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

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

BackgroundKnowledge Management InstituteCompetence Center for Knowledge Technologies Interdisciplinary team of about 45 researchers Bridge between science and industry > 400 applied research projects over last 10 years 2 © Know-Center 2011

Power Tools for the Brain 3 © Know-Center 2011

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

Overview Challenges for Competence Development Work-integrated Learning Spectrum of Learning Guidance Software Framework for Work-integrated Learning Support (APOSDLE) Application Domains Evaluation Results 5 © Know-Center 2011

Challenges for Competence DevelopmentIncreasing 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information is doubling every 2 years Half of what technical degree students learn in their first year of study will be outdated by their third year of study 6 © Know-Center 2011

Solutions?US$ 70 billion spent yearly on formal training [Haskell, 1998] Seminars and Courses Learning Management Systems Web-based Tutors LifeLongLearning ProgramsWhy formal training does not suffice << 30% learning transfer from formal training to professional workplace [Robinson, 2003] 7 © Know-Center 2011

Myths about LearningMYTH 1: ‘Stock-piling’ learning is possible false it is hard to predict which knowledge will be needed in 2 yearsMYTH 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 8 © Know-Center 2011

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 WorkDaily 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 9 © Know-Center 2011

Key Distinctions: Learning Perspective Real Time Real Work Environment Work-integrated Learning Real Content 10 © Know-Center 2011

Supporting Work-Integrated Learning Assisting knowledge workers in advancing their knowledge and skills directly during their real work tasks Recommender Knowledge WorkDaily 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

Varying Degrees of Learning GuidanceSuggest Artifacts: contextualized recommendation of knowledge artifacts GSuggest People: contextualized recommendation of people UBrowse: navigational support and visualization based on I underlying knowledge structures DLearning Hints: expose relations to surrounding topics AShared Collections: share artifacts, insights, and links to people N CCollaboration Wizard: scripted support for collaboration ETriggering Reflection: visualize competences in user profileLearning Paths: learning resources ordered according to prerequisite relations 12 © Know-Center 2011

Key Distinctions: Technical PerspectiveApplication 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 13 © Know-Center 2011

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

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) ModelOrganizationalIT-Infrastructure Utilizing knowledge resources 15 from within organization © Know-Center 2011

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 16 © Know-Center 2011

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) 54,5 43,5 3 Mean2,5 2 SD1,5 10,5 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

Summative Evaluation: ResultsUsers 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 collectionsMost 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 usageAre learning support measures derived from instructional theories which are focusing on formal learning contexts not very relevant for learning at work? 20 © Know-Center 2011

Summative Evaluation: ResultsWIL 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’ 21 © Know-Center 2011

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

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