personalisation in learning and teaching
TRANSCRIPT
Personalisation in learning and teaching
Nigel Ford
Department of Information Studies
University of Sheffield
8th HEA ICS Annual Conference
Interests…– Human individual differences– Effective teaching and learning in HE– User modelling for adaptive systems– Information seeking– Creativity
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...
Left-handed equipment...
But does it matter?
Left-handed equipment...
But does it matter?
Yes No
Left-handed equipment...
But does it matter?
Yes No
But is “learning” more like this or this?
Left-handed equipment...
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
Left-handed equipment...
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…
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
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
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...
• 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
• 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
Strong in studying informal, autonomous lifelong learning via information seeking
Personalisation
Strand 1 Strand 2
Strong in studying formal, mediated learning via pedagogy
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
Strong in studying informal, autonomous lifelong learning via information seeking
Personalisation
Strand 1 Strand 2
Strong in studying formal, mediated learning via pedagogy
These are coming together
Strong in studying informal, autonomous lifelong learning via information seeking
Personalisation
Strand 1 Strand 2
Strong in studying formal, mediated learning via pedagogy
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!
Educational informatics systems
• 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
Educational informatics systems
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
Educational informatics systems
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
Educational informatics systems
Learning resource metadata allows knowledge of potential learning resources to be accessed
Subject domain ontology
Pedagogic ontology
Learner metadata allows knowledge of learner characteristics to be accessed
Adaptive behaviour generated by mapping
learner needs and characteristics onto appropriate learning
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
Educational informatics systems
Learning resource metadata allows knowledge of potential learning resources to be accessed
Pedagogic ontology
Learner metadata allows knowledge of learner characteristics to be accessed
Adaptive behaviour generated by mapping
learner needs and characteristics onto appropriate learning
resources
The elements become components which can interact with different systems
Subject domain ontology
Educational informatics systems
Learning resource metadata allows knowledge of potential learning resources to be accessed
Pedagogic ontology
Learner metadata allows knowledge of learner characteristics to be accessed
Adaptive behaviour generated by mapping
learner needs and characteristics onto appropriate learning
resources
The elements become components which can interact with different systems
Subject domain ontology
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
Higher education ontologyuniversity is an organisation staff academic professor lecturer administrative students
etc.
Organisations ontologymanagement structure staff salary job specification conditions of service email address committees
etc.
A head of department is a professor.Academic staff teach courses.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
Educational informatics
EducationComputing
Information science
Information science has long been concerned with metadata (cataloguing and indexing), ontologies (classification and thesauri) and with information seeking
Educational informatics
• “The development, use and evaluation of digital systems
• which use pedagogical knowledge
• to engage in or facilitate resource discovery
• in order to support learning.”
Educational informatics
• 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
Educational informatics
• Within educational informatics there is a great range of perspectives and approaches
• Systems may be designed to support– Individual acquisition-based learning
approaches– Social collaborative approaches– Meta-cognitive empowerment approaches
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
Reasoning mechanisms
Adaptive systems
Learner A needs to learn about X (metadata)
Knowledge of particular resources
Knowledge of particular pedagogical approaches
Topic X has prerequisites Y and Z (ontology)
Reasoning mechanisms
Adaptive systems
Learner A does not know about Y and Z (metadata)
Knowledge of particular resources
Knowledge of particular pedagogical approaches
Topic X has prerequisites Y and Z (ontology)
Reasoning mechanisms
Adaptive systems
Learner A needs to know about Y and Z
Knowledge of particular resources
Knowledge of particular pedagogical approaches
Reasoning mechanisms
Topic X has prerequisites Y and Z (ontology)
Adaptive systems
Learner A needs to know about Y and Z
Knowledge of particular resources
Knowledge of particular pedagogical approaches
