IMSTD:Intelligent Multimedia System for teaching Databases - PowerPoint PPT Presentation

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IMSTD:Intelligent Multimedia System for teaching Databases

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IMSTD:Intelligent Multimedia System for teaching Databases By : NAZLIA OMAR Supervisors: Prof. Paul Mc Kevitt Dr. Paul Hanna School of Computing and Mathematical Sciences – PowerPoint PPT presentation

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Title: IMSTD:Intelligent Multimedia System for teaching Databases


1
IMSTDIntelligent Multimedia System for teaching
Databases
  • By NAZLIA OMAR
  • Supervisors Prof. Paul Mc Kevitt
  • Dr. Paul Hanna
  • School of Computing and Mathematical Sciences
  • Faculty of Informatics
  • University of Ulster

2
Intelligent Multimedia System for Teaching
Databases (IMSTD)
  • Literature Review
  • Objectives of research Proposed work
  • Comparison with previous work Contribution to
    the knowledge
  • Conclusion

3
Difficulty in Databases subject
Table 1 Percentage of the difficulty of the
Databases subject
4
Objectives of research
  • To design and implement a transformation tool
  • To design and implement the components of an ITS
  • To create a rich, face-to-face learning
    interaction through the use of a pedagogical
    agent
  • To integrate all of the above components to form
    IMSTD
  • To evaluate students and educators attitudes
    towards this ITS

5
Literature Review in ITS in Databases
6
Literature review in systems that apply NLP in
Databases
7
Architecture of IMSTD
8
Prospective Tools
  • Macromedia Authorware
  • Brills tagger
  • Microsoft Agent

9
Proposed research work
  • Step 1 Read natural language input text into
    IMSTD
  • Step 2 Part of speech tagging using Brills
    tagger

10
Proposed research work
11
Proposed research work
  • Step 3 Classifying and removing redundancies and
    plurals

12
Proposed research work
  • Step 4 Apply heuristics
  • Step 5 Refer to history

13
Proposed research work
  • Step 6 Produce preliminary model

14
Proposed research work
  • Step 7 Human intervention
  • Step 8 Produce final model
  • Step 9 Incorporate into ITS

15
Comparison with other ITS in Databases
16
Comparison with other systems that apply NLP in
Databases
17
Contribution to the knowledge
  • A new technique to transform a natural language
    database specification into an ER model
  • The formation of new heuristics

18
Conclusion
  • Questionnaire results support the evidence that
    Data Modelling is difficult
  • Proposed project will contribute to knowledge
  • Worked examples show that the project is
    achievable within the time period
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