IMSTD:Intelligent Multimedia IMSTD:Intelligent Multimedia System for teaching DatabasesSystem for teaching Databases

By : NAZLIA OMAR

Supervisors: Prof. Paul Mc Kevitt Dr. Paul Hanna

School of Computing and Mathematical SciencesFaculty of InformaticsUniversity of Ulster

Intelligent Multimedia System for Intelligent Multimedia System for Teaching Databases (IMSTD)Teaching Databases (IMSTD)

Literature ReviewObjectives of research + Proposed

work Comparison with previous work +

Contribution to the knowledgeConclusion

Difficulty in Databases subjectDifficulty in Databases subjectSubject Very

difficultDifficult Easy Very

easy

Introduction to Databases - 7.7 76.9 7.7

Entity-Relationship Modelling

- 48.7 48.7 2.6

Normalization 12.8 71.8 12.8 2.6

The Relational Model - 71.8 25.6 2.6

SQL 2.6 41.0 48.7 5.0

Table 1: Percentage of the difficulty of the Databases subject

Objectives of researchObjectives 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 Literature Review in ITS in DatabasesDatabases

System Objective TechniqueDB_Tutor(Raguphati andSchkade, 1992)

Assist users in databasedesign

Using hypertext (in the form of nodesand links) to present the information ondatabases

SQL-Tutor(Mitrovic, 1998;Mitrovic andOhlsson, 1999)

Supports student learningSQL

Based on Constraint-Based Modelling(CBM)

COLER(Constantino-Gonzalez andSuthers, 2000)

Coach students in entity-relationship modelling ina collaborative learningenvironment

Based on an architecture for intelligentcollaborative learning (Belvedere)

ITS in DatabaseDesign (Canavan,1996)

To assists students inlearning Normalization

Menu-based

Literature review in systems that Literature review in systems that apply NLP in Databasesapply NLP in Databases

System Aim Type of user Techniques used

Dialogue Tool(RADD)(Buchholz etal., 1995)

To obtain askeleton designof EER modelfrom designer

DatabaseDesigner

Dialogue Syntactic analysis –

ID/LP format Semantic analysis –

using Jackendoff’shypothesis

Heuristics Attribute Grammar Pragmatic interpretation

DMG (Tjoaand Berger,1993)

To supportdesigner inextractingknowledge fromrequirementsspecification

DatabaseDesigner

Rules Heuristics Dialogue

ANNAPURA(Eick andLockemann,1985)

To provide acomputerizedenvironment forsemi-automaticdatabase design

DatabaseDesignerExperts ofUoD

S-Diagrams Heuristics

Architecture of IMSTDArchitecture of IMSTD

User Interface

Domain Model

Knowledge Expertise

Tutor Model

Teaching goals Tutoring strategies

Student Model

Student overlay knowledge Student misconceptions

Agent

Transformation Tool

Prospective ToolsProspective Tools

Macromedia AuthorwareBrill’s taggerMicrosoft Agent

Proposed research workProposed research work

Step 1 : Read natural language input text into IMSTD

Step 2: Part of speech tagging using Brill’s tagger

Proposed research workProposed research workWords tagged Result MeaningA DT Determinerdepartment NN Noun, singular massmay MD Modalhave VB Verb, base formseveral JJ Adjectivelocations NNS Noun, plural. . .

Table 2: Result from Brill’s tagger

Proposed research workProposed research work

Step 3: Classifying and removing redundancies and plurals

Sentence Noun Verb Adjectivecompany isFirstdepartments organizeddepartment has uniquename manages particularnumberemployee

Second

departmentdepartment have SeverallocationsThird

Table 3: Classification of words according to the selected category

Proposed research workProposed research work

Step 4: Apply heuristicsStep 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

Book 12 1 4 0

Table 4: An example of the history file

Proposed research workProposed research work

Step 6: Produce preliminary model

Figure 1: A preliminary model of the scenario

Proposed research workProposed research work

Step 7: Human interventionStep 8: Produce final modelStep 9: Incorporate into ITS

Comparison with other ITS in Comparison with other ITS in DatabasesDatabases

Presence of basic ITS ModulesSystem Objective Technique Domain

moduleTutoringmodule

Studentmodule

Othermodule

NLP Presence ofagent

DB_Tutor(Raguphati andSchkade, 1992)

Assist users in databasedesign

Using hypertext(in the form ofnodes and links)to present theinformation ondatabases

Yes Yes No No No No

SQL-Tutor(Mitrovic,1998; Mitrovicand Ohlsson,1999)

Supports student learningSQL

Based onConstraint-Based Modelling(CBM)

Implemented inthe studentmodule wherethe knowledgeis represented inthe form ofconstraints

Yes Yes CBM No No

COLER(Constantino-Gonzalez andSuthers, 2000)

Coach students in entity-relationship modelling in acollaborative learningenvironment

Based on anarchitecture forintelligentcollaborativelearning(Belvedere)

Implementedunder the sub-moduleDifferencesRecognizer

No Implementedunder the sub-moduleParticipationmonitor

Coachmodule

No Yes, butlimitedfeature

ITS in DatabaseDesign(Canavan,1996)

To assists students inlearning Normalization

Menu-based Yes Yes No No No No

IMSTD To assists students inlearning Data Modelling

Agent-based Yes Yes Yes Yes Yes Yes

Comparison with other systems that Comparison with other systems that apply NLP in Databasesapply NLP in Databases

Type of userSystem AimDatabaseDesigner

Expert Educators StudentTechniques used User

InvolvementLanguage

Dialogue Tool(RADD)(Buchholz et al.,1995)

To obtain a skeletondesign of EERmodel fromdesigner

Yes No No No Dialogue Syntactic analysis

– ID/LP format Semantic analysis

– usingJackendoff’shypothesis

Heuristics Attribute

Grammar Pragmatic

interpretation

Yes German

DMG (Tjoa andBerger, 1993)

To support designerin extractingknowledge fromrequirementsspecification

Yes No No No Rules Heuristics Dialogue

Yes German

ANNAPURA(Eick andLockemann,1985)

To provide acomputerizedenvironment forsemi-automaticdatabase design

Yes Yes No No S-Diagrams Heuristics

Yes English

Transformationtool (IMSTD)

To aid students aneducators inderiving an ERModel from naturallanguage text

No No Yes Yes Brill’s tagger Heuristics History file

Yes English

Contribution to the knowledgeContribution to the knowledge

A new technique to transform a natural language database specification into an ER model

The formation of new heuristics

ConclusionConclusion

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