Course coordinator
Dr Josh Arnold
Course overview
- Study period
- Semester 1, 2025 (24/02/2025 - 21/06/2025)
- Study level
- Postgraduate Coursework
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Elec Engineering & Comp Science School
Information systems analysis, design and implementation, relational database technology, data modelling, data quering using SQL, building a small scale information system using a relational database management system.
This course provides the foundation concepts on designing and implementing information systems, necessary for advanced data management and data analysis courses taught in various Information Technology, Engineering, Business and Science programs. The course includes modules on data modelling, principles of correct database design, the SQL language for querying relational databases, and developing a small scale database application using MySQL.
Course requirements
Assumed background
Senior Math B or MATH1040. Basic set theory is useful. No computer programming experience is assumed.
Incompatible
You can't enrol in this course if you've already completed the following:
INFS1200 or BISM2207 or BISM3203 or BISM7206 or INFS7901
Course contact
Course staff
Lecturer
Dr Josh Arnold
Professor Shazia Sadiq
Dr Md Abdul Awal
Timetable
The timetable for this course is available on the UQ Public Timetable.
Aims and outcomes
This course aims to introduce students to the foundation concepts onﾠdesigning and implementingﾠinformation systems, necessaryﾠfor advanced data managementﾠand data analysis courses taught later in various programs.ﾠThe course will prepare students to deploy small information system in MySQL
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Extract information systems requirements to create basic conceptual models
LO2.
Map basic conceptual data models to relational database schema
LO3.
Express natural language queries correctly using the SQL language
LO4.
Reason with the logical foundations of the relational data model and the fundamental principles of correct relational database design
LO5.
Explain key security concepts related to database control measures and SQL injection
LO6.
Construct a small-scale information system in a relational database management system
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Tutorial/ Problem Set |
Weekly RiPPLE activities
|
10% (Best 9 of 11) |
Week 3, Fri 4:00 pm Week 4, Fri 4:00 pm Week 5, Fri 4:00 pm Week 6, Fri 4:00 pm Week 7, Fri 4:00 pm Week 8, Thu 4:00 pm Week 9, Fri 4:00 pm Week 10, Fri 4:00 pm Week 11, Fri 4:00 pm Week 12, Fri 4:00 pm Week 13, Fri 4:00 pm
Due 4pm each Friday from Week 3 to Week 13 (except Round 6 due Thursday in Week 8) |
| Paper/ Report/ Annotation, Tutorial/ Problem Set | Assignment 1 | 25% |
11/04/2025 3:00 pm |
| Computer Code |
Assignment 2
|
25% |
9/05/2025 3:00 pm
Oral assessment will be run during practical sessions in Week 12 |
| Examination |
Final exam
|
40% |
End of Semester Exam Period 7/06/2025 - 21/06/2025 |
A hurdle is an assessment requirement that must be satisfied in order to receive a specific grade for the course. Check the assessment details for more information about hurdle requirements.
Assessment details
Weekly RiPPLE activities
- Online
- Mode
- Written
- Category
- Tutorial/ Problem Set
- Weight
- 10% (Best 9 of 11)
- Due date
Week 3, Fri 4:00 pm
Week 4, Fri 4:00 pm
Week 5, Fri 4:00 pm
Week 6, Fri 4:00 pm
Week 7, Fri 4:00 pm
Week 8, Thu 4:00 pm
Week 9, Fri 4:00 pm
Week 10, Fri 4:00 pm
Week 11, Fri 4:00 pm
Week 12, Fri 4:00 pm
Week 13, Fri 4:00 pm
Due 4pm each Friday from Week 3 to Week 13 (except Round 6 due Thursday in Week 8)
- Other conditions
- Peer assessed.
- Learning outcomes
- L01, L02, L03, L04, L05
Task description
Twelve weekly assessment activities run through RiPPLe are designed to test information system analysis and design techniques. The assessable tasks will consist of a combination of learning resource creation, moderation and practice. Submissions will be due at the end of each teaching week beginning in Week 3 and ending Week 13. The best 9 of 11 submissions will be used to calculate a student's mark, with each worth 1% of the course mark. Detailed task specifications will be announced and placed on Blackboard at the required time.
Submission guidelines
The assessment will be submitted online via the RiPPLE platform. Detailed instructions will be provided on Blackboard the task specification.
Generative AI and Machine Translation
Artificial Intelligence (AI) and Machine Translation (MT) are emerging tools that may support students in completing this assessment task. Students may appropriately use AI and/or MT in completing this assessment task. Students must clearly reference any use of AI or MT in each instance. A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct
Deferral or extension
You cannot defer or apply for an extension for this assessment.
To accommodate unforeseen circumstances such as illness, the best 9 of 11 submissions will contribute to the total RiPPLE mark, allowing for two missed submissions across the semester.
