2011 11 11 (uc3m) emadrid slindstaedt kmi tug computational support for work-integrated learning
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Transcript of 2011 11 11 (uc3m) emadrid slindstaedt kmi tug computational support for work-integrated learning
gefördert durch das Kompetenzzentrenprogramm
www.know-center.at
© Know-Center 2011
Computational Support for Work-integrated Learning
November 11, 2011
Stefanie Lindstaedt
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Background
Knowledge Management Institute
Competence Center for Knowledge Technologies
Interdisciplinary team of about 45 researchers
Bridge between science and industry
> 400 applied research projects over last 10 years
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Power Tools for the Brain
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3. Invent
5. Learn1. Discover
2. Visualize 4. Mature
Power Tools for the Brain
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Overview
Challenges for Competence Development
Work-integrated Learning
Spectrum of Learning Guidance
Software Framework for Work-integrated Learning Support (APOSDLE)
Application Domains
Evaluation Results
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Challenges for Competence Development
Increasing time to competence
7 years for a new engineer entering the corporation [Shell]
Increasing unpredictability
Increasing fluctuation (25% of workers have been with their current employer for less than 1 year)
The amount of new technical information is doubling every 2 years
Half of what technical degree students learn in their first year of study will be outdated by their third year of study
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Solutions?
US$ 70 billion spent yearly on formal training [Haskell, 1998]
Seminars and Courses
Learning Management Systems
Web-based Tutors
LifeLongLearning Programs
Why formal training does not suffice
<< 30% learning transfer from formal training to professional workplace
[Robinson, 2003]
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Myths about Learning
MYTH 1: ‘Stock-piling’ learning is possiblefalse it is hard to predict which knowledge will be needed in 2 years
MYTH 2: Separation of working and learning activities is possiblefalse they are deeply intertwined [Eraut, 2007]
MYTH 3: Separation of ‘technical content’ and application skills (incl. social skills) is possiblefalse learning happens on tightly intertwined learning trajectories [Hirsh, 2007]
‘A shift from training to learning is sorely needed.’ [CIPD, professional body for trainers and HR managers in the UK, 2004]
Continuous learning at work
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Courses▪ pre-described goals▪ structured topic ▪ ask teacher▪ general▪ strong learning guidance
Daily Work▪ short term goals▪ spontaneous search ▪ ask colleagues ▪ work context▪ no learning guidance
Work-integrated Learning▪ learning goals deduced from work context▪ intelligent information delivery based on
work context & competencies▪ using & extending organization wide knowledge▪ dynamic building of learning groups▪ varying degrees of learning guidance
Knowledge Work
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Key Distinctions: Learning Perspective
Real Time
Real Work Environment
Real Content
Work-integrated Learning
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Courses▪ pre-described goals▪ structured topic ▪ ask teacher▪ general▪ strong learning guidance
Daily Work▪ short term goals▪ spontaneous search ▪ ask colleagues ▪ work context▪ no learning guidance
Knowledge Work
Assisting knowledge workers in advancing their knowledge and skills directly during
their real work tasks
Recommender
Supporting Work-Integrated Learning
Descriptivelearningguidance
Prescriptivelearningguidance
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Varying Degrees of Learning Guidance
Suggest Artifacts: contextualized recommendation of knowledge artifacts
Suggest People: contextualized recommendation of people
Browse: navigational support and visualization based on underlying knowledge structures
Learning Hints: expose relations to surrounding topics
Shared Collections: share artifacts, insights, and links to people
Collaboration Wizard: scripted support for collaboration
Triggering Reflection: visualize competences in user profile
Learning Paths: learning resources ordered according to prerequisite relations
GUIDANCE
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Key Distinctions: Technical Perspective
Application of semantic technologies together with ‘soft computing’ approaches
Automatic discovery of user context based on user interactions
Automatic inference of user competencies based on task executions
Automatic extraction and mapping of semantic structures based on analysis of backend systems
Automatic identification of similarities based on text, multi-media data and semantic analysis
Automatic maintenance of similarity measures and user profiles based on usage data and user feedback
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Robert, Innovation Consultant
… Kick-Off Meeting
User Model
Tasks: • kick-off meeting organized (21) • project proposal developed (57)
Knowledge:• customer-relationship skills (++) • kick-off methods (+)
Creativity Workshop
KnowledgeGap Analysis Recommendations
• Learning Opportunities• Snippets• Documents• Experts & Peers• Learning Hints• Learning Paths•
Adaptive Sytem for WIL Support
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Software Framework for WIL Support
Sensors Functions embedded in Tools
LearningGoal
Model
ProcessModel
Domain Model
(Ontology)
OrganizationalIT-Infrastructure
HybridKnowledgeServices
Clients
Users
User ModelUser Context
UICO
Domain independent
Recommendations
Context aware
Utilizing knowledge resourcesfrom within organization
Adaptive
Varying degrees oflearning guidance
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Software Framework for WIL Support
APOSDLE: Advanced Process-Oriented Self-DirectedLearning Environment (www.aposdle.org)
Offers support on varying degrees of learning guidance for learning activities within work and learning processes
Domain independent: semantic models, repurposing content from organizational memory for learning (text & multi-media), and employees as learning peers and “tutors”
Considerably reduced modelling efforts for creating domain-specific installations
Embedded within computational work environment
Stable and good level of usability
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September 30, 2010 / 17
Client
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Application Domains
Simulation ofElectromagnetic Effects
on Aircraft
Intellectual Property RightsManagement
Requirements Engineering
Process
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Summative Evaluation: Methodology
⇒Goal: Evaluation of WIL environment effect on learning at work
⇒Setting: workplaces at 3 enterprises for a duration of 3 months
⇒Domains: electromagnetic simulation (EADS), innovationmanagement (ISN), intellectual property rights management(CCI)
⇒Data: User diaries, log data, on-site visits and interviews, exit questionnaire
00,5
11,5
22,5
33,5
44,5
5
Learning material
relevant to current task
Aware of learning material
Existing knowledge improved
High quality learning material provided
APOSDLE learning helped task completion
Learning time planned and
managed
Experts accurately sorted by relevance
Exit questionnaire results (5 = Strongly agree, 0 = Strongly disagree)
Mean
SD
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Summative Evaluation: Results
Users clearly favored features with low learning guidance
learners extensively used the different functionalities to browse and search the knowledge structures, followed the provided content suggestions, and collected relevant learning content within collections
Most features with strong learning guidance were rarely used
Hints, notes, and learning paths were only sporadically used and mainly to explore their functionalities
Users did not use Reflection Support to reflect on their activities but rather to examine the environment’s perception of their usage
Are learning support measures derived from instructional theories which are focusing on formal learning contexts not very relevant for learning at work?
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Summative Evaluation: Results
WIL support improves task completion ability and supports learning
Domain: Very effective for highly specialized, stable domains in which knowledge is captured in working documents; less effective in broad, customer-driven domains in which knowledge is mainly share in person
Experience Level: Most effective for learners and inexperienced employees in ‘information seeking mode’
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iAPOSDLEwww.APOSDLE.org
Thanks for your Attention!
see also APOSDLE video on YouTube