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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 Faculty of Informatics University of Ulster Intelligent Multimedia System for Teaching Databases (IMSTD) Literature Review Objectives of research + Proposed work Comparison with previous work + Contribution to the knowledge Conclusion Difficulty in Databases subject Subject Very difficult Difficult Introduction to Databases - 7.7 76.9 7.7 Entity-Relationship Modelling - 48.7 48.7 2.6 12.8 71.8 12.8 2.6 - 71.8 25.6 2.6 2.6 41.0 48.7 5.0 Normalization The Relational Model SQL Easy Very easy Table 1: Percentage of the difficulty of the Databases subject 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 Literature Review in ITS in Databases System DB_Tutor (Raguphati and Schkade, 1992) Objective Assist users in database design Technique Using hypertext (in the form of nodes and links) to present the information on databases SQL-Tutor (Mitrovic, 1998; Mitrovic and Ohlsson, 1999) COLER (ConstantinoGonzalez and Suthers, 2000) ITS in Database Design (Canavan, 1996) Supports student learning SQL Based on Constraint-Based Modelling (CBM) Coach students in entityrelationship modelling in a collaborative learning environment To assists students in learning Normalization Based on an architecture for intelligent collaborative learning (Belvedere) Menu-based Literature review in systems that apply NLP in Databases System Aim Dialogue Tool (RADD) (Buchholz et al., 1995) To obtain a skeleton design of EER model from designer DMG (Tjoa and Berger, 1993) ANNAPURA (Eick and Lockemann, 1985) To support designer in extracting knowledge from requirements specification To provide a computerized environment for semi-automatic database design Type of user Database Designer Techniques used Dialogue Syntactic analysis – ID/LP format Semantic analysis – using Jackendoff’s hypothesis Heuristics Attribute Grammar Pragmatic interpretation Rules Heuristics Dialogue S-Diagrams Heuristics Database Designer Database Designer Experts of UoD Architecture of IMSTD Agent User Interface Transformation Tool Domain Model Tutor Model Knowledge Expertise Teaching goals Tutoring strategies Student Model Student overlay knowledge Student misconceptions Prospective Tools Macromedia Authorware Brill’s tagger Microsoft Agent Proposed research work Step 1 : Read natural language input text into IMSTD Step 2: Part of speech tagging using Brill’s tagger Proposed research work Words tagged A department may have several locations . Result DT NN MD VB JJ NNS . Meaning Determiner Noun, singular mass Modal Verb, base form Adjective Noun, plural . Table 2: Result from Brill’s tagger Proposed research work Step 3: Classifying and removing redundancies and plurals Sentence First Second Third Noun company departments department name number employee department department locations Verb is organized has manages Adjective have Several unique particular Table 3: Classification of words according to the selected category Proposed research work Step 4: Apply heuristics Step 5: Refer to history Word Entity Attribute Relationship Cardinality Name 1 10 0 0 Employee 5 0 0 0 Colour 1 5 0 0 Has 0 0 12 0 12 1 4 0 Book Table 4: An example of the history file Proposed research work Step 6: Produce preliminary model Figure 1: A preliminary model of the scenario Proposed research work Step 7: Human intervention Step 8: Produce final model Step 9: Incorporate into ITS Comparison with other ITS in Databases System Objective Technique DB_Tutor (Raguphati and Schkade, 1992) Assist users in database design SQL-Tutor (Mitrovic, 1998; Mitrovic and Ohlsson, 1999) Supports student learning SQL Using hypertext (in the form of nodes and links) to present the information on databases Based on ConstraintBased Modelling (CBM) COLER (ConstantinoGonzalez and Suthers, 2000) Coach students in entityrelationship modelling in a collaborative learning environment ITS in Database Design (Canavan, 1996) IMSTD To assists students in learning Normalization To assists students in learning Data Modelling Based on an architecture for intelligent collaborative learning (Belvedere) Menu-based Agent-based Presence of basic ITS Modules Domain Tutoring Student module module module Yes Yes No Other module NLP Presence of agent No No No Implemented in the student module where the knowledge is represented in the form of constraints Implemented under the submodule Differences Recognizer Yes Yes CBM No No No Implemented under the submodule Participation monitor Coach module No Yes, limited feature Yes Yes No No No No Yes Yes Yes Yes Yes Yes but Comparison with other systems that apply NLP in Databases System Dialogue Tool (RADD) (Buchholz et al., 1995) Aim To obtain a skeleton design of EER model from designer Database Designer Yes Type of user Expert Educators Student No No No Techniques used User Involvement Language Yes German Yes German Yes No No No Dialogue Syntactic analysis – ID/LP format Semantic analysis – using Jackendoff’s hypothesis Heuristics Attribute Grammar Pragmatic interpretation Rules Heuristics Dialogue Yes Yes No No S-Diagrams Heuristics Yes English No No Yes Yes Brill’s tagger Heuristics History file Yes English DMG (Tjoa and Berger, 1993) ANNAPURA (Eick and Lockemann, 1985) Transformation tool (IMSTD) To support designer in extracting knowledge from requirements specification To provide a computerized environment for semi-automatic database design To aid students an educators in deriving an ER Model from natural language text Contribution to the knowledge A new technique to transform a natural language database specification into an ER model The formation of new heuristics 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