Late submission
You will receive a mark of 0 if this assessment is submitted late.
Assignment 1
- Mode
- Product/ Artefact/ Multimedia, Written
- Category
- Paper/ Report/ Annotation, Tutorial/ Problem Set
- Weight
- 25%
- Due date
11/04/2025 3:00 pm
- Learning outcomes
- L01, L02
Task description
The assignments are designed to test information system analysis and design techniques as well as the information system development skills acquired in practical sessions. Assignment 1 focuses on ER models and the relational data model. Task specifications will be announced and placed on Blackboard at the required time. Please ensure that you download your copy as soon as possible on or after the date of issue. Students are advised to progressively submit versions of their assignments, and make backup copies, as internet connections and software can fail (or be lost) for many reasons and it happens quite frequently.
Submission guidelines
The assignment will be submitted online via Gradescope. Detailed instructions will be provided in the assignment description.
Generative AI and Machine Translation
Artificial Intelligence (AI) and Machine Translation (MT) are emerging tools that may support students in completing this assessment task. Students may appropriately use AI and/or MT in completing this assessment task. Students must clearly reference any use of AI or MT in each instance. A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
Marked submissions with feedback and/or detailed solutions with feedback will be released to students within 7 days.
Late submission
Assignment submissions received after the due time (or any approved extended deadline) will be subject to a 100% late penalty. A one-hour grace period applies to the due time after which time (4:00pm) the 100% late penalty will be imposed. This grace period is designed to deal with issues that might arise during submission (e.g. delays with Blackboard or Gradescope) and should not be considered a shift of the due time.
Assignment 2
- Online
- Mode
- Oral, Product/ Artefact/ Multimedia, Written
- Category
- Computer Code
- Weight
- 25%
- Due date
9/05/2025 3:00 pm
Oral assessment will be run during practical sessions in Week 12
- Learning outcomes
- L03, L04, L06
Task description
The assignments are designed to test information system analysis and design techniques as well as the information system development skills acquired in practical sessions. Assignment 2 focuses on SQL and specifications will be announced and placed on Blackboard at the required time. Please ensure that you download your copy as soon as possible on or after the date of issue. Students are advised to progressively submit versions of their coding assignments, and make back-up copies, as internet connections and software can fail (or be lost) for many reasons and it happens quite frequently.
This assessment item has two parts:
- Part one is an online code submission.
- Part two is an oral critique of your submitted code. The oral interview is a secure component to Assignment 2, and will be completed during an in-person meeting with a demonstrator scheduled during practical sessions in Week 12, after the code submission. In the meeting with the demonstrator, students will discuss the work they have submitted and explain their approach and why they used particular techniques. All oral assessments must be given live and will be recorded by the teaching team for archiving purposes. Students must achieve a pass (+/-) for the critique portion of the task to be eligible to pass Assignment 2.
Submission guidelines
- Part one of the assignment will be submitted online via Gradescope.
- Part two will be conducted in practical sessions during Week 12.
Detailed instructions will be provided in the assignment sheet and via Blackboard announcements.
Generative AI and Machine Translation
This task has been designed to be challenging, authentic and complex. Whilst students may use AI and/or MT technologies, successful completion of assessment in this course will require students to critically engage in specific contexts and tasks for which artificial intelligence will provide only limited support and guidance.
A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
To pass this assessment, students will be required to demonstrate detailed comprehension of their written submission independent of AI and MT tools.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
Late submission
Part 1: Assignment submissions received after the due time (or any approved extended deadline) will be subject to a 100% late penalty. A one-hour grace period applies to the due time after which time (4:00pm) the 100% late penalty will be imposed. This grace period is designed to deal with issues that might arise during submission (e.g. delays with Blackboard or Gradescope) and should not be considered a shift of the due time.
Part 2: Will be conducted in practical sessions during Week 12. Non-attendance, without an approved extension, will result in a fail mark for the practical session. Note that an approved extension for Part 1 does not automatically provide an extension for Part 2.
Final exam
- Hurdle
- Identity Verified
- In-person
- Mode
- Written
- Category
- Examination
- Weight
- 40%
- Due date
End of Semester Exam Period
7/06/2025 - 21/06/2025
- Learning outcomes
- L01, L02, L03, L04, L05
Task description
Exams are designed to test theoretical concepts and modelling techniques introduced in lectures and tutorials. More information on exams will be provided on the assessment page of the course website at the appropriate time. Students will sit an on-campus invigilated exam. The use of Artificial Intelligence (AI) tools will not be permitted. Any attempted use of AI may constitute student misconduct under the Student Code of Conduct.
