Скачать презентацию Models of Context Why So we Скачать презентацию Models of Context Why So we

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Models of Context • Why? – So we talk about it, write about it, Models of Context • Why? – So we talk about it, write about it, argue about it – So we can show it to the user – So the user can understand it… – …and change it

Our model • Context as a dynamic process with historic dependencies • Context is Our model • Context as a dynamic process with historic dependencies • Context is comprised of a series of context states, like scenes in a movie

Context awareness for MOBIlearn • We have developed: – An interactional model of context Context awareness for MOBIlearn • We have developed: – An interactional model of context – A software implementation of that model – Web service interface for the software – Integrated prototype using sensor inputs

 • Context Awareness Purpose: – Context enables appropriate action - in this case • Context Awareness Purpose: – Context enables appropriate action - in this case learning • Process: Context = a dynamic and historical process… …constructed from context states… … which are constructed through interaction between actors, situations, objects and activities. . . etc

Context Model Context What’s going on over time Context State Elements from the Learning Context Model Context What’s going on over time Context State Elements from the Learning and Setting at one particular point in time, space, or goal sequence Context Substate Elements from the Learner and Setting that are relevant to the current focus of learning and desired level of context awareness

Mapping this on to metadata Metadata Content = Learning Objects + Resources + Services Mapping this on to metadata Metadata Content = Learning Objects + Resources + Services Learner + Setting Context awareness = Context Substate

Basic Operation 1. Context features acquired or input 2. Context substate constructed from context Basic Operation 1. Context features acquired or input 2. Context substate constructed from context features 3. Unsuitable content excluded 4. Remaining content ranked using current context state 5. Rankings output to delivery subsystems

Architecture Content Server Content XML Content metadata Environment Content recommendations Sensors XML Context Awareness Architecture Content Server Content XML Content metadata Environment Content recommendations Sensors XML Context Awareness Subsystem XML User profile XML User input

Objectives • Use a model of context to dynamically select content • Implement tracking Objectives • Use a model of context to dynamically select content • Implement tracking system to provide real-time user location to the context system • Evaluate technical issues surrounding implementation • Perform trials of the system in mock-up gallery

Current status at Uo. B • Context Awareness Subsystem – – Java implementation Available Current status at Uo. B • Context Awareness Subsystem – – Java implementation Available as a web service Reads metadata from available content Provides recommendations • User tracking – Ultrasound positioning system – Tracking device attached to learner’s i. Paq • Content delivery – Pushed delivery of simple XHTML content to viewer on i. Paq

Context awareness • Two factors: – Where is the user? – How long have Context awareness • Two factors: – Where is the user? – How long have they been there? • Content recommended based on painting (from position) and inferred level of interest (from time) – 10 s = low = short title – 20 s = medium = short description – 30 s = high = full text

Test set-up • Trials to be run at Nottingham Castle Museum in September • Test set-up • Trials to be run at Nottingham Castle Museum in September • Testing underway in mock-up art gallery • Technology: – – Ultrasound positioning sensors Wireless PDAs Content & metadata server Other MOBIlearn system services • Collaborative services • Multimedia streaming • Soon to be installed: – RFID tags & readers for i. Paqs

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User Trials • Small scale user trials, December 2003 • Using limited contextual data. User Trials • Small scale user trials, December 2003 • Using limited contextual data. . . – – – Location of others Current question Questions answered by others Time on question • . . . to modify content: – Painting/artist details – Recommended next question

Title • . Title • .

Issues from user trials • It works: – Learners able to quickly find relevant Issues from user trials • It works: – Learners able to quickly find relevant information and successfully answer the questions • Open issues: – Interface: crucial to get the representation right – Understanding: some people weren’t quite sure why the system did what it did, and were surprised by the constantly changing list of options – Distraction vs Engagement: offering multiple choices led to sidetracking or encouraged people to further their exploration of the content – Mixed content: need to to distinguish questions, content, physical resources

Navigation metaphor • Context aware navigation of content is replacing the more familiar web Navigation metaphor • Context aware navigation of content is replacing the more familiar web browser metaphor • User interface issues include: – Should we provide web-style navigation (eg Back, Forward, History) – Will users exploit the context metaphor for content navigation (eg movement = navigation) or will it hinder them?

Next 3 -6 months • User trials – Uffizi – Nottingham Castle Museum • Next 3 -6 months • User trials – Uffizi – Nottingham Castle Museum • Development – Display of context model to user – Provision of user controls, eg ‘hold’ button and ‘Why was this recommended to me? ’ – Exploration of ‘context navigation metaphor’ – Use of context history to influence current recommendations