MXB161 Computational Explorations
To view more information for this unit, select Unit Outline from the list below. Please note the teaching period for which the Unit Outline is relevant.
| Unit code: | MXB161 |
|---|---|
| Prerequisite(s): | Admission to (MS01 or MV01 or MV05 or MV06 or DS01 or IX30 or SE20 or SE30 or SE40 or SE70 or ST20 or SE07 or ID29) or 48 credit points of study in current course |
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
| CSP student contribution | $592 |
| Domestic tuition unit fee | $3,816 |
| International unit fee | $4,872 |
Unit Outline: Semester 1 2026, Gardens Point, Internal
| Unit code: | MXB161 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | Admission to (MS01 or MV01 or MV05 or MV06 or DS01 or IX30 or SE20 or SE30 or SE40 or SE70 or ST20 or SE07 or ID29) or 48 credit points of study in current course |
| Coordinators: | Qianqian Yang | q.yang@qut.edu.au Nicholas Buttle | n.buttle@qut.edu.au Paul Corry | p.corry@qut.edu.au |
Overview
This unit introduces you to techniques of computation and simulation across a range of application areas in Science, Technology, Engineering and Mathematics (STEM). Computation and simulation are cornerstones of modern practice across STEM; practitioners skilled in these areas can explore behaviours of real-world systems that would be impractical or impossible to undertake using only theoretical or experimental means. In this introductory unit, you will develop your computation and simulation skills through individual and collaborative problem-solving activities. Depending on your course, further exploration may be available through a minor in this field.
Learning Outcomes
On successful completion of this unit you will be able to:
- Demonstrate knowledge of a range of data formats, and the techniques to manipulate such data.
- Use programming skills to implement computational solutions to problems using MATLAB.
- Apply problem solving and critical thinking skills in adapting, extending and synthesising learned techniques to solve real world problems.
- Collaborate and use effective teamwork skills in a multidisciplinary team environment.
Content
Introduction to the multipdisciplinary field of Computational Science. MATLAB as a tool for data manipulation, input/output and visualisation. Programming fundamentals as implemented in MATLAB. Analysis of spatial data including latitude/longitude and x-y coordinate data. Image formats and data types, image manipulation and processing, animations, input/output of images and animations. Sound formats and data types, sound manipulation and processing, time/frequency analysis of signals, input/output of sounds. Random walk simulations, 1D and 2D lattice-based, extensions and applications including diffusion. Cellular automata, 1D and 2D grids, Conway's Game of Life
Learning Approaches
This unit is available for you to study in either on-campus or online mode. You can expect to spend 10 - 15 hours per week involved in preparing for and attending scheduled classes, preparing and completing assessment tasks as well as independent study and consolidation of your learning.
This unit engages you in your learning through a theory-to-practice approach, and is by its design a multi-disciplinary learning experience. The unit is taught in the form of weekly topics, with the delivery of topic content followed by opportunities to complete practical exercises to develop your programming and problem-solving skills in MATLAB, both individually and in group work.
Each week comprises an online interactive lecture which will introduce the application area along with any new required programming techniques, a computer-based individual practical where you hone the knowledge and skills, and a computer-based group practical where you will engage in collaborative activity with your peers in the form of multi-disciplinary groups. Your weekly progress in MATLAB programming competency and understanding of weekly topic content will be supported by formative online quizzes embedded in each weekly worksheet.
As a first year unit, your learning will be carefully guided and scaffolded by the teaching staff, but you will be expected to develop some self-directed learning capabilities to facilitate your transition from dependent to independent learner. Additional free support relating to this unit is available through the STIMulate peer program.
Feedback on Learning and Assessment
Formative feedback will be provided through online quizzes and though weekly written feedback provided on group worksheets.
Summative feedback will be provided throughout the semester with results posted via Canvas.
Assessment
Overview
The assessment items in this unit are designed to determine your level of competency in meeting the unit learning outcomes while providing you with a range of tasks with varying levels of skill development and difficulty.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Problem Solving Task
You will be required to solve specific coding problems assigned to you to determine your level of competency in MATLAB programming and to measure your understanding the of weekly topic content. These coding problems will be of a similar nature to the online formative quizzes embedded in each weekly module. Your submission will take the form of short MATLAB code files, which will be marked for their adherence to the requirements set out in the specific question.
