Personalisation in learning and teaching

Personalisation in learning and teaching

Nigel Ford

Department of Information Studies

University of Sheffield

8th HEA ICS Annual Conference

[email protected]

Interests…– Human individual differences– Effective teaching and learning in HE– User modelling for adaptive systems– Information seeking– Creativity

mailto:[email protected]

Diverse themes

• Some interesting work is reported at this conference– Synoptic assessment

• stressing knowledge of inter-relationships between areas, complementing more narrowly focussed assessment

– Different metaphors for teaching programming• use of story telling metaphors with the availability

of visual programming environments, complementing algorithmic approaches

Diverse themes

– Catering for students with different levels of expertise

– Managing student perceptions, expectations and disillusionment

– Managing students’ motivations and self-confidence and anxiety

– Enculturating and empowering students

Diverse themes

– Enhancing the research-teaching nexus– Support for collaborative learning via wikis– Use of mobile computing and podcasts– Development of learning objects

Diverse themes

• The territory is characterised by rich diversity

Diverse themes

• The territory is characterised by rich diversity

• As are our students!

Diverse themes

• In fact – in some ways we’re all the same, and in other ways we’re all different...

What do these people have in common?

They’re bothLEFT HANDED

What do these people NOT have in common?

Left-handed equipment...


But does it matter?


But does it matter?

Yes No


But does it matter?

Yes No

But is “learning” more like this or this?


But does it matter?

Yes No

And what’s the equivalent of

“equipment”?

Individual differences

• From one perspective we may regard the vehicles for learning we provide for our students as the equivalent of “equipment”– learning resources– the navigation we provide for them to find and

find their way through them– learning designs including the tasks we set

them


But does it matter?

Yes No

And what’s the equivalent of left/right

handed in terms of learning?

Individual differences

• From one perspective we may regard the vehicles for learning we provide for our students as the equivalent of “equipment”– learning resources– the navigation we provide for them to find and find

their way through them– learning designs including the tasks we set them

• And mental (cognitive and affective) differences as the equivalent of “handedness”

People are different across many dimensions...

“Not very practical”

A “safe pair of hands”Creative

ReflectiveImpulsive

“Hands on”

Very visual Good with words

A “one thing at a time” personA “multi-tasker”

T E

N S I O

N S

There are many dimensions...

• Deep and surface approaches• Field-dependent and field-independent• Sequential and parallel processors• Description-builders and procedure-builders• Divergent and convergent• Dualistic and relativistic• Visualisers and verbalisers• Socially-oriented and analytic

Personalisation

• But is matching educational “equipment” to learners’ individual differences feasible?

• And does it matter?

Styles…

• Wendy’s keynote yesterday…– Computer Science [and/or Web Science]

curriculum needs to include “hard” and “soft” disciplinary aspects

• Maths• Social impacts of computing• Hardware engineering• Philosophical engineering• Software design• Ethics

Styles…

• If Computer Science is to become increasingly involved in social aspects of computing– Students with learning styles associated

more with social sciences may need to be accommodated

– “Existing” CS students will be learning more social science-y content

Styles…

• Matching / mismatching– These styles are readily translatable into

different ways of structuring and presenting information – and courses

– Some evidence that matching teaching/presentation with learning styles can significantly affect learning

– Studying in seriously mismatched conditions can contribute to student disillusionment and dropout

Personalisation

• A simple example relates to the routes we ask out students to navigate subject content

Free Controlled

4 5

3

7 8

6

2

1 1 1 2

1 0

1 4 1 5

1 3

9

1

Stereotypical “serialist” style route

Animals

Vertebrates Invertebrates

Warm blooded Cold blooded

Mammals Birds

Learning content

Fish Reptiles

Etc….

Etc….

4 5

3

7 8

6

2

1 1 1 2

1 0

1 4 1 5

1 3

9

1

Stereotypical “global” style route

Animals

Vertebrates Invertebrates

Warm blooded Cold blooded

Mammals Birds Fish Reptiles

Etc….

Etc….

