Using Schema Analysis for Feedback in Authoring Tools for Learning Environments

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Using Schema Analysis for Feedback in Authoring Tools for Learning Environments. Harrie Passier* & Johan Jeuring** Faculty of Informatics * Open University of the Netherlands ** Open University of the Netherlands and University of Utrecht. Overview. Introduction Context Feedback
Using Schema Analysis for Feedback in Authoring Tools for Learning Environments Harrie Passier* & Johan Jeuring** Faculty of Informatics * Open University of the Netherlands ** Open University of the Netherlands and University of Utrecht Overview
  • Introduction
  • Context
  • Feedback
  • Lack of feedback
  • Research goal
  • Ontology based feedback
  • Using Schema Analysis for Feedback in Authoring Tools
  • Schemata
  • Schema representations
  • Schemata: abstract interpretations
  • Schema analysis
  • Two examples: completeness and synonyms
  • Questions and discussion
  • Harrie Passier, OUNL Context
  • Faculty of Informatics of the OUNL
  • Research interest: Generating Feedback
  • Feedback to students
  • Design education like modelling (UML – class and object diagrams)
  • Mathematics courses (solving systems of linear equation)
  • Feedback to authors
  • Course development
  • Information from student phase to author phase: optimisation of e-course (sub project of Alfanet project –Audit module)
  • Harrie Passier, OUNL Feedback
  • Definition
  • Comparison of actual performance with some set standard (norm)
  • Assess progress, correct errors and improve performance
  • An essential element needed for effective learning
  • Harrie Passier, OUNL Lack of feedback
  • Student side: there is a frequently lack of (semantically rich) feedback in eLearning systems (Mory, 2003)
  • Author side: eLearning systems are often complex tools. There is a high probability of mistakes. To improve the quality, authoring tools should include mechanisms for checking the authored information (Murray, 1999)
  • Harrie Passier, OUNL Research goal
  • Develop generic, domain and task independent feedback mechanisms that produce semantically rich feedback to learners and authors
  • Three types of feedback
  • To a student during learning
  • To an author during course authoring
  • From a group of learners who study a course to an author
  • Ontologies are arguments of the general feedback engine
  • Reusability, flexibility and adaptability of knowledge structures (Aroyo, 2004)
  • Harrie Passier, OUNL Ontology based feedbackFunctional architecture eLearning system Domain ontology Player Feedback engine Model language ontology Task ontology Author tool Education ontology Feedback ontology Harrie Passier, OUNL Ontologies as norms Examples
  • Author perspective:
  • Domain ontology (communication technology)
  • Course structure(IMS Learning Design – IMS LD)
  • Task ontology (steps to develop a course)
  • Education (inductive and deductive learning)
  • Feedback (preventive and corrective feedback)
  • Student perspective:
  • Domain ontology (communication technology)
  • Model language ontology (UML)
  • ..
  • Harrie Passier, OUNL Using Schema Analysis for Feedback in Authoring Tools Scope:
  • Authoring
  • Structural aspects
  • Course structure
  • Domain structure
  • Harrie Passier, OUNL Schema
  • An ontology specifies the objects in a domain of interest together with their characteristics in terms of attributes, roles and relations. Many aspects can be represented, such as categories (taxonomic hierarchy), time, events and composition.
  • A schema is a certain type of ontology. It describes the structure of a composite object. A composite object contains objects related to other objects using ‘has_part’ or ‘uses’ relations.
  • Harrie Passier, OUNL wheel has_part spoke rim Schema representations Two schemata:
  • Domain schema: RDF <resource, property, value>
  • Course structure: IMS LD (= Document Type Defintion -DTD)
  • Addition of specific annotations to content and structure:
  • New elements: Definition and Example
  • New attribute: Educational-strategy (Inductive | Deductive)
  • In practice many elements can be added
  • Harrie Passier, OUNL Example IMS LD definition <!ELEMENT Activity %Activity-model; > <!ATTLIST Activity … Educational-strategy (Inductive | Deductive) > <!ENTITY %Activity-model "(Metadata?, …, Activity-description)" > <!ELEMENT Activity-description (Introduction?, What, How?, …, Feedback-description?) > <!ELEMENT What %Extra-p; > <!ENTITY %Extra-p "(…| Figure | Audio | Emphasis | List | … | Example | Definition)*" > <!ELEMENT Definition (Description, Concept, RelatedConcept+) > <!ATTLIST Definition Id ID #REQUIRED Name CDATA #REQUIRED> <!ELEMENT Example (Description, Concept, RelatedConcept+) > <!ATTLIST Example Id ID #REQUIRED Name CDATA #REQUIRED Belongs-to-definition IDREFS #REQUIRED> Harrie Passier, OUNL Schemata: abstract interpretations Possible properties of a course:
  • Completeness: Are all concepts that are used in the course defined somewhere?
  • Correctness: Does the definition of a concept used in the course correspond to the definition of the concept in the ontology?
  • Timely: Are all concepts used in a course defined on time?
  • Use of educational strategy attribute (inductive, deductive)
  • Recursive concepts: Are there concepts defined in terms of it self?
  • Synonyms: Are there concepts with different names but exactly the same definition?
  • Homonyms: Are there concepts with multiple, different definitions?
  • Harrie Passier, OUNL Schema analysis
  • The analyses take schemata as input
  • We perform two types of analyses
  • The analysis of structural properties of one schema, for example the recursive property
  • The comparison of a schema with one or more other schemata, for example to test on correctness
  • Harrie Passier, OUNL a c b e d Some definitions (I) Suppose o = Ont [(a, [b,c]), (b, []), (c, [d,e]), (d, []), (e, [])] :: Ontology, where the letters represent concepts Harrie Passier, OUNL a c b e d Some definitions II Then
  • terminalConcepts = [(b, []), (d, []), (e, [])]
  • nonTerminalConcepts= [(a, [b,c]), (c, [d,e])]
  • allConcepts= [(a, [b,c]), (b, []), (c, [d,e]), (d, []), (e, [])]
  • reachable nonTerminalConcepts allConcepts = [(a, [b,c,d,e]), (b, []), (c, [d, e]), (d, []), (e, [])]
  • reachableTerminals nonTerminalConcepts nonTerminalConcepts = [(a, [b,d,e]), (c, [d,e])] NB. Functions based on fixpoint calculations (grammar analyses)
  • Harrie Passier, OUNL Example I: Completeness
  • Definition: are all concepts used in the course defined somewhere?
  • Within a course
  • Within an domain ontology
  • Between a course and an domain ontology
  • Steps (within a course)
  • Determine the set of used concept id’s
  • in the right- and left hand sides of concepts within examples
  • in the right hand side of concepts within definitions
  • Determine the set of defined concept id’s
  • in the left-hand side of concepts in definitions
  • Check that each of the used concepts appears in the set of defined concepts
  • Harrie Passier, OUNL Example II: Synonyms
  • Concepts with different names may have exactly the same definition
  • Within an ontology
  • Example
  • Concept a (a, [c,d]) and concept b (b, [c,d]), are synonyms
  • Formal definition: Given a set of productions, two concepts x and y are synomyms if their identifiers are different, IdxIdy, and (reachableTerminals productions x) equals (reachableTerminals productions y)
  • Steps
  • Determine for all concepts in the ontology all reachable terminal concepts
  • Collect the concepts with the same reachable terminal concepts and different concept id’s
  • Harrie Passier, OUNL Questions?Thank you!Contact me:
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