Topics Y and Z have no prerequisites (ontology)
Reasoning mechanisms
Adaptive systems
Learner A needs to know about Y and Z
Select resources that teach Y and Z (metadata)
Knowledge of particular pedagogical approaches
Reasoning mechanisms
Topics Y and Z have no prerequisites (ontology)
Adaptive systems
Learner A has learning style J (metadata)
Knowledge of particular pedagogical approaches
Topics Y and Z have no prerequisites
Reasoning mechanisms
Select resources that teach Y and Z (metadata)
Adaptive systems
Learning style J would benefit from presentation mode F and sequencing mode H
Topics Y and Z have no prerequisites
Reasoning mechanisms
Learner A has learning style J (metadata)
Select resources that teach Y and Z (metadata)
Adaptive systems
Select resources that teach Y and Z, and have presentation mode F (metadata)
Topics Y and Z have no prerequisites
Learning style J would benefit from presentation mode F and sequencing mode H (ontology)
Learner A has learning style J (metadata)
Reasoning mechanisms
Adaptive systems
Select resources that teach Y and Z, and have presentation mode F (metadata)
Topics Y and Z have no prerequisites
Learning style J would benefit from presentation mode F and sequencing mode H (ontology)
Learner A has learning style J (metadata)
Reasoning mechanisms
Now sequence the resources according to mode H
Adaptive systems
Learner A needs to know about Y and Z
Select resources that teach Y and Z
Knowledge of particular pedagogical approaches
Topics Y and Z have no prerequisites
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
Learner A needs to know about Y and Z
Select resources that teach Y and Z
Knowledge of particular pedagogical approaches
Topics Y and Z have no prerequisites
Reasoning mechanismsThese may be stored in structured specialist learning object repositories
Adaptive systems
Learner A needs to know about Y and Z
Select resources that teach Y and Z
Knowledge of particular pedagogical approaches
Topics Y and Z have no prerequisites
Reasoning mechanismsHowever, a number of systems are attempting to extend to unstructured open corpus information sources
Adaptive systems
Learner A needs to know about Y and Z
Select resources that teach Y and Z
Knowledge of particular pedagogical approaches
Topics Y and Z have no prerequisites
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)
Argumentation ontologies
• 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.
Educational informatics
• Within educational informatics there is a great range of perspectives and approaches
• Systems may be designed to support– Individual acquisition-based learning
approaches– Social collaborative approaches– Meta-cognitive empowerment approaches
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
Educational informatics
• Within educational informatics there is a great range of perspectives and approaches
• Systems may be designed to support– Individual acquisition-based learning
approaches– Social collaborative approaches– Meta-cognitive empowerment approaches
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
Learner 2 uses the resource
Metadata from learner 2
Learner 3 uses the resource
Metadata from learner 3
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)
Educational informatics
• 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
• 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
Educational informatics
• 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
Educational informatics
• Moving towards the Learning Web?
Inference engine offers individually filtered information
for the learner
Learner metadata
Resource metadata
Pedagogical ontologies
Subject domain ontologies
Argumentation 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
Argumentation 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 …”
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
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.
Ontologies
• Ontologies can “speak to” each other, thereby enabling more powerful reasoning to take place
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
Higher education ontologyuniversity is an organisation staff academic professor lecturer administrative students
etc.
Organisations ontologymanagement structure staff salary job specification conditions of service email address committees
etc.
A head of department is a professor.Academic staff teach courses.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
Top part of the image Bottom part of the image
????Integrating theme?
Top part of the image Bottom part of the image
????Integrating theme?
Top part of the image Bottom part of the image
Failure to establish a satisfactory Integrating
theme
Cannot synthesise the 2 elements into a 3D
object
Top part of the image Bottom part of the image
Failure to establish a satisfactory Integrating
theme
The image is 2D – lines on paper
Cannot synthesise the 2 elements into a 3D
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
Top part of the image Bottom part of the image
Failure to establish a satisfactory Integrating
theme
The image is 2D – lines on paper
Cannot synthesise the 2 elements into a 3D
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