Course overview
- Study period
- Semester 2, 2024 (22/07/2024 - 18/11/2024)
- Study level
- Postgraduate Coursework
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Civil Engineering School
Understanding and applying the elements of good research design for Engineers is the primary focus of this course. These elements include the critical synthesis of literature and prior knowledge, formulation of research questions, composition of research hypotheses, proper design of field data collection and experiments, collection and analysis of data (observational and experimental), modelling of stochastic data, and interpretation and dissemination of research results.
This postgraduate course teaches the fundamental concepts of how to conduct research in experimental and non-experimental (observational) settings, and teaches a variety of statistical and mathematical modelling tools and techniques for handling empirical data. The fundamental concepts of various statistical and mathematical methods will be taught; however, the prime focus will be on the correct application and interpretation of a variety of statistical and mathematical methods to experimental and non-experimental data. Basic concepts in numerical optimisation will also be taught, with some examples of optimisation problems in Engineering provided. The balance of material will be about 25% research design, considerations, and preparation, 25% statistical and econometric methods, 25% mathematical methods, and 25% optimisation.
In-class examples will be conducted using empirical data, with the majority of examples coming from Engineering applications. Assessments will be focused mostly on applied problems in engineering, and students will be asked to locate data sets specific to their research area or discipline for analysis and to satisfy homework assignments.ﾠMany of the datasets used to illustrate techniques will be a mixture of data from engineering applications. Students will be required to use a readily available statistical software package (e.g. Excel, R)ﾠto analyze data and estimate statistical and mathematical models described in assessments.
Course requirements
Incompatible
You can't enrol in this course if you've already completed the following:
CIVL7505
Course contact
Course staff
Lecturer
Tutor
Dr Saeed Mohammadian
Timetable
The timetable for this course is available on the UQ Public Timetable.
Aims and outcomes
This course aims to provide students with basic knowledge on how to properly conduct a research project using scientific methods and how to produce a research report professionally and reproducibly. Moreover, this course also aims to educate students on essential data analysis skills, including how to i) deal with data quality issues, ii) estimate statistical models on data, and interpret statistical output, and iii) solve simple optimisation problems arising in Engineering contexts.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Explain the basic elements and process of conducting academic research, including but not limited to framing research questions, developing hypotheses, conducting critical literature reviews, designing experiments or observational studies, ethics issues and fallacies & obstacles to scientific research.
LO2.
Identify and cope with common data issues, and carry out descriptive data analysis.
LO3.
Estimate basic statistical models, interpret their findings, and convey results in a meaningful manner.
LO4.
Explain the basic principles of numerical optimization problems in Engineering, how they arise, and how to solve basic problems.
LO5.
Explain basic concepts, theories, and procedures of some commonly used optimisation methods in Engineering.
LO6.
Articulate the limitations of your research and how to remain objective when reviewing your own research or research of others.
LO7.
Write and format your research report professionally and communicate research results in a reproducible manner.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Paper/ Report/ Annotation |
Identifying hypotheses and data
|
15% |
19/09/2024 4:00 pm |
| Project | Statistical Modelling | 25% |
14/10/2024 4:00 pm |
| Tutorial/ Problem Set |
Data Analysis and Optimisation Problem
|
20% |
25/10/2024 4:00 pm |
| Examination |
Final Exam
|
40% |
End of Semester Exam Period 2/11/2024 - 16/11/2024 |
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
Identifying hypotheses and data
- Online
- Mode
- Written
- Category
- Paper/ Report/ Annotation
- Weight
- 15%
- Due date
19/09/2024 4:00 pm
- Learning outcomes
- L01, L02, L06, L07
Task description
The purpose of this homework is to begin the development of a research project.
It will involve:
1) Introduction of topic and establishing importance of topic.
2) Fully articulate the theory/research problem/hypotheses of prime interest.
3) Demonstrate that you understand the critical elements of a literature review needed for undertaking research: Synthesising what is already known about topic; articulating clearly what is not known about topic; motivating the need for further research into this topic and identifying appropriate references.
4) Identify and fully describe a set of data.