The ethical and responsible use of generative artificial intelligence (GenAI) tools is authorised in this assessment. See the relevant assessment details in Canvas for specific guidelines.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Portfolio
The portfolio comprises the work undertaken by your group in the weekly group practical classes. These consist of your refined computational solutions to the real-world applications introduced in the weekly group worksheets. Throughout the semester you will receive formative feedback on your group's initial attempt at the worksheet solutions, and will have until the end of semester to act upon this feedback for the final, summative submission.
Furthermore, in the final weeks of semester, your group will develop a computational solution to a novel application of your choosing to also include in the portfolio. In all, your portfolio will comprise MATLAB Live Script files that present your group’s solution to the provided application questions, as well as the novel application of your group’s choosing.
The ethical and responsible use of generative artificial intelligence (GenAI) tools is authorised in this assessment. See the relevant assessment details in Canvas for specific guidelines.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Examination (invigilated)
A written examination that requires you to demonstrate programming knowledge and computational problem solving skills, drawing on all material covered throughout the semester.
The use of generative artificial intelligence (GenAI) tools is prohibited during this assessment.
The examination will require attendance at a local testing centre. For students enrolled as internal or on-campus, the local testing centre will be on QUT campus. For students enrolled as online, QUT Examinations will provide local testing centre information.
Academic Integrity
Academic integrity is a commitment to undertaking academic work and assessment in a manner that is ethical, fair, honest, respectful and accountable.
The Academic Integrity Policy sets out the range of conduct that can be a failure to maintain the standards of academic integrity. This includes, cheating in exams, plagiarism, self-plagiarism, collusion and contract cheating. It also includes providing fraudulent or altered documentation in support of an academic concession application, for example an assignment extension or a deferred exam.
You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.
Breaching QUT’s Academic Integrity Policy or engaging in conduct that may defeat or compromise the purpose of assessment can lead to a finding of student misconduct (Code of Conduct – Student) and result in the imposition of penalties under the Management of Student Misconduct Policy, ranging from a grade reduction to exclusion from QUT.
Resources
All learning materials for this unit will be made available in the Canvas unit site. There is no set text for this unit.
Risk Assessment Statement
There is minimal health and safety risk in this unit. It is your responsibility to familiarise yourself with the Health and Safety policies and procedures applicable within campus areas and laboratories.
Standards/Competencies
This unit is designed to support your development of the following standards\competencies.
Engineers Australia Stage 1 Competency Standard for Professional Engineer
1: Knowledge and Skill Base
Relates to: Problem Solving Task, Portfolio, Examination (invigilated)
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.DS01 Bachelor of Data Science
- Demonstrate a broad and coherent knowledge of the principles, concepts and techniques of the data science discipline, with depth of knowledge in at least one area developed through a major.
Relates to: ULO4, Problem Solving Task, Portfolio, Examination (invigilated) - Use appropriate statistical, computational, modelling, data management, programming and generative artificial intelligence techniques to develop solutions for deriving insights from data.
Relates to: ULO4, Problem Solving Task, Portfolio, Examination (invigilated) - Demonstrate critical thinking and problem-solving skills, as well as adaptivity in applying learned techniques in new and unfamiliar contexts.
Relates to: ULO4, Problem Solving Task, Portfolio, Examination (invigilated) - Work effectively both independently and collaboratively in diverse and interdisciplinary teams.
Relates to: Portfolio
EN01 Bachelor of Engineering (Honours)
- Display leadership, creativity, and initiative in both self-directed and collaborative contexts of professional engineering practice.
Relates to: ULO4, Portfolio - Manage projects to solve complex engineering problems, using appropriate information, engineering methods, and technologies.
Relates to: ULO3, Problem Solving Task, Portfolio, Examination (invigilated) - Demonstrate coherent knowledge and skills of physical, mathematical, statistical, computer, and information sciences that are fundamental to professional engineering practice.
Relates to: ULO1, ULO2, Problem Solving Task, Portfolio, Examination (invigilated)
EN29 Bachelor of Engineering Studies
- Evidence of displaying leadership, creativity, and initiative in both self-directed and collaborative contexts of professional engineering practice.
Relates to: ULO4, Portfolio - Evidence of being able to manage projects to solve some engineering problems, using appropriate information, engineering methods and technologies.
