2011 11 11 (uc3m) emadrid slindstaedt kmi tug computational support for work-integrated learning

1. November 11, 2011
www.know-center.at
Computational Support
for Work-integrated
Learning
Stefanie Lindstaedt
© Know-Center 2011 gefördert durch das Kompetenzzentrenprogramm

2. 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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© Know-Center 2011

3. Power Tools for the Brain
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4. Power Tools for the Brain
3. Invent
2. Visualize 4. Mature
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1. Discover 5. Learn © Know-Center 2011

5. 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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6. 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year of study will be outdated by their third year of study
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7. 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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8. Myths about Learning
MYTH 1: ‘Stock-piling’ learning is possible
false it is hard to predict which knowledge will be needed
in 2 years
MYTH 2: Separation of working and learning activities is
possible
false they are deeply intertwined [Eraut, 2007]
MYTH 3: Separation of ‘technical content’ and application skills
(incl. social skills) is possible
false 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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9. 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
Daily Work Courses
▪ short term goals ▪ pre-described goals
▪ spontaneous search ▪ structured topic
▪ ask colleagues ▪ ask teacher
▪ work context ▪ general
▪ no learning guidance ▪ strong
learning guidance
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10. Key Distinctions: Learning Perspective
Real Time
Real Work Environment Work-integrated Learning
Real Content
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11. Supporting Work-Integrated Learning
Assisting knowledge workers in advancing
their knowledge and skills directly during
their real work tasks
Recommender
Knowledge Work
Daily Work Courses
▪ short term goals
Descriptive ▪ pre-described goals
Prescriptive
▪ spontaneous search ▪ structured topic
learning
▪ ask colleagues
learning
guidance ▪ ask teacher
guidance
▪ work context ▪ general
▪ no learning guidance ▪ strong 11
learning guidance
© Know-Center 2011

12. Varying Degrees of Learning Guidance
Suggest Artifacts: contextualized recommendation of
knowledge artifacts
G
Suggest People: contextualized recommendation of people
U
Browse: navigational support and visualization based on
I
underlying knowledge structures
D
Learning Hints: expose relations to surrounding topics
A
Shared Collections: share artifacts, insights, and links to people
N
C
Collaboration Wizard: scripted support for collaboration
E
Triggering Reflection: visualize competences in user profile
Learning Paths: learning resources ordered
according to prerequisite relations
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13. 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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© Know-Center 2011

14. Adaptive Sytem for WIL Support
Robert,
Innovation Consultant
Kick-Off Creativity
…
Meeting Workshop
Knowledge
User Model Gap Analysis
Recommendations
Tasks:
• kick-off meeting organized (21)
• project proposal developed (57)
• Learning Opportunities
Knowledge: • Snippets
• customer-relationship skills (++) • Documents
• kick-off methods (+) • Experts & Peers
• Learning Hints 14
• Learning Paths © Know-Center 2011
•

15. Software Framework for WIL Support
Users
Clients
Sensors
Context aware Varying degrees of
Functions embedded in Tools
learning guidance
Hybrid User User Model
Knowledge Context
Adaptive Recommendations
Services UICO
Domain Process
Learning
Model Model
Domain independentGoal
(Ontology) Model
Organizational
IT-Infrastructure Utilizing knowledge resources 15
from within organization
© Know-Center 2011

16. Software Framework for WIL Support
APOSDLE: Advanced Process-Oriented Self-Directed
Learning 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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17. Client
September 30,
2010 / 17
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18. Application Domains
Intellectual Property Rights
Management
Simulation of
Electromagnetic Effects
on Aircraft
Requirements
Engineering
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Process
© Know-Center 2011

19. 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), innovation
management (ISN), intellectual property rights management
(CCI)
⇒ Data: User diaries, log data, on-site visits and interviews, exit
questionnaire
Exit questionnaire results (5 = Strongly agree, 0 = Strongly disagree)
5
4,5
4
3,5
3 Mean
2,5
2 SD
1,5
1
0,5
0
Learning Aware of Existing High quality APOSDLE Learning time Experts 19
material learning knowledge learning learning helped planned and accurately
relevant to material improved material task completion managed sorted by
© Know-Center 2011
current task provided relevance

20. 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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© Know-Center 2011

21. 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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22. Thanks for your Attention!
see also APOSDLE video on YouTube
iAPOSDLE
www.APOSDLE.org
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© Know-Center 2011