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AGENT-DYSL: A Novel IntelligentReading System for Dyslexic Learners
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AGENT-DYSL: A Novel Intelligent Reading System for Dyslexic Learners

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Presentation at the eChallenges 2007 conference at The Hague, Oct 25, 2007

Presentation at the eChallenges 2007 conference at The Hague, Oct 25, 2007

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  • 1. AGENT-DYSL: A Novel Intelligent Reading System for Dyslexic Learners Andreas Schmidt FZI Research Center for Information Technologies Germany [email_address]
  • 2. Motivation, problem area
    • Dyslexia is the most common form of reading disorder
      • estimations are at ~10%
    • Reading difficulties often lead to low achievements in education overall
      • 74% of poor readers at 3rd grade are still poor readers at 9th grade
      • impact on vocabulary development and knowledge construction
      • negative effect on self-esteem and motivation
    • Dyslexia, however, does not mean that affected students cannot learn reading well at all
      • they only need more time and more assistance
  • 3. Research Objectives
    • Key objective of the EU FP6 project AGENT-DYSL is an intelligent adaptive reading system
      • complementing teacher activities
      • allowing for an accomodative educational setting, i.e., students remain in „ordinary“ classes
    • Key partners & competencies
      • AtosOrigin, Spain: administrative coordination and integration
      • ICCS, Greece: technical coordination, recognition of facial expressions
      • Dyslexia Action, UK; Danish Education Ministry; FOME, Spain experts on dyslexia
      • FZI, Germany: ontology-based representation of adaptation knowledge
      • ULBS, Romania: user profiling
      • ILSP, Greece: speech recognition
      • Eurodynamics, Greece
  • 4. General Approach
    • Monitor the performance of a dyslexic user and his current emotional and physical state
      • using speech recognition to detect errors
      • using image recognition to detect emotional state/attention
    • Automated construction of a user-profile
    • Predict possible future errors of the learner based on this profile
    • Provide personalized assistance to the learner, according to his profile.
  • 5. Technical Approach
    • The characteristics of the learner and their current situation in a semantically rich user model
      • speech processing: detect anomalies in the reading performance like hesitance, but also reading errors.
      • image processing: capture attention patterns and emotional state
      • input from teachers who encode their knowledge of the individual learner (e.g., type of errors)
    • Descriptive encoding of expert pedagogical knowledge for adaptation
    • Derivation of adaptation actions applied to the user interface like
      • predictive highlighting of difficult words in various forms
      • adaptation of the reading speed
      • presentation of additional help and pronunciation clues
  • 6. AGENT-DYSL Architecture
  • 7. AGENT-DYSL Ontology
  • 8. Key Innovation
    • a deep knowledge about the user and the current context or situation
    • context-aware pedagogical knowledge about how to respond to a particular situation
    • appropriate adaptation possibilities in the reading software user interface
    • teachers and experts gaining new insights into the effectiveness of certain pedagogical strategies.
  • 9. Vision: Closed Loop Approach
  • 10. Conclusion and Outlook
    • AGENT-DYSL started in September 2006
    • Requirements Analysis and first adaptation strategies
    • System architecture and concept finalized
    • First GUI-Prototype ready
    • Second prototype next year will be evaluated in Greece and Denmark
    • More information at http://www.agent-dysl.eu
    Andreas Schmidt Department Manager FZI Research Center for Information Technologies, Karlsruhe, GERMANY andreas.schmidt@fzi.de, http://andreas.schmidt.name