ieee p1589 'arlem' virtual meeting, september 9, 2015
TRANSCRIPT
A Standard forAugmented RealityLearning Experience Models(AR-LEM)
Fridolin Wild1), Christine Perey2)1) The Open University, UK 2) Perey Research and Consulting, CH
Call for Potentially Essential Patents
If anyone in this meeting is personally aware of the holder of any patent claims that are potentially essential to implementation of the proposed standard(s) under consideration by this group and that are not already the subject of an Accepted Letter of Assurance: Either speak up now or
Provide the chair of this group with the identity of the holder(s) of any and all such claims as soon as possible or
Cause an LOA to be submitted
Overview and discussionxAPI integration
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Fridolin Wild1), Paul Lefrere1), Maurizio Megliola2), Gianlugi De Vito2), Roberto Sanguini3) 1) The Open University, UK 2) Piksel, Italy, 3) AgustaWestland, Italy
“control of one's bodily motions,
the capacity to handle objects skillfully, a sense of
timing, a clear sense of the goal of a physical
action, along with the ability to train responses”
-- Gardner, 2011
Bodily-Kinaesthetic Intelligence
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• World of Repetition (that enabled long cycles of training to be cost-justified) … is gone!
• Short-run or personalised production (with advanced machinery and lower demand in physical dexterity) … is emerging!
• Learning Design Languages (Laurillard & Ljubojevic, 2011; Fuente Valentin, Pardo, & Degado Kloos, 2011; Mueller, Zimmermann, & Peters, 2010; Koper & Tattersall, 2005; Wild, Moedritscher, & Sigurdarson, 2008)
enable modelling of experiences, but fall short of Capturing and codification– Handling hybrid (human-machine) experiences
Context: Professional training
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BLUE COLLAR WORKER EXPERIENCE
Enquire
Mix
Match
Optimise
?
Traces
Need, Problem
ActivityXML incl.
Constraints
Report,Analytics
Suggestion, Recommendation
Classifiable? Known?
Unknown?
• Navigational positioning in
taxonomy• Discovery
support
• Selection of existing mixes with ranked
search• Authoring of new or
modified mixes
• Personalised suggestions for
improvement of mix / experience
tracking
Constraints:• e.g. returned tool 15• e.g. watched video A
• e.g. 14/15 in PT session• e.g. 0 FOD problems
• e.g. energy < daily limit
activities:• e.g. job cards
• e.g. tasks• e.g. learning paths
• Queries• (Reasoning)
Learning by Experience
(Wild et al., 2014; Wild et al., 2013)
The eXperience API
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curl -X POST --data @example.json
-H "Content-Type: application/json"
-- user 465ea716cebb2476fa0d8eca90c3d4f594e64b51
http://www…
API call
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Example statement
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Example trace (Helicopter Industry)
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user cleaned
‘corrosion inhibitors’
Open Source LRS: LearningLocker
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Analytics: cRunch
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plot(net, usearrows = TRUE, usecurve = T)
“verbs that refer to physical actions are naturally grounded in
representations that encode the temporal flow of events“
-- Roy, 2005:391
Verbs of handling and motion
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• Specify force dynamics (out of a limited set)• Specify temporal Allen relations
(A ends after B, A starts with B, …)• Specify valency (number of arguments, cf. Palmer et al., 2005)• Primitives are ‘movement’, ‘path’, and ‘location’ (Chatterjee,
2001)
Any higher-level composition can be traced back to these atomic relations
Verbs of handling and motion
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General taxonomy across applications (Robertson and MacIntyre, 2009)
– ‘Style’ strategies: ‘include’, ‘visible’, ‘find’, ‘label’, ‘recognizable’, ‘focus’, ‘subdue’, ‘visual property’, ‘ghost’, and ‘highlight’ (p.149f)
– Communicative goals signified by these styling operations for visual overlays are: ‘show’, ‘property’, ‘state’, ‘location’, ‘reference’, ‘change’, ‘relative-location’, ‘identify’, ‘action’, ‘move’, and ‘enhancement’ (p.148)
Need to be complemented with workplace specific taxonomy (of handling and motion)
Communicative intent
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xAPI verb statements Helicopter Industry
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• Application-driven versus tracking-driven drop statements
• Behaviour validation using automated tracking:– State-based:
Removed cap: cap was there, cap not there– Location-based:
Removed screw: returned screw to used parts bucket– Dependancy-based:
Removed screw: picked up replacement screw• Related industries (Furniture + Textiles):
common core?
Challenges
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Ralf Klamma, RWTH Aachen, GermanyRequirements Bazaar
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Lehrstuhl Informatik 5(Information
Systems)Prof. Dr. M. Jarke
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LearningLayers Requirements Bazaar – Involving End
Users in Requirements Engineering End User Involvement
– Open Innovation [Chesbrough, 2003]
– ideas from the long tail [Anderson, 2006]
– emerging from practices, needs adoption [Denning, 2004]
Requirements Bazaar [Klamma et al., 2011], [Renzel et al., 2013]
– social continuous innovation platform– listening to the long tail communities– mobile first Web application
Lehrstuhl Informatik 5(Information
Systems)Prof. Dr. M. Jarke
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LearningLayers
https://requirements-bazaar.org
Lehrstuhl Informatik 5(Information
Systems)Prof. Dr. M. Jarke
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LearningLayers
DevOps Life Cycle Rapid release cycles Strong feedback loop
Continuous integration Continuous delivery Continuous deployment
Containerized microservices
Lehrstuhl Informatik 5(Information
Systems)Prof. Dr. M. Jarke
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LearningLayers DevOpsUse Life Cycle
for Continuous InnovationInvolving end users in the designand development process
ideas and needs co-design beta testing context adaptation awareness
Lehrstuhl Informatik 5(Information
Systems)Prof. Dr. M. Jarke
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LearningLayers Requirements Bazaar 2.0 for
Continuous Innovation Redeveloped version launched in April 2015
– Increased maintainability– Based on componentized architecture– Mobile first responsive Web design
Incorporating DevOpsUse practices
Requirements Bazaar Kanban Board Responsive Design
Open Problems
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Task Force and Open Problems
Task Forces identified:– Use Cases, Storyboards, and Requirements– Test Battery (Authoring of ARLEMs)– Reference Implementations– Validator Service– Pilots: Running Code
Open Problems– Real-time messaging (multiuser, multi-device, smart objects)– Revision needed: xAPI auto-logging– query language for constraint validation– Performance analytics– LEM aggregator (‘Open LEM’)
The END