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Session: 2022/23
Last modified: 22/07/2022 10:48:51
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Title of Module: Decision Support Systems |
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Code: COMP10062 |
SCQF Level: 10
(Scottish Credit and Qualifications Framework) |
Credit Points: 20 |
ECTS: 10
(European Credit Transfer Scheme) |
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| School: | School of Computing, Engineering and Physical Sciences |
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| Module Co-ordinator: | Keshav
Dahal |
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| Summary of Module |
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This module introduces a collection of computer technologies that support decision making process. Making decisions may require considerable amounts of relevant data, information, and knowledge. The module will focus on how all stages of the decision-making process can be supported by conventional and intelligent decision support systems for improving the overall quality of decisions. The students will learn how to apply different decision support technologies for solving various practical real-life decision problems and how to develop simple decision-support systems. It has three major components: First, the types of decision to be made based on working environments, people and styles of decision making. It addresses if it is possible to construct a generalised DSS given the diversity of environments and examines ways in which the organisation may change as a consequence of applying this technology. The second component focuses on Decision Theory and reviews the generalised theories which have been developed for supporting decisions. The final component merges these two to demonstrate that DSS can indeed be of use and have real potential. The module will develop a range of graduate attributes. Knowledge in the principles behind the decision techniques will develop skills to critically evaluate decision theory and the generalised methods which have been developed for supporting decisions and gain a systematic understanding of the characteristics, feasibility and the supporting mathematics of decision support systems. This module provides a way to train students in carrying out hands-on tasks, while developing their creative thinking and preparing them for future employment.
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| Module Delivery Method |
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| Face-To-Face | Blended | Fully Online | HybridC | HybridO | Work-based Learning |
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Face-To-Face
Term used to describe the traditional classroom environment where the students and the lecturer meet synchronously in the same room for the whole provision.
Blended
A mode of delivery of a module or a programme that involves online and face-to-face delivery of learning, teaching and assessment activities, student support and feedback. A programme may be considered “blended” if it includes a combination of face-to-face, online and blended modules. If an online programme has any compulsory face-to-face and campus elements it must be described as blended with clearly articulated delivery information to manage student expectations
Fully Online
Instruction that is solely delivered by web-based or internet-based technologies. This term is used to describe the previously used terms distance learning and e learning.
HybridC
Online with mandatory face-to-face learning on Campus
HybridO
Online with optional face-to-face learning on Campus
Work-based Learning
Learning activities where the main location for the learning experience is in the workplace.
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| Term(s) for Module Delivery |
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(Provided viable student numbers permit).
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| Term 1 | | Term 2 | | Term 3 | |
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| Learning Outcomes: (maximum of 5 statements) |
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On successful completion of this module the student will be able to:
L1.
L1 analyse the need for, and effectiveness of, computerised methods for supporting decision making in business, and apply mathematics of decision support systems.
L2.
L2 evaluate and apply information and Information Technologies both systematically and creatively in solving decision making problems.
L3.
L3 demonstrate analytical and decision making skills in complex and unpredictable situations. |
| Employability Skills and Personal Development Planning (PDP) Skills |
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| SCQF Headings |
During completion of this module, there will be an opportunity to achieve
core skills in:
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| Knowledge and Understanding (K and U) |
SCQF Level 10.
Knowledge & understanding of working principle of decision theory and the generalised methods which have been developed for supporting decisions. |
| Practice: Applied Knowledge and Understanding |
SCQF Level 10.
Knowledge of applying practical skills in designing and building computerised methods for supporting decision making in business. |
| Generic Cognitive skills |
SCQF Level 10.
Students will learn how to bring together information from various sources so as to complete their laboratory and tutorial tasks. |
| Communication, ICT and Numeracy Skills |
SCQF Level 10.
Compiling individual report students will develop communication skills as well as the ability to write technical report. Students will gain a systematic understanding of supporting mathematics of decision support systems. |
| Autonomy, Accountability and Working with others |
SCQF Level 10.
Students will be encouraged to work with others in tutorials and lab sessions for finding information and solving problems on the assigned task. In doing so, students will develop a sense of accountability to the other members. |
| Pre-requisites: |
Before undertaking this module the student should have
undertaken the following:
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Module Code:
| Module Title:
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| Other: | N/A |
| Co-requisites | Module Code:
| Module Title:
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* Indicates that module descriptor is not published.
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| Learning and Teaching |
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| The module will be delivered by means of lectures, tutorials and practical lab work aimed at developing the knowledge and skills on decision support systems. The lectures will cover core concepts and principles of decision making, decision theory and decision support systems with real-life practical exercises. The follow-on lab work will enable students to put the theory into practice. The tutorial exercises will help consolidate both the lecture material and the skills practiced during the lab work. A real-world business case study is provided for the decision theory application as part of coursework. This involves critically evaluating and applying the suitable decision making techniques to the real-world application. Oral feedback will be given during the tutorials and labs. Practical understanding and skills and learning outcomes will be tested through the coursework and formal examination. |
Learning Activities
During completion of this module, the learning activities undertaken to
achieve the module learning outcomes are stated below:
| Student Learning Hours
(Normally totalling 200 hours):
(Note: Learning hours include both contact hours and hours spent on other learning activities) |
| Lecture/Core Content Delivery | 24 |
| Laboratory/Practical Demonstration/Workshop | 24 |
| Independent Study | 152 |
| 200
Hours Total
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**Indicative Resources: (eg. Core text, journals, internet
access)
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The following materials form essential underpinning for the module content
and ultimately for the learning outcomes:
Module Resources on Aula/Moodle
Microsoft Excel Software
The World Wide Web
Applied Management Science: Modeling, Spreadsheet Analysis, and Communication for Decision Making, 2nd Edition, John A. Lawrence, Barry A. Pasternack, Wiley, 2002.
Decision Analysis for Management Judgment, 4th Edition, Paul Goodwin, George Wright, Wiley, 2010.
Artificial Intelligence: A Guide to Intelligent Systems (3rd Edition), Michael Negnevitsky, Pearson, 2011.
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(**N.B. Although reading lists should include current publications,
students are advised (particularly for material marked with an asterisk*) to
wait until the start of session for confirmation of the most up-to-date
material)
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| Engagement Requirements |
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In line with the Academic Engagement Procedure, Students are defined as academically engaged if they are regularly engaged with timetabled teaching sessions, course-related learning resources including those in the Library and on the relevant learning platform, and complete assessments and submit these on time. Please refer to the Academic Engagement Procedure at the following link: Academic engagement procedure |
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Supplemental Information
| Programme Board | Computing |
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| Assessment Results (Pass/Fail) |
No
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| Subject Panel | Business & Applied Computing |
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| Moderator | Zeeshan Pervez |
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| External Examiner | R Khusainov |
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| Accreditation Details | |
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| Version Number | 1.10 |
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| Assessment: (also refer to Assessment Outcomes Grids below) |
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| A formal adapted assessment (weighted at 60%). |
| A written assignment (weighted at 40%). |
(N.B. (i) Assessment Outcomes Grids for the module
(one for each component) can be found below which clearly demonstrate how the learning outcomes of the module
will be assessed.
(ii) An indicative schedule listing approximate times
within the academic calendar when assessment is likely to feature will be
provided within the Student Handbook.)
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Assessment Outcome Grids (Footnote A.)
Footnotes
A. Referred to within Assessment Section above
B. Identified in the Learning Outcome Section above
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Note(s):
- More than one assessment method can be used to assess individual learning outcomes.
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Schools are responsible for determining student contact hours. Please refer to University Policy on contact hours (extract contained within section 10 of the Module Descriptor guidance note).
This will normally be variable across Schools, dependent on Programmes &/or Professional requirements.
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| Equality and Diversity |
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This module is suitable for any student. The assessment regime will be applied flexibly so that a student who can attain the practical outcomes of the module will not be disadvantaged. When a student discloses additional support requirements, or if a tutor is concerned about a student, the tutor in consultation with the School Enabling Support coordinator will agree the appropriate adjustments to be made. (N.B. Every effort will be made by the University to accommodate any equality and diversity issues brought to the attention of the School.)
UWS Equality and Diversity Policy |
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(N.B. Every effort
will be made by the University to accommodate any equality and diversity issues
brought to the attention of the School)
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