This assessment task evaluates students' abilities, skills and knowledge without the aid of generative Artificial Intelligence (AI) or Machine Translation (MT). Students are advised that the use of AI or MT technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Assignment must be submitted via Turnitin through BlackBoard.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 28 days. Extensions are given in multiples of 24 hours.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Statistical Modelling
- Mode
- Written
- Category
- Project
- Weight
- 25%
- Due date
14/10/2024 4:00 pm
- Learning outcomes
- L01, L02, L03, L06, L07
Task description
Using the data provided on the Blackboard, you will estimate a statistical model to describe and explain the data generating process. The assessment shall include:
1) Propose statistical model that represents data generating process
2) Estimate statistical model and defend final specification, show 'best' and '2nd best' models
3) Assess the 'best' model's performance
3) Explain all variables' effects using the 'best' model
The report should be totally reproducible. Two versions of your report should be submitted:
• The qmd (or rmd) file;
• The document (in one of the following formats: PDF, html, or MS Word)generated from the qmd (or rmd) file.
This assessment task evaluates students' abilities, skills and knowledge without the aid of generative Artificial Intelligence (AI) or Machine Translation (MT). Students are advised that the use of AI or MT technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Assignment must be submitted via Turnitin through BlackBoard.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 28 days. Extensions are given in multiples of 24 hours.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Data Analysis and Optimisation Problem
- Online
- Mode
- Written
- Category
- Tutorial/ Problem Set
- Weight
- 20%
- Due date
25/10/2024 4:00 pm
- Learning outcomes
- L03, L04, L05
Task description
This assessment will require you to solve a set of data analysis and rudimentary optimisation problems using concepts taught in class.
This assessment task evaluates students' abilities, skills and knowledge without the aid of generative Artificial Intelligence (AI) or Machine Translation (MT). Students are advised that the use of AI or MT technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Assignment must be submitted via Turnitin through BlackBoard.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 28 days. Extensions are given in multiples of 24 hours.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Final Exam
- Hurdle
- Identity Verified
- In-person
- Mode
- Written
- Category
- Examination
- Weight
- 40%
- Due date
End of Semester Exam Period
2/11/2024 - 16/11/2024
- Other conditions
- Time limited.
- Learning outcomes
- L01, L02, L03, L04, L05, L06
Task description
This assessment task evaluates students' abilities, skills and knowledge without the aid of generative Artificial Intelligence (AI) or Machine Translation (MT). Students are advised that the use of AI or MT technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Hurdle requirements
In order to receive a grade of 4 or more for the course, a student must obtain a mark of at least 40% on the final examination.Exam details
| Planning time | 10 minutes |
|---|---|
| Duration | 120 minutes |
| Calculator options | (In person) Casio FX82 series only or UQ approved and labelled calculator |
| Open/closed book | Closed Book examination - specified written materials permitted |
| Materials | One A4 sheet of handwritten notes, double sided, is permitted |
| 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 - 19.99 |
Absence of evidence of achievement of course learning outcomes. Course grade description: Must achieve weighted score above minimum required for grade and below minimum for next higher grade. |
| 2 (Fail) | 20 - 44.99 |
Minimal evidence of achievement of course learning outcomes. Course grade description: Must achieve weighted score above minimum required for grade and below minimum for next higher grade. |
| 3 (Marginal Fail) | 45 - 49.99 |
Demonstrated evidence of developing achievement of course learning outcomes Course grade description: Must achieve weighted score above minimum required for grade and below minimum for next higher grade. |
| 4 (Pass) | 50 - 64.99 |
Demonstrated evidence of functional achievement of course learning outcomes. Course grade description: Must achieve weighted score above minimum required for grade and below minimum for next higher grade. |
| 5 (Credit) | 65 - 74.99 |
Demonstrated evidence of proficient achievement of course learning outcomes. Course grade description: Must achieve weighted score above minimum required for grade and below minimum for next higher grade. |
| 6 (Distinction) | 75 - 84.99 |
Demonstrated evidence of advanced achievement of course learning outcomes. Course grade description: Must achieve weighted score above minimum required for grade and below minimum for next higher grade. |
| 7 (High Distinction) | 85 - 100 |
Demonstrated evidence of exceptional achievement of course learning outcomes. Course grade description: Must achieve weighted score above minimum required for grade. |
Supplementary assessment
Supplementary assessment is available for this course.
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 in correspondence with course aims Learning outcomes: L01, L02, L03, L04, L05, L06, L07 |
Tutorial |
Tutorial Tutorials and computer coding activities. Learning outcomes: L01, L02, L03, L04, L05, L06, L07 |
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: