. KravcikR. Klamma
Z. Petrushyna
JTEL Summer School
June 2010Slide 5
Human Mind
S. Pinker: Our mind is made for fitness, not for truthMeaning depends on (D. Gilbert):• context• frequency• recency• preferences
R. Thaler: Humans predictably err• this knowledge can be harnessed to help them
Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. Jarke
M. KravcikR. Klamma
Z. Petrushyna
JTEL Summer School
June 2010Slide 6
2 Kinds of ThinkingAutomatic system (AS):– gut reaction:
• intuitive, rapid, instinctive• associated with the oldest parts of the brain
Reflective system (RS) – conscious thought:
• rational, deliberate, self-conscious
Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. Jarke
M. KravcikR. Klamma
Z. Petrushyna
JTEL Summer School
June 2010Slide 7
Heuristics & Biases emerge from the interplay between AS & RS
• people prefer information that confirms their preconceptions or hypothesesConfirmation bias
• people see events that have occurred, as more predictable than they in fact were before they took placeHindsight bias
• people are over-optimistic about the outcome of planned actionsOptimism bias
• people overestimate the length or the intensity of future feeling statesImpact bias
• as people usually see just the winners, not the losers, they may misattribute the causes that led to the winningObservation bias
• people underestimate task-completion timesPlanning fallacy
• creating a story post-hoc so that an event will seem to have an identifiable causeNarrative fallacy
• believing that the unstructured randomness found in life resembles the structured randomness found in gamesLudic fallacy
Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. Jarke
M. KravcikR. Klamma
Z. Petrushyna
JTEL Summer School
June 2010Slide 8
Survey on Present Learning
10 questions on 3 issues:
• Freedom of learner• Pedagogical support• Importance of content & form• Necessity of tutors (teachers)
31 participants, mostly students of the Summer School
Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. Jarke
M. KravcikR. Klamma
Z. Petrushyna
JTEL Summer School
June 2010Slide 9
Freedom of Learner
The more freedom for the learner the better
Too much freedom for the learner may be overwhelming and contra productive
Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. Jarke
M. KravcikR. Klamma
Z. Petrushyna
JTEL Summer School
June 2010Slide 10
Pedagogical SupportEach learner should have some pedagogical knowledge to be able to learn without an external help
Not everybody can be an expert in education (teacher), therefore pedagogical assistance for the learner is required
Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. Jarke
M. KravcikR. Klamma
Z. Petrushyna
JTEL Summer School
June 2010Slide 11
Importance of Content & FormIn learning the content is crucial and the form (presentation, organization) of the learning experience is secondary
The form of the learning experience is most important and the content is secondary
Both content and form (org.) of the learning experience are equally important for successful learning
Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. Jarke
M. KravcikR. Klamma
Z. Petrushyna
JTEL Summer School
June 2010Slide 12
Necessity of TutorThe tutor(teacher) is not necessary in thepresent learning
It is always good when the learner has a competent tutor (teacher)
Tutors (teachers) may be successfully replaced by peer-learners
Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. Jarke
M. KravcikR. Klamma
Z. Petrushyna
JTEL Summer School
June 2010Slide 13
Categories of Biases (1)
3 basic categories of biases (Thaler & Sunstein)Bounded rationality: our rationality is delimited
• unrealistic optimism is a pervasive feature of human life
• humans fear loss more than they love gain
• people have a tendency to stick with their current situation
• choices depend on the way in which problems are stated
Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. Jarke
M. KravcikR. Klamma
Z. Petrushyna
JTEL Summer School
June 2010Slide 14
Categories of Biases (2)Self-control: our rationality and temptation may be in conflict
an individual is containing two semiautonomous selves, which means there is a two-system conception of self-control:
• planner (RS)
• doer (AS)
Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. Jarke
M. KravcikR. Klamma
Z. Petrushyna
JTEL Summer School
June 2010Slide 15
Categories of Biases (3)Social influences: we are influenced by the behavior of other people
• information – on actions and thoughts of others (we tend to conform)
• peer pressure –considering what other people think to avoid their disapproval
• priming – subtle influences can increase the ease with which certain info comes to mind (channel factors)
Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. Jarke
M. KravcikR. Klamma
Z. Petrushyna
JTEL Summer School
June 2010Slide 16
Uneasy Choices(Thaler & Sunstein)
• self-control issues arise when choices and their consequences are separated in timeDelayed effects
• many problems in life are difficult and there is no technology to helpDifficulty
• some decisions are rare, therefore there is a lack of practiceInfrequency
• learning requires immediate and clear feedback after each tryPoor feedback
• ambiguous relation between a choice and its consequenceUnclear impact
Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. Jarke
M. KravcikR. Klamma
Z. Petrushyna
JTEL Summer School
June 2010Slide 17
Choice Architecture(Thaler & Sunstein)
• they are very powerful, as usually a lot of people end up with itDefault options
• a well designed system is as forgiving as possibleExpect error
• it is the best way how to improve the performance of humansGive feedback
• options should be comprehensibleUnderstand mappingsfrom choice to welfare
• Elimination by aspects: eliminate the unsuitable alternatives• Collaborative filtering: use the judgements of similar people
Structure complexchoices
• put the right incentives on the right people – Who uses? Who chooses? Who pays? Who profits?Incentives
Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. Jarke
M. KravcikR. Klamma
Z. Petrushyna
JTEL Summer School
June 2010Slide 18
Proposed SolutionAS always wins over RS – do not confuse our AS (Thaler & Sunstein)Libertarian paternalism: preserves liberty and tries to influence choices in a way that will make choosers better off, as judged by themselves
• This influence can be realized via suitable alerts or nudges• A nudge should alert people’s behavior in a predictable way and at the same time it should be easy and cheap to avoidThe golden rule of libertarian paternalism: offer nudges that are most likely to help and least likely to inflict harm
Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. Jarke
M. KravcikR. Klamma
Z. Petrushyna
JTEL Summer School
June 2010Slide 19
ROLE Approach to the Design of Learning Experiences
guidance & freedom of
learner
motivation of learner (intrinsic,
extrinsic)
stimulation of learner’s meta-
cognition
collaboration & good practice sharing among
peers
personalization & adaptability to learner & context What is the impact of
these findings from behavioral & cognitive psychology on design
of learning?
Goal settingPlanningReflection
Control & ResponsibilityRecommendation
Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. Jarke
M. KravcikR. Klamma
Z. Petrushyna
JTEL Summer School
June 2010Slide 20
OverviewIntroduction (30 min)
Collaboration:• discussion in groups of 4 (15 min)• presentation of outcomes (15 min)• clustering of outcomes (15 min)
Summary:• discussion & feedback (15 min)
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