Relates to: ULO3, Problem Solving Task, Portfolio, Examination (invigilated) - Evidence of engaging with and applying regulatory requirements relating to safety, risk management and sustainability in professional engineering practice.
Relates to: ULO1 - Evidence of demonstrating coherent knowledge and skills of physical, mathematical, statistical, computer and information science.
Relates to: ULO2, Problem Solving Task, Portfolio, Examination (invigilated)
EV01 Bachelor of Engineering (Honours)
- Display leadership, creativity, and initiative in both self-directed and collaborative contexts of professional engineering practice.
Relates to: Portfolio - Manage projects to solve complex engineering problems, using appropriate information, engineering methods, and technologies.
Relates to: Problem Solving Task, Portfolio, Examination (invigilated) - Demonstrate coherent knowledge and skills of physical, mathematical, statistical, computer, and information sciences that are fundamental to professional engineering practice.
Relates to: Problem Solving Task, Portfolio, Examination (invigilated)
MS01 Bachelor of Mathematics
- Formulate and model problems in mathematical terms and apply appropriate mathematical, statistical and computational techniques to solve practical and abstract problems.
Relates to: Problem Solving Task, Portfolio, Examination (invigilated) - Demonstrate aptitude in computer programming, and familiarity with industry-leading programming languages and relevant specialised mathematical, statistical and generative artificial intelligence software and tools.
Relates to: Problem Solving Task, Portfolio, Examination (invigilated) - Demonstrate critical thinking and problem solving skills across a range of applied mathematical and statistical contexts, and adaptivity in applying learned techniques in new or unfamiliar contexts.
Relates to: Problem Solving Task, Portfolio, Examination (invigilated) - Work both independently and collaboratively in diverse teams, including cross-cultural and cross-disciplinary teams.
Relates to: Portfolio
MV01 Bachelor of Mathematics
- Formulate and model problems in mathematical terms and apply appropriate mathematical, statistical and computational techniques to solve practical and abstract problems.
Relates to: Problem Solving Task, Portfolio, Examination (invigilated) - Demonstrate aptitude in computer programming, and familiarity with industry-leading programming languages and relevant specialised mathematical, statistical and generative artificial intelligence software and tools.
Relates to: Problem Solving Task, Portfolio, Examination (invigilated) - Demonstrate critical thinking and problem solving skills across a range of applied mathematical and statistical contexts, and adaptivity in applying learned techniques in new or unfamiliar contexts.
Relates to: Problem Solving Task, Portfolio, Examination (invigilated) - Work both independently and collaboratively in diverse teams, including cross-cultural and cross-disciplinary teams.
Relates to: Portfolio
Unit Outline: Semester 1 2026, Online
| Unit code: | MXB161 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | Admission to (MS01 or MV01 or MV05 or MV06 or DS01 or IX30 or SE20 or SE30 or SE40 or SE70 or ST20 or SE07 or ID29) or 48 credit points of study in current course |
Overview
This unit introduces you to techniques of computation and simulation across a range of application areas in Science, Technology, Engineering and Mathematics (STEM). Computation and simulation are cornerstones of modern practice across STEM; practitioners skilled in these areas can explore behaviours of real-world systems that would be impractical or impossible to undertake using only theoretical or experimental means. In this introductory unit, you will develop your computation and simulation skills through individual and collaborative problem-solving activities. Depending on your course, further exploration may be available through a minor in this field.
Learning Outcomes
On successful completion of this unit you will be able to:
- Demonstrate knowledge of a range of data formats, and the techniques to manipulate such data.
- Use programming skills to implement computational solutions to problems using MATLAB.
- Apply problem solving and critical thinking skills in adapting, extending and synthesising learned techniques to solve real world problems.
- Collaborate and use effective teamwork skills in a multidisciplinary team environment.
Content
Introduction to the multipdisciplinary field of Computational Science. MATLAB as a tool for data manipulation, input/output and visualisation. Programming fundamentals as implemented in MATLAB. Analysis of spatial data including latitude/longitude and x-y coordinate data. Image formats and data types, image manipulation and processing, animations, input/output of images and animations. Sound formats and data types, sound manipulation and processing, time/frequency analysis of signals, input/output of sounds. Random walk simulations, 1D and 2D lattice-based, extensions and applications including diffusion. Cellular automata, 1D and 2D grids, Conway's Game of Life
Learning Approaches
This unit is available for you to study in either on-campus or online mode. You can expect to spend 10 - 15 hours per week involved in preparing for and attending scheduled classes, preparing and completing assessment tasks as well as independent study and consolidation of your learning.