Hurdle requirements
Mark of 50/100 on final exam is required to pass the course. Students who fail to meet this requirement will have their mark capped at 49 and receive grade of at most 3.Exam details
| Planning time | 10 minutes |
|---|---|
| Duration | 120 minutes |
| Calculator options | No calculators permitted |
| Open/closed book | Closed Book examination - specified written materials permitted |
| Materials | One A4 sheet double-sided of typed or handwritten notes |
| Exam platform | Paper based |
| Invigilation | Invigilated in person |
Submission guidelines
Deferral or extension
You may be able to defer this exam.
Course grading
Full criteria for each grade is available in the Assessment Procedure.
| Grade | Cut off Percent | Description |
|---|---|---|
| 1 (Low Fail) | 0 - 19 |
Absence of evidence of achievement of course learning outcomes. Course grade description: Total of at most 19%. |
| 2 (Fail) | 20 - 46 |
Minimal evidence of achievement of course learning outcomes. Course grade description: Total of at least 20%. |
| 3 (Marginal Fail) | 47 - 49 |
Demonstrated evidence of developing achievement of course learning outcomes Course grade description: Total of at least 47%. |
| 4 (Pass) | 50 - 64 |
Demonstrated evidence of functional achievement of course learning outcomes. Course grade description: Total of at least 50%, and pass (i.e. 50%) in the final exam. |
| 5 (Credit) | 65 - 74 |
Demonstrated evidence of proficient achievement of course learning outcomes. Course grade description: Total of at least 65%, and pass (i.e. 50%) in the final exam. |
| 6 (Distinction) | 75 - 84 |
Demonstrated evidence of advanced achievement of course learning outcomes. Course grade description: Total of at least 75%, and pass (i.e. 50%) in the final exam. |
| 7 (High Distinction) | 85 - 100 |
Demonstrated evidence of exceptional achievement of course learning outcomes. Course grade description: Total of at least 85%, and pass (i.e. 50%) in the final exam. |
Additional course grading information
Your final percentage will be rounded to the nearest whole number.
Students must receive a passing grade on the final exam in order to pass this course (i.e.ﾠachieve at least 50/100). If you fail the exam, your final mark will be capped at 49 and your final grade will be capped at 3.
The course coordinator reserves the right to moderate marks.
Supplementary assessment
Supplementary assessment is available for this course.
Additional assessment information
Generative AI and Machine Translation in Assessment
Artificial Intelligence (AI) and Machine Translation (MT) are emerging tools that may support students in completing this assessment task. Students may appropriately use AI and/or MT in completing the Assignment 1 and 2 tasks. Students must clearly reference any use of AI or MT in each instance.
A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
Having Troubles?
If you are having difficulties with any aspect of the course material you should seek help. Speak to the course teaching staff.
If external circumstances are affecting your ability to work on the course, you should seek help as soon as possible. The University and UQ Union have organisations and staff who are able to help, for example, UQ Student Services are able to help with study and exam skills, tertiary learning skills, writing skills, financial assistance, personal issues, and disability services (among other things).
Complaints and criticisms should be directed in the first instance to the course coordinator. If you are not satisfied with the outcome, you may bring the matter to the attention of the School of EECS Director of Teaching and Learning.
Learning resources
You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.
Library resources
Find the required and recommended resources for this course on the UQ Library website.
Learning activities
The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.
Filter activity type by
Please select
| Learning period | Activity type | Topic |
|---|---|---|
Multiple weeks From Week 1 To Week 13 |
Lecture |
Lectures Lectures will provide an introduction to various concepts and techniques in information systems analysis and the design of Database Management Systems (DBMS). Lecture notes and recordings will be available on Blackboard. Learning outcomes: L01, L02, L03, L04, L05 |
Problem-based learning |
In-class exercises A series of in-class exercises will be conducted to provide opportunity for better student engagement with content and in depth discussions. Learning outcomes: L01, L02, L03, L04, L05 |
|
Multiple weeks From Week 2 To Week 13 |
Tutorial |
Small group work Contact sessions will provide opportunity to practice the techniques introduced in lectures Learning outcomes: L01, L02, L03, L04, L05 |
Practical |
Lab work Practicals will allow students to get hands on experience in implementing small scale information systems using a relational DBMS and practicing SQL. Learning outcomes: L01, L02, L03, L04, L05, L06 |
|
Information technology session |
RiPPLE personalised learning activities Students will be required to complete four RiPPLE rounds, which includes answering, generating and reviewing instructor and peer-generated questions. This work forms a progressive assessment task. Learning outcomes: L01, L02, L03, L04, L05 |
Policies and procedures
University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:
- Student Code of Conduct Policy
- Student Integrity and Misconduct Policy and Procedure
- Assessment Procedure
- Examinations Procedure
- Reasonable Adjustments - Students Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.
School guidelines
Your school has additional guidelines you'll need to follow for this course: