Decision Support Systems and Expert Systems

MQF Level 7

6 Credits (ECTS)

Decision Support Systems and Expert Systems

Start
October 2024
Module Type
Elective
Price
€975
ECTS Credits
6 Credits (ECTS)
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Get 70% back via Tax Credit

€683 refund on this module

Module Description

This module has two parts. The first part will equip learners with the ability to understand and analysing information and information systems in organisations.

It will also provide students with exposure and understanding of technical and organisational aspects of decision support systems (DSS) and expert systems, their importance, their building blocks, their management, and their applications.

Learners will also learn to analyse an organization’s need for a DSS, to design, to implement and to validate a DSS.

The second part of the module will equip learners with the ability to understanding and designing expert systems (ES) in organizations.

It will provide a theoretical framework, design methodologies, and applications of ES. The learners will learn CLIPS language, which they can use for developing their own ES.

Through case studies, they will learn how they can integrate theory to develop fully functional ES (using CLIPS).

Entry Requirements

Candidates who apply for this course must possess one of the following: 

  • a Level 6 degree related to AI/Computer-Science/Mathematics/Electronics; 

OR 

  • a Level 6 degree not related to AI/Computer Science/Mathematics/Electronics and a minimum of two years’ relevant experience; 

 OR 

  • a Level 5 diploma or higher diploma and five years’ of relevant work experience.   

Target Audience

This course is targeted at: 

  • Industry professionals working in different domains, including Technology, Engineering, Science, IT, Finance, Accountancy, Management, Marketing, Insurance, Banking, Gaming, Healthcare, Medicine, Pharmaceutical, Human Resources, Psychology, Blockchain, Legal, Administration, Policy Making, Digital Art, Archaeology, Architecture, Education and other related areas. 
  • Recent graduates with degrees in Computer Science, Technology, Marketing, Finance, Economics, Accountancy, Management, HR, Law, Engineering, Science, Medicine, Psychology, Digital Art, Game Development, Archaeology, Architecture or Business. 
  • Mid-career-break professionals looking for opportunities to return to or change their career.  

The target group may also be extended to positions such as that of wedding manager, transport manager, maintenance manager, operations manager, marketing manager, conference manager and even that of general manager. 

Module / Unit Instructions

The proposed structure comprises a blended approach promoting the building of a community of practice via peer-to-peer learning.

The structure uses primarily two dimensions of teaching-learning modes:

  1. Face-to-face sessions: 18 hours.
  2. Online Learning Activities: 12 hours.

Face to Face sessions

  • Face-to-face sessions include lectures, tutorials, discussions, presentations and workshop activities promoting peer-to-peer learning.

Online Learning Activities

Online learning activities incorporate tutorials and asynchronous discussions. These may consist of active interaction, participation and contributions in fora discussions, sharing resources and self-reflection exercises.

Learners also contribute to the building of the community of practice by providing feedback to their peers as critical friends, enhancing the learner’s critical engagement throughout the study period.

The tutor provides continual support during both teaching -learning modes by providing information, readings and tasks relevant to the module in question.

The tutor provides continuous formative feedback as an on-going guidance during the student’s learning experience in preparation for their summative assessment.

How you’ll be assessed

Assessment of each module consists of two assignments, each carrying a weighting as below:

a) One Formative assignment carries 20% of total module mark achieved. b) One Summative assignment carries 80% of total module mark achieved.

For successful completion of a study module the student is required to achieve a minimum of 41% pass mark in both the formative and the summative assignment.

The overall grade achieved for each module is calculated as the sum of:  20% of the mark achieved for formative assignment; and  80% of the mark achieved for the summative assignment.

All assignment tasks of both formative and summative assessment aim to provide the learner an opportunity to produce evidence of his/her competences aligned to the learning outcomes of each individual Module.

Assessment

a) Formative assessment tasks are provided in the form of structured online discussions that support learners in their development throughout all of the modules studied.

Such discussions are facilitated and monitored by lecturer who provides students with constructive feedback to help them improve and prepare for summative assignment and dissertation.

Formative assessment tasks will contribute to the student’s final mark to acknowledge their work and give chance to improve.

This method allows students to also contribute to the building of the community of practice by providing feedback to their peers as critical friends, enhancing the learner’s critical engagement throughout the study period

b) Summative assessment is done via one assignment at the end of each module. The mode of assessment varies and may include in-class assignments and home-based written assignments.

For this module,  the recommended assessment tool is using case studies or proposals. Other assessment tools which may be used are presentations or reports.

Word count range: 3000 ± 10% For successful completion of a study module, the student is required to achieve a minimum of 41% pass mark in the summative assignment

 

Assignment
Discussions

Module Intake Dates

October 2024
Price
€975

Additional Info

Reading for the entire Master of Science (M.Sc.) in Artificial Intelligence as presented in this brochure costs €9,775.*  

Upon successful completion of this course, students will be eligible for a 70% refund of the cost through the ‘Get Qualified’ scheme.** 

Due to the modular structure of the course, you may also opt to take individual modules as stand-alone. The entry requirements still apply.***  

*Prices are applicable to students who reside in Malta at the time of applying. 

**Terms and conditions apply.  

*** For the price of individual modules, please contact the IDEA Academy team.  

Learning Outcomes

Competences:

At the end of the module/unit the learner will have acquired the responsibility and autonomy to:

  • Exhaustively evaluate the need for and application of DSS and ES in an organisation;
  • Judiciously examine various types of DSS and their suitability for their organisation in terms of efficiency and effectiveness in an organisation.
  • Rigorously assess various ES designs and the efficiency and effectiveness of an ES in an organization.
  • Identify and asesss the need for data warehouse and data mining techniques for a DSS or ES.

Knowledge:

At the end of the module/unit the learner will have been exposed to the following:

  • Classify Complex Systems in context of complexity of real-world systems or domains.
  • Discuss Evolution of Decision Support Systems (DSS) Knowledge Discovery in a DSS.
  • Examine the theoretical foundation of Expert System.
  • Explore/analyse the concepts of Knowledge Discovery and ES Architectures .

Key indicative topic areas cover:

  • Introduction to Complex Systems and decision making
  • Evolution of Decision Support Systems
  • Knowledge Discovery in a DSS
  • Expert Systems (ES)
  • The theoretical foundation of ES
  • Knowledge Discovery and ES Architectures

Skills:

At the end of the module/unit the learner will have acquired the following skills:

  • Apply various DSS and ES;
  • Judiciously examine organisations requirements of DSS and ES.
  • Design their own ES using CLIPS.
  • Rigorously assess the effectiveness and benefits of DSS and ES.
  • Critically evaluate and apply various DSSes and Eses.

Judgment Skills and Critical Abilities:

The learner will be able to:

  • Master problem- solving skills by applying DSS and ES

Module-Specific Communication Skills:

The learner will be able to:

  • Communicates ideas, problems, and solutions to both specialist and non-specialist audiences using a range of DSS and ES techniques.

Module-Specific Learner Skills:

The learner will be able to:

  • Undertake independent and self-directed study through primary and secondary research.

Module-Specific Digital Skills and Competences:

The learner will be able to:

  • Navigate through the online learning platform to find assignments, discussion boards, literature, tutorials etc.
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