Folksonomy-based Course Authoring for Flexible Student

Modeling

Sergey Sosnovsky,Michael Yudelson

PAWS@SIS@PITT

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

http://www.sis.pitt.edu/~paws/