It’s rather like

• Doing a jigsaw…

You can build up the picture step by step in a “one thing at a time” logical order…

Or you can try to

get an overall picture of what it’s all about first

by trying pieces here

and there

Serialists

• Narrow “local” focus• Build up the picture sequentially, step by step• Analyse complex whole into component parts,

and the “big picture” emerges late in the learning process

• “One thing at a time” (master on aspect before moving on to consider another)

• Distracted by non-essential information• High memory load – simple logical

connections

Global learners

• Global focus – quick to get the “big picture”• Establish a good conceptual overview (inter-

connections) before getting down to procedural detail

• Have many things on the go at once• Thrive on analogy and “enrichment” material• Rich idiosyncratic mental associations• Relatively divergent (creative) thought• High tolerance of uncertainty• Exploratory – tentative mapping of the territory

These strategies…

• … are underpinned by more fundamental stylistic differences

Description-building (global)

Procedure-building (analytic)

If description-building is like generating the overall design of a building… (holist)

… then procedure-building is like working out the details of the plumbing, electrics, etc. to realise the design (serialist)

Styles

• There may be characteristic differences in outcome for people with relatively strong stylistic biases.

Styles…

• Analytic– Stronger at step-by-step chains of

detailed logical evidence – Weaker in that s/he may “not see the

wood for the trees”

Styles…

• Global– Stronger building conceptual framework

focusing on topic inter-relationships. How things fit together to form the “big picture”

– Weaker at the detailed logical step-by-step evidence supporting the overall framework

Serialists learn better from

• Material that– Is step by step, one thing at a time – Presents a logical sequence– Gets down to detail early on– Without any extraneous “enrichment”

content

• The overall picture emerges relatively late in the learning process

Global learners prefer [learn better from]

• Material that– Gives an early overview– Maps rich inter-relationships between

concepts… before getting down to details– Provides lots of “enrichment” content

• The overall picture is established early in the learning process

Other styles…

• Field-independent students are likely to – be able to handle and benefit from independence

in learning – Be more adept at analytical algorithmic work

• Field-dependent students more likely to – benefit from having their learning structured for

them– Have a social orientation (good at social

understandings and interactions)

Styles…

• Some students may be strong in divergent (creative) thinking, others in more convergent thinking

• Some links between holists and divergent thinking

Styles…

• Divergent, creative thinking will be needed from students to complement more convergent mastery of basic skills

• Wendy talked yesterday of need for– “motivated students tackling a real and

important set of challenges”

Visualiser/verbaliser differences

Styles…

• Verbaliser/imager differences may be relevant to e.g.– Benefiting from visual programming

environments– Data visualisation

Dimensions of approach

• Some of these echo the old 1970s distinctions between left and right brain hemispheres…

http://images.google.com/imgres?imgurl=www-ucdmag.ucdavis.edu/fall01/graphics/brain.jpg&imgrefurl=http://www-ucdmag.ucdavis.edu/fall01/feature_1.html&h=202&w=185&prev=/images%3Fq%3Dbrain%26start%3D60%26svnum%3D10%26hl%3Den%26lr%3D%26ie%3DUTF-8%26oe%3DUTF-8%26sa%3DN

Right & Left BrainLeft Hemisphere StyleRational / SequentialSequential processingProblem solves by logically and sequentially looking at the partsIs planned and structured Prefers established, certain information Is a splitter: distinction important Is logical, sees cause and effect Draws on previously accumulated, organized informationAnalytic

Right Hemisphere StyleIntuitive / SimultaneousParallel processingProblem solves with hunches, looking for patterns and configurations Is fluid and spontaneous Prefers elusive, uncertain information Connectedness importantIs analogic, sees correspondences, resemblances Draws on unbounded qualitative patterns that are not organized into sequencesSocial

http://www.web-us.com/brain/right_left_brain_characteristics.htm

http://images.google.com/imgres?imgurl=www-ucdmag.ucdavis.edu/fall01/graphics/brain.jpg&imgrefurl=http://www-ucdmag.ucdavis.edu/fall01/feature_1.html&h=202&w=185&prev=/images%3Fq%3Dbrain%26start%3D60%26svnum%3D10%26hl%3Den%26lr%3D%26ie%3DUTF-8%26oe%3DUTF-8%26sa%3DN

But…

• We should avoid simplistic “left/right” dichotomies… – “It is how the two sides of the brain complement and

combine that counts”

'Right Brain' or 'Left Brain' - Myth Or Reality? By John McCrone. The New Scientist http://www.rbiproduction.co.uk New Scientist, RBI Limited 2000

http://www.rbiproduction.co.uk/

But…

• Also more recent evidence of “front/back” brain differences – frontal (right) lobes being associated with

creativity; coordinating connectivity; multi-tasking; shifting attention.

– Other areas with logic / working through the details

• But entailing coordination of both.