This unit engages you in your learning through a theory-to-practice approach, and is by its design a multi-disciplinary learning experience. The unit is taught in the form of weekly topics, with the delivery of topic content followed by opportunities to complete practical exercises to develop your programming and problem-solving skills in MATLAB, both individually and in group work.
Each week comprises an online interactive lecture which will introduce the application area along with any new required programming techniques, a computer-based individual practical where you hone the knowledge and skills, and a computer-based group practical where you will engage in collaborative activity with your peers in the form of multi-disciplinary groups. Your weekly progress in MATLAB programming competency and understanding of weekly topic content will be supported by formative online quizzes embedded in each weekly worksheet.
As a first year unit, your learning will be carefully guided and scaffolded by the teaching staff, but you will be expected to develop some self-directed learning capabilities to facilitate your transition from dependent to independent learner. Additional free support relating to this unit is available through the STIMulate peer program.
Feedback on Learning and Assessment
Formative feedback will be provided through online quizzes and though weekly written feedback provided on group worksheets.
Summative feedback will be provided throughout the semester with results posted via Canvas.
Assessment
Overview
The assessment items in this unit are designed to determine your level of competency in meeting the unit learning outcomes while providing you with a range of tasks with varying levels of skill development and difficulty.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Problem Solving Task
You will be required to solve specific coding problems assigned to you to determine your level of competency in MATLAB programming and to measure your understanding the of weekly topic content. These coding problems will be of a similar nature to the online formative quizzes embedded in each weekly module. Your submission will take the form of short MATLAB code files, which will be marked for their adherence to the requirements set out in the specific question.
The ethical and responsible use of generative artificial intelligence (GenAI) tools is authorised in this assessment. See the relevant assessment details in Canvas for specific guidelines.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Portfolio
The portfolio comprises the work undertaken by your group in the weekly group practical classes. These consist of your refined computational solutions to the real-world applications introduced in the weekly group worksheets. Throughout the semester you will receive formative feedback on your group's initial attempt at the worksheet solutions, and will have until the end of semester to act upon this feedback for the final, summative submission.
Furthermore, in the final weeks of semester, your group will develop a computational solution to a novel application of your choosing to also include in the portfolio. In all, your portfolio will comprise MATLAB Live Script files that present your group’s solution to the provided application questions, as well as the novel application of your group’s choosing.
The ethical and responsible use of generative artificial intelligence (GenAI) tools is authorised in this assessment. See the relevant assessment details in Canvas for specific guidelines.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Examination (invigilated)
A written examination that requires you to demonstrate programming knowledge and computational problem solving skills, drawing on all material covered throughout the semester.
The use of generative artificial intelligence (GenAI) tools is prohibited during this assessment.
The examination will require attendance at a local testing centre. For students enrolled as internal or on-campus, the local testing centre will be on QUT campus. For students enrolled as online, QUT Examinations will provide local testing centre information.
Academic Integrity
Academic integrity is a commitment to undertaking academic work and assessment in a manner that is ethical, fair, honest, respectful and accountable.
The Academic Integrity Policy sets out the range of conduct that can be a failure to maintain the standards of academic integrity. This includes, cheating in exams, plagiarism, self-plagiarism, collusion and contract cheating. It also includes providing fraudulent or altered documentation in support of an academic concession application, for example an assignment extension or a deferred exam.
You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.
Breaching QUT’s Academic Integrity Policy or engaging in conduct that may defeat or compromise the purpose of assessment can lead to a finding of student misconduct (Code of Conduct – Student) and result in the imposition of penalties under the Management of Student Misconduct Policy, ranging from a grade reduction to exclusion from QUT.
Resources
All learning materials for this unit will be made available in the Canvas unit site. There is no set text for this unit.
Risk Assessment Statement
There is minimal health and safety risk in this unit. It is your responsibility to familiarise yourself with the Health and Safety policies and procedures applicable within campus areas and laboratories.
Standards/Competencies
This unit is designed to support your development of the following standards\competencies.
