erp course: knowledge management and learning seeartcielsat...
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ERP Course: Knowledge Management and LearningSee articles at the course web site and in references
Peter Dologdolog [at] cs [dot] aau [dot] dkE2-201Information SystemsOctober 13, 2006
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2Peter Dolog, ERP Course, KMandLearning
A Company
DATA
Material Resources
HumanResources
ProductionTechnology Finances CustomerProduction
Process
Enquire, Evaluate, Manage, and Improve
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3Peter Dolog, ERP Course, KMandLearning
Knowledge Management
Knowledge Management is• A set of systematic and disciplined actions• To get a greatest value from the knowledge available to it
Knowledge in this context include• Experience and understandings of people• IT artifacts (resources, digital objects, documents, …)
Knowledge Management involves:• Organizational, social, and managerial actions• Technology
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4Peter Dolog, ERP Course, KMandLearning
Organizational Learning (Nonaka)
Conversion of knowledge between tacit and explicit forms (bothimportant for organizational effectiveness)
Tacit knowledge• known• derived from experience• embodies beliefs and values• actionable• source of innovation
Explicit knowledge• represented by an IT artifact• used in communication betweem several parties
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5Peter Dolog, ERP Course, KMandLearning
Conversions
Tacit to TacitSOCIALIZATION
e. g. team meetings and discussions
Tacit to ExplicitEXTERNALIZATION(conceptualization, elicitation, and articulation)
e. g. dialog within a team, answerquestions
Explicit to TacitINTERNALIZATION
e. g. learn from a report
Explicit to ExplicitCOMBINATION
e. g. e-mail a report
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6Peter Dolog, ERP Course, KMandLearning
Tacit to Tacit
Tools which support communicationSupplementing or enhancing classical meetingsReplacing face to face meetingsGroupwaresLocating experts
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7Peter Dolog, ERP Course, KMandLearning
Shared experience and knowledge
Asynchronous• Listening to recorded presentation• Using news and virtual discussion forums• Tools to comment and annotate such presentation or a
jointly created documentsSynchronous
• Virtual on line meetings• Voice over IP• Video conferencing• Instant messaging
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8Peter Dolog, ERP Course, KMandLearning
BSCW.de
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9Peter Dolog, ERP Course, KMandLearning
http://kmi.open.ac.uk/
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10Peter Dolog, ERP Course, KMandLearning
Finding a person
Common interest to create an innovationExpert to help with a specific problemTeam creation for a specific assignmentA person for peer reviewSearch engines for peopleBased on profiles
• Explicit evidence• Extracted evidence from portfolios, projects, and activities• Derived evidence from social interactions
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11Peter Dolog, ERP Course, KMandLearning
Tacit to Explicit
Creating a shared mental modelDescribing/externalizing it in an IT artefactProblem tickets in call centersTheir associations to problemsFrequent problems/questionsMetadata, conceptual models, ontologiesFormal concept analysisMatching problem descriptions with existing solutions
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12Peter Dolog, ERP Course, KMandLearning
http://www.saleslogix.com/
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13Peter Dolog, ERP Course, KMandLearning
Explicit to Explicit
CombinationCapturing existing knowledge
• Rewarding people to make documents, problem and solution descriptions
• Use of documents, citations/references, links (page rank in google)
Multimedia analysis – speech recognition (call centers), image searching (design documents)
Search – information retrieval, digital libraries, similaritymeasures, exact databese query, indexing, crawling
Taxonomies, Ontologies, Metadata, Text Classification, Summarization
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14Peter Dolog, ERP Course, KMandLearning
Explicit to Tacit
Dealing with lost in information spaceInformation overloadUsing ontologies to visualizeHiding, summarizationAnotationsAutomatic guidanceShortcutsLocal navigation vs. global navigationTask based navigation – association with business process or task
to be supported
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15Peter Dolog, ERP Course, KMandLearning
Adaptive Course Structure Presentation in Interbook
Indication of your current position
Recommendation annotations (traficlight metaphor)
Visited pages
Guidance
http://www2.sis.pitt.edu/~peterb/
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16Peter Dolog, ERP Course, KMandLearning
Adaptive Link Annotation in Interbook
Current position and upper levelcontent
Additional Guidance
Next items in subsequence
Outcomes and prerequisites
Learner Assesment
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Learning on-line in organizationalcontext
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18Peter Dolog, ERP Course, KMandLearning
Smart (open) spaces
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19Peter Dolog, ERP Course, KMandLearning
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20Peter Dolog, ERP Course, KMandLearning
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21Peter Dolog, ERP Course, KMandLearning
Knowledge Seahttp://www2.sis.pitt.edu/%7Eir/KS/home.htm?kt_sid=470&kt_user=demo
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22Peter Dolog, ERP Course, KMandLearning
http://www.hcd-online.com
To make the personalization process more flexible
To satisfy a user if no results are returned
To enable ranking on results based on documentanalysis
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23Peter Dolog, ERP Course, KMandLearning
Our Approach
The adaptivity seen as decisions among variable resourceswhere decisions are driven by information about a user
Knowledge about:• Resources with metadata seen as constraints on
use• Learner features used for comparing to the
resource metadataHorn logic oriented rules in TRIPLE used to perform the
matching which concludes with personalizationinformation
Horn logic oriented rules in TRIPLE used to construct and rewrite user queries over metadata with restrictionsbased on the user profile
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24Peter Dolog, ERP Course, KMandLearning
eLearning Domain – Metadata Used• Lerning resource
• Concepts/Competencies as learning outcomes• Prerequisites knowledge needed for understanding a
resource• Prerequisites knowledge concepts/competencies to
understand the concepts or to gain competencies• Language used in the resource
• Learner profile• Lerner performance, competencies/concepts previously
acquired and compared to prerequisites of eitherresource/concept/competency
• Language/Concept preferences
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25Peter Dolog, ERP Course, KMandLearning
Knowledge Structure for Resources
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26Peter Dolog, ERP Course, KMandLearning
Knowledge Structure for Domain Concepts – theJava Tutorial
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27Peter Dolog, ERP Course, KMandLearning
Examples in TRIPLE: Instance of a LearningResourcekbs:'Praedikatenlogik3.pdf‚
[dcq:isPartOf -> kbs:'Modul3',dcq:isRequiredBy -> kbs:'Resolution.pdf',dcq:requires ->
kbs:'Praedikatenlogik2.pdf',dc:subject -> acm_ccs:'I.2.4.2.1',dc:language -> lang:de,dc:description -> 'Wie wandele ich Sätze
der Praedikatenlogik in KonjungtiveNormalform um',
dc:title -> 'Vorlesung Künstliche Intelligenz WS 2002 : Umwandlung in KNF
].
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28Peter Dolog, ERP Course, KMandLearning
Knowledge Structure For Learner Features
PerformancePortfoliosGoalsPreferencesPersonal InformationIdentificationTest Performance
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29Peter Dolog, ERP Course, KMandLearning
LearnerPerformance and Competencies
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30Peter Dolog, ERP Course, KMandLearning
Goals and Preferences
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31Peter Dolog, ERP Course, KMandLearning
Example: An Instance of a Learner Performancestudent:student1[papi:performance -> student:performance_1].student:performance_bucket_1[papi:performance_bucket_value -> '10min'].student:performance_bucket_1[papi:performance_bucket_name -> 'time_on_task'].student:performance_1[papi:performance_bucket -> student:performance_bucket_1].student:performance_1[papi:performance_value -> '0.6'].student:performance_1[papi:performance_metric -> '0-1'].student:performance_1[papi:performance_coding -> 'number'].student:performance_1[papi:granularity -> topic].student:performance_1[papi:learning_experience_identifier -> kbs:'Praedikatenlogik2.pdf'].student:performance_1[papi:learning_competency -> acm_ccs:'I.2.4.2.1'].student:performance_1[papi:issued_from_identifier -> kbs:'Test_Praedikatenlogik2.pdf'].
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32Peter Dolog, ERP Course, KMandLearning
Querying Metadata in Edutella
Datalog- and RDF-Based QELCommon internal data model for QueriesSeveral wrappers implemented (file based, relational database,
concept base, ...) to support several metadata storage typesTwo kinds of wrappers:
• Implementing provision service• Implementing consumer (query) service
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33Peter Dolog, ERP Course, KMandLearning
Example: Edutella Query for Resources<rdf:RDF
xmlns:RDFNsId1='http://www.edutella.org/edutella#'xmlns:rdf='http://www.w3.org/1999/02/22-rdf-syntax-ns#'xmlns:rdfs='http://www.w3.org/2000/01/rdf-schema#'><RDFNsId1:QEL3Query rdf:about='http://www.elena.org/gen#query'>
<RDFNsId1:hasResultType rdf:resource='http://www.edutella.org/edutella#TupleResult'/>
<RDFNsId1:hasQueryLiteral><RDFNsId1:RDFReifiedStatement
rdf:about='http://www.elena.org/gen#st0'><rdf:subject
rdf:resource='http://www.elena.org/gen#Resource'
rdf:type='http://www.edutella.org/edutella#Variable'rdfs:label='Resource'/>
<rdf:predicaterdf:resource='http://purl.org/dc/elements/1.1/subject'/>
<rdf:object rdf:resource='http://hoersaal.kbs.uni-hannover.de/rdf/java_ontology.rdf#Variables'/>
</RDFNsId1:RDFReifiedStatement></RDFNsId1:QEL3Query></rdf:RDF>
s(X, <dc:subject>, <java:variables>).
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34Peter Dolog, ERP Course, KMandLearning
Adding Restriction on Language
<rdf:Description rdf:about="&n4;genid0"><n1:type rdf:resource="&n4;RDFReifiedStatement"/>
</rdf:Description><rdf:Description rdf:about="&n4;genid0">
<n1:subject rdf:resource="&n5;Resource"/></rdf:Description><rdf:Description rdf:about="&n4;genid0">
<n1:predicate rdf:resource="&n3;language"/></rdf:Description><rdf:Description rdf:about="&n4;genid0">
<n1:object rdf:resource="&n7;de"/></rdf:Description>
s(X, <dc:subject>, <java:variables>),s(X, <dc:language>, <lang:de>).
