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Concept-based Learning Spaces

Concept-based Learning Spaces. Apply domain-specific KOS principles for organizing collections/services for given applications Terence R. Smith, Marcia L. Zeng, and Alexandria Digital Library (ADL) Project Team University of California, Santa Barbara. Outline. 1. Viewing an example

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Concept-based Learning Spaces

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  1. Concept-based Learning Spaces Apply domain-specific KOS principles for organizing collections/services for given applications Terence R. Smith, Marcia L. Zeng, and Alexandria Digital Library (ADL) Project Team University of California, Santa Barbara

  2. Outline • 1. Viewing an example • 2.Explaining the concept model • 3. Discussing: why a strongly-structured model

  3. Science learning spaces: Concept KOS • Concepts of scienceas basic knowledge granules • Sets of concepts form bases for scientific representation • DL and KOS technology can support organization of science learning materials in terms of concepts • Collections of models of science concepts (knowledge base) • Collections of learning objects (LO) cataloged with concepts • Collections of instructional materials organized by concepts • Organize learning materials as “trajectory through concept space” • Lecture, lab, self-paced materials • Services for creating/editing/displaying such materials

  4. Application to learning environments • Application • Introductory physical geography (F2002, S2003) • Collections created • Knowledge base (KB) of strongly structured concepts • Structured lectures and labs • Learning objects cataloged by ADN metadata (+ concepts) • Services created • For concepts • Web-based concept input tool • Graphic and text-based display tools • For instructional materials • Web-based “lecture composer” • “Conceptualization” graphing tool • For learning objects • Metadata input tool

  5. Learning environment display (lecture mode) • The lecture is presented on three projection screens, showing the • Concept window (left) • Lecture window (center) • Object window (right)

  6. The left-hand frame displays the structure of the lecture The right-hand frame displays the content of the lecture ADL icons (globe image) attached to a concept link to a display of concept properties in the concept window Current instructional material window Other icons attached to a concept link to a display of concept examples in the illustration window

  7. View of learning material by concepts

  8. Learning environment display (lecture mode) • The lecture is presented on three projection screens, showing the • Concept window (left) • Lecture window (center) • Object window (right)

  9. The left-hand frame displays the structure of the lecture The right-hand frame displays the content of the lecture ADL icons (globe image) attached to a concept link to a display of concept properties in the concept window Current instructional material window Other icons attached to a concept link to a display of concept examples in the illustration window

  10. Item in concept knowledge base

  11. Outline • 1. Viewing an example • 2.Explaining the concept model • 3. Discussing: why a strongly-structured model

  12. Model of science concepts • Representing a concept involves more than terms • Objective, information-rich, scientific representations • e.g., for concepts of heat diffusion, DNA, drainage basin, … • Associated semantics • e.g., relating to measurement, recognition,… • Many interrelationships • e.g., hierarchical, causative, property,… • Models of science concepts • Already exist for chemistry (ASA), materials (NIST),… • Generalize such models for this application • Structure items in concept KB using model • Original design • Current structure as seen from the lecture

  13. c o n c e p t u a l m o d e l - f r a m e w o r k ID Preferred Terms Nonpreferred Descriptions TypeOfConcept ClassOfPhenomena FieldsOfStudy ConceptModel KnowledgeDomain Topics HistoricalOrigins Examples Relationships

  14. c l a s s i f i c a t i o n o f c o n c e p t s type of concept observations measures syntactic (linguistic) logical analysis abstract mathematical examples identification/characterization topics concepts representation methodological models understanding application questions/answers measurable communication problems/solutions recognizable interpreted abstract hypotheses/evaluations predictions/tests concrete statements/deviations applications/evaluations facts/validations

  15. c l a s s i f i c a t i o n o f p h e n o m e n a class of phenomena object material process form event state … …

  16. c o n c e p t u a l m o d e l – r e l a t i o n s h i p s Relationships CotainedIn SetMembership Contains Hierarchical IsPartOf Partitive HasParts ScientificUse Applications ExplicitFull ExplicitPartial HasRepresentation Representation ImplicitFull PartiallyRepresents ImplicitPartial AbstractSyntactic Defining Operation Methodological HasProperty Property PropertyOf CoRelated CausedBy Causal Causes other

  17. Current Model of science concepts • ID • TYPE and FACET • CONTEXT (KNOWLEDGE DOMAIN) • TERM(S) (P/NP) • DESCRIPTION(S) • HISTORICAL ORIGIN(S) • EXAMPLE(S) • HIERARCHICAL RELATIONS • DEFINING OPERATIONS • SCIENTIFIC REPRESENTATION(S) • Scientific classifications • Data/Graphical/Mathematical/Computational reps • PROPERTIES • CAUSAL RELATIONS • CO-RELATIONS • APPLICATION(S) As displayed in the lecture mode

  18. Apply KOS principles to domain-specific applications

  19. Outline • 1. Viewing an example • 2. Explaining the concept model • 3. Discussing: why a strongly-structured model

  20. They typically take the form of structured sets of terms representing concepts and their interrelationships. Graphical representations of concepts and interrelationships derived from such KOS typically take the simple form of a set of named nodes connected by named links. Types of structured models • Term lists • Classification and categorization schemes • Relationship groups • Metadata content standards • General knowledge representation languages KOS

  21. Values of these models • They support, for example, access to traditional knowledge containers, such as texts and journals, in which term-based representations of concepts occur. • They are also of value in supporting high-level graphical views (or “concept maps”) of the interrelationships among concepts.

  22. Limits of these models • They could not provide deep organization of, and access to, scientific knowledge that is important for learning. • Accessing knowledge is largely restricted to the traditional information containers. • They cannot easily support access to, or integration of, knowledge concerning many of the attributes of concepts that make them useful in SME modeling activities.

  23. A Taxonomy of KOS Ontologies Semantic networksThesauri Relationship Groups: Strongly-structured Classification schemesTaxonomiesCategorization schemes Classification &Categorization: Subject Headings Authority FilesGlossaries/DictionariesGazetteers Weakly-structured Term Lists: Natural language Controlled language

  24. Toward Strongly-Structured Models • These models focus on such attributes as the • objective representations, • operational semantics, • use, and • interrelationships of concepts, • all of which play important roles in constructing representations of phenomena that further understanding of MSE domains of knowledge.

  25. Toward Strongly-Structured Models • Taxonomy + metadata (or attribute-value pairs) • Ontology for knowledge based systems • Taxonomy and thesaurus + domain-specific markup languages • Specialized models for learning scientific concepts

  26. EDUCATIONAL APPLICATIONS GEOREFERENCED DIGITAL LIBRARIES • knowledgebase and lecture composing, visualization, and presentation tools • physical geography concept space and learning object collections • applications to undergraduate education • educational evaluation • learning services and DL integration • digital classrooms • metadata content standards • learning objects • computational models • distributed georeferenced DL services • NSDL core infrastructure • data environment (e.g., GIS) integration • hardware acceleration for spatial data • collaborative tools • Z39.50 support • ingest and workflow systems KNOWLEDGE ORGANIZATION • gazetteers: research and community • gazetteer content standard • web service protocols for gazetteers, thesauri, and other KOS • ADL gazetteer • thesauri for feature and object types • duplicate detection for gazetteers • textual-geospatial integration services USER INTERFACES • reusable user interface components • contextual maps, footprint creation • KOS navigation • lightweight GIS functionality • Digital Earth visualization • image processing • query-by-content, classification • spatial extent determination OPERATIONAL APPLICATIONS • georeferenced DL tutorials • distributable software packages • operational libraries: UCSB library, ... • outreach; federated nodes ADLP Activities

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