“Looking for inspiration” by Helen Phillips. New Scientist, October 2005, 40-42. “The neurologist: all in my brain” by Alice Flaherty New Scientist, October 2005, 49.

But…

• And of high/low activity in the cortex– Inspiration: alpha waves with low cortical arousal

“a relaxed state, as though the conscious mind was quiet while the brain was making connections behind the scenes.”

– Elaboration: increased cortical arousal“more corralling of activity and more organised thinking”

• Creativity requires a toggling between the two– particularly noticeable on the right side of the brain

• Also, areas of brain activity may also vary– according to the domain of creativity (e.g.

mathematicians and writers)

“Looking for inspiration” by Helen Phillips. New Scientist, October 2005, 40-42.

Styles…• Within Computer Science increasingly

– Need to cater for a wider range of learning styles?

– More social science content?– Divergent, creative thinking will be

needed from students to complement more convergent mastery of basic skills?

There are many dimensions...


Personalisation

• Much work designed to deliver a degree of personalisation…

Personalisation

• Two broad strands– High pedagogical control, with relatively low

volume/diversity of information.• Computer-assisted learning, intelligent tutoring

systems, adaptive hypermedia

– Learner freedom in selecting diverse topics/problems but less pedagogic control.

• Project- and inquiry-based learning, entailing high levels of independent information seeking

Strong in studying informal, autonomous lifelong learning via information seeking

Personalisation

Strand 1 Strand 2

Strong in studying formal, mediated learning via pedagogy


Personalisation

Strand 1 Strand 2


Links resolved at learning (query) time

Links established pre-learning

Means that you select a particular learning design for students

Also means that you select a particular learning design for students – entailing independent information seeking


Personalisation

Strand 1 Strand 2


These are coming together


Personalisation

Strand 1 Strand 2


These are coming together

as educational informatics systems attempt to blend pedagogical

mediation with information seeking and resource discovery

Educational computing

EducationComputing

Information science

Educational informatics systems

• Entail– Domain representations– Models of learners– Pedagogical models– Machine reasoning techniques

Nothing new!


• Entail– Domain representations– Models of learners– Pedagogical models– Machine reasoning techniques

Educational computing has been using these to develop intelligent learning systems since the 1970s


Learning resource metadata allows knowledge of potential learning resources to be accessed

Subject domain ontology

Pedagogic model

Learner metadata allows knowledge of learner characteristics to be accessed

Adaptive behaviour generated by mapping

learner needs and characteristics onto appropriate learning

resources

model

indexingmodel




Pedagogic ontology




resources

The development of standardised ways of describing and structuring different components allows inter-operability and the discovery and

sharing of components

model

indexingmodel

model



Pedagogic ontology




resources

The elements become components which can interact with different systems




Pedagogic ontology




resources

The elements become components which can interact with different systems


Metadata standards Ontology standards

Web service standards

Joined up reasoning

• Ontologies can speak to each other

Higher education ontologyuniversity is an organisation staff academic professor lecturer administrative students

etc.

A head of department is a professor.Academic staff teach courses.etc.

Does a head of department have a salary?I DUNNO


etc.

Organisations ontologymanagement structure staff salary job specification conditions of service email address committees

etc.


A head of department therefore has a salary, job specification, etc.

But if we specify that this ontology references a separate “organisations” ontology

Educational informatics

• Sharing and resource discovery are central issues within library/information science


EducationComputing

Information science

Information science has long been concerned with metadata (cataloguing and indexing), ontologies (classification and thesauri) and with information seeking


• “The development, use and evaluation of digital systems

• which use pedagogical knowledge

• to engage in or facilitate resource discovery

• in order to support learning.”


• Within educational informatics there is a great range of perspectives and approaches

• Systems designed to support– Individual acquisition-based learning

approaches– Social collaborative approaches– Meta-cognitive empowerment approaches



• Systems may be designed to support– Individual acquisition-based learning


Adaptive systems

Knowledge of individual learners (metadata)

Knowledge of particular resources (metadata)

Knowledge of particular pedagogical approaches (educational ontologies)

Knowledge of subject domains (ontologies)

Reasoning mechanisms

Learner A needs to learn about X (metadata)

Knowledge of particular resources

Knowledge of particular pedagogical approaches

Knowledge of subject domains


Adaptive systems

Learner A needs to learn about X (metadata)



Topic X has prerequisites Y and Z (ontology)


Adaptive systems

Learner A does not know about Y and Z (metadata)





Adaptive systems

Learner A needs to know about Y and Z





Adaptive systems




Topics Y and Z have no prerequisites (ontology)