Engineers Australia Stage 1 Competency Standard for Professional Engineer
1: Knowledge and Skill Base
Relates to: Problem Solving Task, Portfolio, Examination (invigilated)
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.DS01 Bachelor of Data Science
- Demonstrate a broad and coherent knowledge of the principles, concepts and techniques of the data science discipline, with depth of knowledge in at least one area developed through a major.
Relates to: ULO4, Problem Solving Task, Portfolio, Examination (invigilated) - Use appropriate statistical, computational, modelling, data management, programming and generative artificial intelligence techniques to develop solutions for deriving insights from data.
Relates to: ULO4, Problem Solving Task, Portfolio, Examination (invigilated) - Demonstrate critical thinking and problem-solving skills, as well as adaptivity in applying learned techniques in new and unfamiliar contexts.
Relates to: ULO4, Problem Solving Task, Portfolio, Examination (invigilated) - Work effectively both independently and collaboratively in diverse and interdisciplinary teams.
Relates to: Portfolio
EN01 Bachelor of Engineering (Honours)
- Display leadership, creativity, and initiative in both self-directed and collaborative contexts of professional engineering practice.
Relates to: ULO4, Portfolio - Manage projects to solve complex engineering problems, using appropriate information, engineering methods, and technologies.
Relates to: ULO3, Problem Solving Task, Portfolio, Examination (invigilated) - Demonstrate coherent knowledge and skills of physical, mathematical, statistical, computer, and information sciences that are fundamental to professional engineering practice.
Relates to: ULO1, ULO2, Problem Solving Task, Portfolio, Examination (invigilated)
EN29 Bachelor of Engineering Studies
- Evidence of displaying leadership, creativity, and initiative in both self-directed and collaborative contexts of professional engineering practice.
Relates to: ULO4, Portfolio - Evidence of being able to manage projects to solve some engineering problems, using appropriate information, engineering methods and technologies.
Relates to: ULO3, Problem Solving Task, Portfolio, Examination (invigilated) - Evidence of engaging with and applying regulatory requirements relating to safety, risk management and sustainability in professional engineering practice.
Relates to: ULO1 - Evidence of demonstrating coherent knowledge and skills of physical, mathematical, statistical, computer and information science.
Relates to: ULO2, Problem Solving Task, Portfolio, Examination (invigilated)
EV01 Bachelor of Engineering (Honours)
- Display leadership, creativity, and initiative in both self-directed and collaborative contexts of professional engineering practice.
Relates to: Portfolio - Manage projects to solve complex engineering problems, using appropriate information, engineering methods, and technologies.
Relates to: Problem Solving Task, Portfolio, Examination (invigilated) - Demonstrate coherent knowledge and skills of physical, mathematical, statistical, computer, and information sciences that are fundamental to professional engineering practice.
Relates to: Problem Solving Task, Portfolio, Examination (invigilated)
MS01 Bachelor of Mathematics
- Formulate and model problems in mathematical terms and apply appropriate mathematical, statistical and computational techniques to solve practical and abstract problems.
Relates to: Problem Solving Task, Portfolio, Examination (invigilated) - Demonstrate aptitude in computer programming, and familiarity with industry-leading programming languages and relevant specialised mathematical, statistical and generative artificial intelligence software and tools.
Relates to: Problem Solving Task, Portfolio, Examination (invigilated) - Demonstrate critical thinking and problem solving skills across a range of applied mathematical and statistical contexts, and adaptivity in applying learned techniques in new or unfamiliar contexts.
Relates to: Problem Solving Task, Portfolio, Examination (invigilated) - Work both independently and collaboratively in diverse teams, including cross-cultural and cross-disciplinary teams.
Relates to: Portfolio
MV01 Bachelor of Mathematics
- Formulate and model problems in mathematical terms and apply appropriate mathematical, statistical and computational techniques to solve practical and abstract problems.
Relates to: Problem Solving Task, Portfolio, Examination (invigilated) - Demonstrate aptitude in computer programming, and familiarity with industry-leading programming languages and relevant specialised mathematical, statistical and generative artificial intelligence software and tools.
Relates to: Problem Solving Task, Portfolio, Examination (invigilated) - Demonstrate critical thinking and problem solving skills across a range of applied mathematical and statistical contexts, and adaptivity in applying learned techniques in new or unfamiliar contexts.
