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2019/20 Undergraduate Module Catalogue

COMP3771 User Adaptive Intelligent Systems

10 creditsClass Size: 60

Module manager: Dr Lydia Lau

Taught: Semester 2 View Timetable

Year running 2019/20

Pre-requisite qualifications

Students must study one of the below modules.


COMP2611Artificial Intelligence

This module is not approved as a discovery module


On completion of this module, students should be able to:
- apply human-computer interaction methodology to identify user needs, draw requirements, design, and evaluate user-adaptive systems;
- identify most common techniques for user modelling and adaptation and apply them in practical areas;
- implement one or more recommender system techniques in a practical application;
- reason about the significance of user-adaptive systems and directions the field is going to develop.

Learning outcomes
On completion of this module, students should be able to:
-understand and demonstrate coherent and detailed subject knowledge and professional competencies some of which will be informed by recent research/scholarship in the discipline;
-deploy accurately standard techniques of analysis and enquiry within the discipline;
-demonstrate a conceptual understanding which enables the development and sustaining of an argument;
-describe and comment on particular aspects of recent research and/or scholarship;
-appreciate the uncertainty, ambiguity and limitations of knowledge in the discipline;
-make appropriate use of scholarly reviews and primary sources;
-apply their knowledge and understanding in order to initiate and carry out an extended piece of work or project;

Skills outcomes
Experience and understanding of techniques for user modelling and their application to build user adaptive intelligent systems


- Adaptable and adaptive systems;
- General Schema of User-adaptive Systems;
- Basics for User Modelling;
- User Profiling (implicit and explicit methods);
- Stereotypes (construction and use);
- Modelling user knowledge and beliefs, affect and context;
- Adaptive Content Presentation - static and dynamic;
- Recommender Systems - item-item and user-user collaborative filtering, content-based approach, and hybrid approaches;
- Issues of scalability, diversity, potentials and drawbacks;
- Making prediction about the User;
- Evaluation of adaptive systems;
- Trends.

Teaching methods

Delivery typeNumberLength hoursStudent hours
Private study hours80.00
Total Contact hours20.00
Total hours (100hr per 10 credits)100.00

Private study

Recommended 40 hrs of private study, to include 20 hrs working on summative coursework and 20 hrs private study following lectures. Remaining hours to read the articles issued in lectures and revision for examination.

Opportunities for Formative Feedback

Progress is monitored through class exercises throughout the module and summative coursework.

Methods of assessment

Assessment typeNotes% of formal assessment
Report10 page project report20.00
Total percentage (Assessment Coursework)20.00

This module is re-assessed by exam only.

Exam typeExam duration% of formal assessment
Open Book exam2 hr 00 mins80.00
Total percentage (Assessment Exams)80.00

This module is re-assessed by exam only.

Reading list

The reading list is available from the Library website

Last updated: 30/04/2019


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