Adaptive systems


Select resources that teach Y and Z (metadata)



Topics Y and Z have no prerequisites (ontology)

Adaptive systems

Learner A has learning style J (metadata)


Topics Y and Z have no prerequisites



Adaptive systems

Learning style J would benefit from presentation mode F and sequencing mode H





Adaptive systems

Select resources that teach Y and Z, and have presentation mode F (metadata)


Learning style J would benefit from presentation mode F and sequencing mode H (ontology)



Adaptive systems

Select resources that teach Y and Z, and have presentation mode F (metadata)


Learning style J would benefit from presentation mode F and sequencing mode H (ontology)



Now sequence the resources according to mode H

Adaptive systems


Select resources that teach Y and Z



Reasoning mechanismsUsing standards to structure these resources as “learning objects” facilitates their sharing and re-use in different contexts and for different purposes

Adaptive systems





Reasoning mechanismsThese may be stored in structured specialist learning object repositories

Adaptive systems





Reasoning mechanismsHowever, a number of systems are attempting to extend to unstructured open corpus information sources

Adaptive systems





Reasoning mechanismsHowever, a number of systems are attempting to extend to unstructured open corpus information sources

Adaptive systems

Via the automatic extraction/generation of metadata

Argumentation systems

• Adding to pedagogical and subject domain ontologies are argumentation ontologies

Argumentation ontologies

• Representing argumentation structures in “knowledge maps” (Stutt and Motta (2004)


• Include, for example, argumentation and debate, reasoning by analogy, constructing narratives by which phenomena can be understood, simulations, or cause/effect models

• At one level, an ontology would describe basic knowledge types, and each knowledge type, in turn, would have its own ontology. – For example, an argumentation/debate ontology could

include concepts such as claim, refutation, support, and confirmation, as well as “debate moves” linked by rhetorical relations.

– A narrative ontology may include actors, events, etc.

Meta-cognitive approaches

• Attempts to build “meta-cognitively facilitating” expertise into IR systems – This work is predicated on a recognition of the

inherently imprecise nature of information and learning needs, and the need to “release” learners’ implicit meta-cognitive knowledge.

Meta-cognitive approaches

• Utilises meta-cognitive “devices” built into retrieval systems...– ...designed to align students’ perceptions of

the information they need for particular academic tasks more realistically to what is likely to be productive

Ecological paradigm

• Moving away from concept of metadata as– authority and consensus based– static and closed– deductively derived

• to metadata as– inductively derived– fluid and open– idiosyncratic and context-bound

Learning resource

Metadata is attached to the resource at the time of use relating to:

Learner characteristics and goals

What the resource is “about” from the learner’s perspective as well as “standard” descriptions from schema

The interaction – e.g. user–perceived usefulness of the resource

Learner 1 uses the resource

Metadata from learner 1





McCalla, G. (2004). The Ecological Approach to the Design of E-Learning Environments. Journal of Interactive Media in Education, 2004 (7). [www.jime.open.ac.uk/2004/7]

Ecological paradigm

• More metadata will be attached to the resource as it is used by different learners, and/or by the same learner on different occasions.

• Thus each time a learning resource is used, a model of the learner and the interaction is attached to it.

• The model represents a context- and time-bound snapshot. Next time the learner accesses the same resource, the model will have changed.

Ecological paradigm

• Over time, each learning resource will accumulate many models.

• This data can then be subjected to data mining in order to discover patterns that are useful in achieving particular tasks e.g.– information retrieval – discovery of resources (and people)

appropriate to fulfilling a particular need– collection weeding

Ecological paradigm

• This distributed modelling applies to learners as well as resources…

Active learner modelling

• “Active learner modelling” entails the development and application of partial and context-bound learner models – in which knowledge of any particular learner is

fragmented amongst the various distributed agents which collect it.

• This information is gradually accumulated as a result of user behaviour in real time.

McCalla, Vassileva, Greer, and Bull (2000) and Vassileva, McCalla, and Greer (2003)

Active learner modelling

• The agents can then use their particular knowledge – again in real time – to negotiate with other agents in order to fulfil whatever task is being worked on at the time.

• This entails analysis of raw data computed as required and specific to particular purposes and contexts of use.

“Intelligent” social tagging

• “CommonFolks”– “Rather than designing an ontology and then

providing instances that fit the ontology for given resources, we are able to skip the design process and begin using instances to describe our resource...