Relates to: Problem Solving Task, Portfolio, Examination (invigilated) - Work both independently and collaboratively in diverse teams, including cross-cultural and cross-disciplinary teams.
Relates to: Portfolio
Unit Outline: Semester 2 2026, Gardens Point, Internal
| Unit code: | MXB161 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | Admission to (MS01 or MV01 or MV05 or MV06 or DS01 or IX30 or SE20 or SE30 or SE40 or SE70 or ST20 or SE07 or ID29) or 48 credit points of study in current course |
| Coordinators: | Timothy Moroney | t.moroney@qut.edu.au |
Overview
This unit introduces you to techniques of computation and simulation across a range of application areas in Science, Technology, Engineering and Mathematics (STEM). Computation and simulation are cornerstones of modern practice across STEM; practitioners skilled in these areas can explore behaviours of real-world systems that would be impractical or impossible to undertake using only theoretical or experimental means. In this introductory unit, you will develop your computation and simulation skills through individual and collaborative problem-solving activities. Depending on your course, further exploration may be available through a minor in this field.
Learning Outcomes
On successful completion of this unit you will be able to:
- Demonstrate knowledge of a range of data formats, and the techniques to manipulate such data.
- Use programming skills to implement computational solutions to problems using MATLAB.
- Apply problem solving and critical thinking skills in adapting, extending and synthesising learned techniques to solve real world problems.
- Collaborate and use effective teamwork skills in a multidisciplinary team environment.
Content
Introduction to the multipdisciplinary field of Computational Science. MATLAB as a tool for data manipulation, input/output and visualisation. Programming fundamentals as implemented in MATLAB. Analysis of spatial data including latitude/longitude and x-y coordinate data. Image formats and data types, image manipulation and processing, animations, input/output of images and animations. Sound formats and data types, sound manipulation and processing, time/frequency analysis of signals, input/output of sounds. Random walk simulations, 1D and 2D lattice-based, extensions and applications including diffusion. Cellular automata, 1D and 2D grids, Conway's Game of Life
Learning Approaches
This unit is available for you to study in either on-campus or online mode. You can expect to spend 10 - 15 hours per week involved in preparing for and attending scheduled classes, preparing and completing assessment tasks as well as independent study and consolidation of your learning.
This unit engages you in your learning through a theory-to-practice approach, and is by its design a multi-disciplinary learning experience. The unit is taught in the form of weekly topics, with the delivery of topic content followed by opportunities to complete practical exercises to develop your programming and problem-solving skills in MATLAB, both individually and in group work.
Each week comprises an online interactive lecture which will introduce the application area along with any new required programming techniques, a computer-based individual practical where you hone the knowledge and skills, and a computer-based group practical where you will engage in collaborative activity with your peers in the form of multi-disciplinary groups. Your weekly progress in MATLAB programming competency and understanding of weekly topic content will be supported by formative online quizzes embedded in each weekly worksheet.
As a first year unit, your learning will be carefully guided and scaffolded by the teaching staff, but you will be expected to develop some self-directed learning capabilities to facilitate your transition from dependent to independent learner. Additional free support relating to this unit is available through the STIMulate peer program.
Feedback on Learning and Assessment
Formative feedback will be provided through online quizzes and though weekly written feedback provided on group worksheets.
Summative feedback will be provided throughout the semester with results posted via Canvas.
Assessment
Overview
The assessment items in this unit are designed to determine your level of competency in meeting the unit learning outcomes while providing you with a range of tasks with varying levels of skill development and difficulty.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Problem Solving Task
You will be required to solve specific coding problems assigned to you to determine your level of competency in MATLAB programming and to measure your understanding the of weekly topic content. These coding problems will be of a similar nature to the online formative quizzes embedded in each weekly module. Your submission will take the form of short MATLAB code files, which will be marked for their adherence to the requirements set out in the specific question.
The ethical and responsible use of generative artificial intelligence (GenAI) tools is authorised in this assessment. See the relevant assessment details in Canvas for specific guidelines.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Portfolio
The portfolio comprises the work undertaken by your group in the weekly group practical classes. These consist of your refined computational solutions to the real-world applications introduced in the weekly group worksheets. Throughout the semester you will receive formative feedback on your group's initial attempt at the worksheet solutions, and will have until the end of semester to act upon this feedback for the final, summative submission.
