Folksonomy-based Course Authoring for Flexible Student Modeling Sergey Sosnovsky, Michael Yudelson

Outline Definition: Folksonomy Project Motivation KnowledgeTree: –Interface: SEDONA –Model Storage and (de)Composition CUMULATE –Flexible Student Modeling

: –“A folksonomy is an Internet-based information retrieval methodology consisting of collaboratively generated, open-ended labels that categorize content such as Web pages, online photographs, and Web links”. »Thomas Vander Wal The main difference from formal knowledge models like ontology: –Subjective view (community-based) –Uncontrolled vocabulary –Poor structure Folksonomy

Motivation: QuizGuide – successful example of topic-based navigation QuizGuide – provides adaptive navigation support based on fuzzy knowledge units – topics. Topic - ……

Motivation: Customizable topic-based course structures Topics provide: –Good enough source for adaptation –Natural way to organize learning material –Easy way to index learning material However a topic-based model of the course as all folksonomic structures is subjective Main idea: To provide a friendly authoring interface for teachers for customization of their own topic-based course structures To scaffold topic reuse and topic-based modeling of student knowledge as well to enable inter-folksonomy knowledge transition we store topic models on the ontology server as rdf-documents

KnowledgeTree: Course Authoring

KnowledgeTree: Topic Authoring

SEDONA: Model Storage and (de)Composition Stores three kinds of rdf-models: –Domain model: Topics and relations between them (if any) –LO repository (QuizPACK, WebEx) models: Metadata of LO’s –Basic Course structure Course metadata Topic-LO relations On-demand: –retrieves models, combines them and reports the enhanced model to the KnowledgeTree –receives enhanced model, decomposes, stores changes (in the domain model and/or course structure) –reports to CUMULATE topic-LO associations

Domain Model

LO Repository Model

Course Structure

CUMULATE: Flexible Student Modeling Retrieving topics and their associations with LOs from SEDONA and cashing it Student knowledge inference in new topics based on student activity with LO Reporting of student knowledge to adaptive services

Conclusion Framework for: –Easy authoring of topic-based course structures –Automatic modeling of student knowledge in newly created topics Using of ontology server scaffolds topic reuse and allows inter-model knowledge inference.

Thank you for you questions

Topics Provide useful way of learning material aggregation Play two roles for adaptation: –As domain elements used for recourse indexing and students’ knowledge assessment –As interface elements used for resource structuring and navigation Coarse-grained (not precise UM) Subjective