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35Peter Dolog, ERP Course, KMandLearning
A Rule for Adding Such RestrictionFORALL QUERY, VAR, PRED, OBJ, NEWLIT
QUERY[edu:hasQueryLiteral -> edu:NEWLIT] ANDedu:NEWLIT[rdf:type ->
edu:RDFReifiedStatement;rdf:subject -> VAR; rdf:predicate ->
PRED; rdf:object -> OBJ]<- EXISTS LITERAL, ANY (QUERY[rdf:type ->
edu:QEL3Query;edu:hasQueryLiteral -> LITERAL] AND
LITERAL[rdf:type -> edu:RDFReifiedStatement;
rdf:subject -> VAR[rdf:type -> edu:Variable]; rdf:predicate -> dc:ANY] )
AND EXISTS AA[rdf:type ->
edu:AddSimpleRestriction;rdf:predicate -> PRED;rdf:object -> OBJ]@PP
AND unify(NEWLIT, lit(VAR,PRED,OBJ)).
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36Peter Dolog, ERP Course, KMandLearning
A Rule to Generate Recomendation Annotationon ResultsFORALL U, D recommended(U, D) <- user(U) AND
document(D) ANDFORALL Dl (prereq(D, Dl) ->
(FORALL T (topic(Dl, T) -> (EXISTS P
(U[papi:performance->P]@uli:learner -> P[papi:learning_competency->T]@uli:learner)) AND EXISTS D (prereq(D, Dl))))).
Extending the Knowledge Structure of the Resource by at Least Additional Attribute => D[hasAnnotation->recommended].
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37Peter Dolog, ERP Course, KMandLearning
Recommendation in the Search Results
Mapping the value of the hasAnnotationattibute to a visual representation
hasAnnotation -> recommended => GreenBall
hasAnnotation -> not_recommended => RedBall...
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38Peter Dolog, ERP Course, KMandLearning
Example: A Rule for Generating Examples forPresented Learning Material
FORALL R, E example(R,E) <-LearningResource(R) AND example(E) AND EXISTS C1 (R[dc:subject->C1]) AND FORALL C2 (R[dc:subject->C2]
-> E[dc:subject->C2]).
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39Peter Dolog, ERP Course, KMandLearning
The Personal Reader
Similarly to the Example Rule, Summaries, Details, Generalizations, and Excercises are generated
Mapping to Visual Representation as Separate Boxes
www.personal-reader.de
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40Peter Dolog, ERP Course, KMandLearning
Adaptive Hypermedia Technologies
AdaptiveHypermediaTechnologies
AdaptivePresentation
AdaptiveMultimediaPresentation
AdaptiveTextPresentation
Adaptation ofModality
AdaptiveNavigationSupport
NaturalLanguageAdaptation
CannedTextAdaptation
Inserting/RemovingFragments
Altering Fragments
Stretchtext
Sorting Fragments
Dimming Fragments
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41Peter Dolog, ERP Course, KMandLearning
Roles of Concepts in a Resource
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42Peter Dolog, ERP Course, KMandLearning
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43Peter Dolog, ERP Course, KMandLearning
The technical approach
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44Peter Dolog, ERP Course, KMandLearning
Retrieving Resources
Distributed contentDistributed standard based
metadata descriptions about:• Content• Relationships between
the content• Learner
Logic Programs• Query and adapt
content delivery and its links
• Visualize adaptive navigation support
P2P
Content
RelationshipsContent Metadata
Logic ProgramsLearner Model
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45Peter Dolog, ERP Course, KMandLearning
http://www.hcd-online.com
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46Peter Dolog, ERP Course, KMandLearning
Semantic Annotation
Annotator is activated from repository administration interface (for all or only new resources)
Loads (linguistically annotated) ontology/ontologies that can be used to annotate this repository
Loads textual parts (title, description, key words, content if available) of resources and stores them in full-text (Lucene) index
Finds (new) annotations• Linguistic expressions of ontology are used as queries against full-text
index to find (new) annotations• Text classification techniques are used to find additional annotations
(e.g., use combined and pruned term frequency vectors of all resources of one concept as new query)
Sends new annotations back to repository administration interface where user checks correctness
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47Peter Dolog, ERP Course, KMandLearning
Technological Issues
Exact Match – not always work – similarity measures considered already in Personal Reader, more doc. Analysis in HCD-Suite online
Heterogeneity (inconsistencies, contradictions)OrderingOpposite situation – not to many results/links but to fewPerformance
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48Peter Dolog, ERP Course, KMandLearning
Questions?Additional References to those at the course web site:Peter Dolog et. al: Personalization in Distributed e-Learning
Environments. In Proc. WWW2004. New York.
Peter Dolog et. al: The Personal Reader: Personalizing and Enriching Learning Resources using Semantic Web Technologies. In Proc. AH2004. Eindhoven
Peter Dolog and Wolfgang Nejdl: Semantic Web Technologies for the Adaptive Web. In Peter Brusilovsky, Alfred Kobsa, Wolfgang Nejdl (eds.), The Adaptive Web: Methods and Strategies of Web Personalization. To appear.