– We can later infer a domain ontology for a set of tags (instances used to describe resources) at any point, based on our own or the communities’ annotations.”

Bateman, Brooks and McCalla (2006)


• A great diversity of approaches…

• Displaying a great variety of teaching pespectives

Competing tensions

Help them individually acquire factual knowledge and fundamental skills

Help them find their own solutions to situated problems

Aim for enculturation and community participation

Inculcate intrinsic interest Assess studentstensions

Transmit to students authority-based “best” knowledge – tell them “how things are done”

Help students develop autonomy

Control what students do

Help students be creativeHelp students perform to

agreed measurable standards

Open-ended “real world” problemsClosed easily assessable problems

Coupled with learner diversity



• Bringing together the notions of– Pedagogical mediation

• of different sorts by different stakeholders (teachers, gatekeepers, communities)

• and at different levels

– Resource discovery• Structured learning resources• Unstructured open corpus information


• Moving towards the Learning Web?

Inference engine offers individually filtered information

for the learner

Learner metadata

Resource metadata

Pedagogical ontologies

Subject domain ontologies


Teacher/course metadata

Web 2.0 style data mining…

…filter by collective intelligence

Learnersneedsstylesetc.

Teachersgoals

preferred pedagogical approaches

etc.Resources

primarysecondaryteaching-oriented

perspectives

Inference engine offers individually filtered information

for the learner

Learner metadata

Resource metadata

Pedagogical ontologies

Subject domain ontologies


Teacher/course metadata

Data mining…

…filter by collective intelligence

Learnersneedsstylesetc.

Teachersgoals

preferred pedagogical approaches

etc.Resources

primarysecondaryteaching-oriented

perspectives

These can be broadcast as RDF triples in discoverable/harvestable form…

…by teachers, communities, institutions, gatekeepers… people with perspectives

(Obligatory Google mention)

• Like adaptive/personalised Google map filters– But numerous and multi-layered– Including predefined and inductively derived

Pervasive problems

Personalisation

• Whilst personalisation may be a force for increasing motivation and intrinsic interest...

But...

• The pressures may be great for some (many) students, possibly resulting in– Being assessment- and cue-focused– Experiencing anxiety to the point of adopting surface

and surface strategic approaches – Choosing what they know and are confident in rather

than more challenging unknown new ground– Intellectual U-turns and creative failures can be bad

news in the context of tight deadlines, cumulative coursework and production-line coursework generation

A pervasive difficulty...

• And much teaching/learning design is predicated on– deep and intrinsic engagement with subject

matter• Whereas much student learning behaviour is

predicated on– surface, calculating and extrinsic approaches,

sometimes “going through the motions” of deep engagement and intrinsic interest where this may be strategically advantageous

A pervasive difficulty...

• In our teaching we must emphasise– Accountability– Achieving agreed measurable standards of

performance– Classifying students’ performance in numbers– Differentiating better from worse students– Foreseeing how doing anything different (“a

good pedagogical idea at the time”) might look in the cold light of litigation

Teacher view

Learner view

Deep Intrinsic interest

Surface extrinsic effects of assessment

Research

Teaching

Creativity

Accountability

Factors conducive to accountability/control

EfficiencyMeasurable performanceAccountabilityTimetablesDeadlinesBudgetsGood administrative proceduresHard workFinancial forecastingMilestones & deliverablesGood planningClear objectives

Accountability/control

“Next logical step”Figuring out answers to what we know we don’t knowCertaintyPlanning and controlPredictabilityClarityPerspirationConvergenceConsensusLogic

Creativity

Gestalt shifts/reconfigurationsFiguring out what we don’t know we don’t knowUncertaintyOpen-endedness & SerendipityRisk and unexpectednessFuzzinessInspirationDivergenceIdiosyncrasyIntuition

Factors conducive tocreativity

Freedom from anxiety & pressure Intrinsic motivationT i m e & s p a c eTalking with colleaguesPublishing for maximum impact not just “best” journalsQuality not “salami publishing”Responsive mode research funding

Conditions

Factors conducive tocreativity

Freedom from anxiety & pressure Intrinsic motivationT i m e & s p a c eTalking with colleaguesPublishing for maximum impact not just “best” journalsQuality not “salami publishing”Responsive mode research funding

Conditions

Factors conducive to accountability/control

EfficiencyMeasurable performanceAccountabilityTimetablesDeadlinesBudgetsGood administrative proceduresHard workFinancial forecastingMilestones & deliverablesGood planningClear objectives

Inspiration New ideas New contacts

Intrinsic motivationRelaxation

Enjoyment

But both

• Both elements of the tension are needed– Too much divergence (serendipity, freedom

and relaxation) un-tempered by sufficient discipline and control may lead to its own excesses…

Vice Chancellor of the New University of Creativity and Serendipity

http://www.psy.msu.ru/illusion/depth.html

University managers and lecturers working on a shared ontology of “university effectiveness”

University managers and lecturers on the upward march to greater effectiveness

Different educational informatics perspectives

Learning elements can be de-contextualised

Context should be preserved since to remove it is to denude of meaning

Closed authority-based centrally produced metadata

Open fluid user-centric and idiosyncratic metadata

Ontologies as representations of established authority consensus

Ontologies as representations of potentially conflicting and provisional multiple perspectives

Control, planning, predictability and certainty in learning.

Unpredictability, open-endedness and uncertainty in learning

Development proceed by analysis and isolation of variables, and accurate specification of procedures (focusing on the parts).

Development proceed by focusing on and preserving and re-configuring the whole (gestalt).

Convergent thinking (learn agreed material to agreed standards)

Divergent thinking (creativity)

Focus on the individual’s cognitive processes. Focus on social interactions.

Technical/logical/objective focus. Social/intuitive focus. Entails handling subjectivity.

Dependence: goal is for the system to supplant selected cognitive processes

Autonomy: goal is to empower people/communities to learn how to learn

Creativity

• Creative thinking arguably requires freedom from – Anxiety– Tight time constraints– Intensely focused thought

Creativity

• Gregory notes (1987: 171): – “The clear implication is that our brains are at their most efficient

when allowed to switch from phases of intense concentration to ones in which we exert no conscious control at all.”

Creativity

• Strauss and Corbin (1997: 29):– “Only under certain conditions will those insights arise …

[Questions and answers] are raised and sought even if on a subliminal level of consciousness, and sometimes for quite a time, before the vital question or answer breaks through to consciousness.

– Although knowledgeable about data and theory, the investigator somehow has to escape the very features of his or her work that may otherwise block the new perspective inherent in the sudden hunch, the flash of insight, the brilliant idea, or the profoundly different theoretical formulation.

– Specific knowledge, alas, is not only necessary but at times constitutes mental baggage that impedes this kind of intellectual creativity.”

Creativity

• Spink and Greisdorf (1997) found an inverse relationship between high levels of relevance – defined in terms of the extent to which retrieved documents matched user queries – and the generation of new ideas and directions by researchers engaged in online searches

Creativity

• “ ‘highly’ relevant items do not change the users’ cognitive or information space in relation to their information problem …

• Highly relevant items may not relate to a shift in a user’s information problem towards resolution but reinforce the current state of the user’s information problem and knowledge state …

• Items retrieved that are not ‘highly’ relevant, but partially relevant, are related to shifts in the users’ thinking about their information problem by providing new information that leads the users in new directions …”

Ontologies


etc.


Ontologies

• Ontologies can “speak to” each other, thereby enabling more powerful reasoning to take place


etc.


Does a head of department have a salary?I DUNNO


etc.

Organisations ontologymanagement structure staff salary job specification conditions of service email address committees

etc.


A head of department therefore has a salary, job specification, etc.

But if we specify that this ontology references a separate “organisations” ontology

The future…

• Web 3.0 will be able to answer any question we throw at it.

The future…

• Web 3.0 will be able to answer any question we throw at it

• Web 4.0 will be able to teach us anything expertly!

The future…

• Web 4.0 will be able to teach us anything expertly

Top part of the image Bottom part of the image

????Integrating theme?

Synthesis

Thesis Antithesis


????Integrating theme?


Failure to establish a satisfactory Integrating

theme

Cannot synthesise the 2 elements into a 3D

object



theme

The image is 2D – lines on paper


object

The image is an optical illusion

Inability to establish an integrating theme at level 1 – is the intended effect of the

image

LEVEL 2

LEVEL 1



theme

The image is 2D – lines on paper


object

The image is an optical illusion

Need to think “outside the box”

But sometimes

• We get “locked into” our boxes

Filters

• The particular lens or filter we look through can affect our perceptions of – Problems– Solutions– Evidence– Ways of gathering and analysing evidence – etc

http://www.andcorp.com/Web_store/Images/Photos/dichroic_sets_sm.jpg

YES NO

YES NO

Personalisation in learning and teaching

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