Furthermore, in the final weeks of semester, your group will develop a computational solution to a novel application of your choosing to also include in the portfolio. In all, your portfolio will comprise MATLAB Live Script files that present your group’s solution to the provided application questions, as well as the novel application of your group’s choosing.
The ethical and responsible use of generative artificial intelligence (GenAI) tools is authorised in this assessment. See the relevant assessment details in Canvas for specific guidelines.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Examination (invigilated)
A written examination that requires you to demonstrate programming knowledge and computational problem solving skills, drawing on all material covered throughout the semester.
The use of generative artificial intelligence (GenAI) tools is prohibited during this assessment.
The examination will require attendance at a local testing centre. For students enrolled as internal or on-campus, the local testing centre will be on QUT campus. For students enrolled as online, QUT Examinations will provide local testing centre information.
Academic Integrity
Academic integrity is a commitment to undertaking academic work and assessment in a manner that is ethical, fair, honest, respectful and accountable.
The Academic Integrity Policy sets out the range of conduct that can be a failure to maintain the standards of academic integrity. This includes, cheating in exams, plagiarism, self-plagiarism, collusion and contract cheating. It also includes providing fraudulent or altered documentation in support of an academic concession application, for example an assignment extension or a deferred exam.
You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.
Breaching QUT’s Academic Integrity Policy or engaging in conduct that may defeat or compromise the purpose of assessment can lead to a finding of student misconduct (Code of Conduct – Student) and result in the imposition of penalties under the Management of Student Misconduct Policy, ranging from a grade reduction to exclusion from QUT.
Resources
All learning materials for this unit will be made available in the Canvas unit site. There is no set text for this unit.
Risk Assessment Statement
There is minimal health and safety risk in this unit. It is your responsibility to familiarise yourself with the Health and Safety policies and procedures applicable within campus areas and laboratories.
Standards/Competencies
This unit is designed to support your development of the following standards\competencies.
Engineers Australia Stage 1 Competency Standard for Professional Engineer
1: Knowledge and Skill Base
Relates to: Problem Solving Task, Portfolio, Examination (invigilated)
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.DS01 Bachelor of Data Science
- Demonstrate a broad and coherent knowledge of the principles, concepts and techniques of the data science discipline, with depth of knowledge in at least one area developed through a major.
Relates to: ULO4, Problem Solving Task, Portfolio, Examination (invigilated) - Use appropriate statistical, computational, modelling, data management, programming and generative artificial intelligence techniques to develop solutions for deriving insights from data.
Relates to: ULO4, Problem Solving Task, Portfolio, Examination (invigilated) - Demonstrate critical thinking and problem-solving skills, as well as adaptivity in applying learned techniques in new and unfamiliar contexts.
Relates to: ULO4, Problem Solving Task, Portfolio, Examination (invigilated) - Work effectively both independently and collaboratively in diverse and interdisciplinary teams.
Relates to: Portfolio
EN01 Bachelor of Engineering (Honours)
- Display leadership, creativity, and initiative in both self-directed and collaborative contexts of professional engineering practice.
Relates to: ULO4, Portfolio - Manage projects to solve complex engineering problems, using appropriate information, engineering methods, and technologies.
Relates to: ULO3, Problem Solving Task, Portfolio, Examination (invigilated) - Demonstrate coherent knowledge and skills of physical, mathematical, statistical, computer, and information sciences that are fundamental to professional engineering practice.
Relates to: ULO1, ULO2, Problem Solving Task, Portfolio, Examination (invigilated)
EN29 Bachelor of Engineering Studies
- Evidence of displaying leadership, creativity, and initiative in both self-directed and collaborative contexts of professional engineering practice.
Relates to: ULO4, Portfolio - Evidence of being able to manage projects to solve some engineering problems, using appropriate information, engineering methods and technologies.
Relates to: ULO3, Problem Solving Task, Portfolio, Examination (invigilated) - Evidence of engaging with and applying regulatory requirements relating to safety, risk management and sustainability in professional engineering practice.
Relates to: ULO1 - Evidence of demonstrating coherent knowledge and skills of physical, mathematical, statistical, computer and information science.
Relates to: ULO2, Problem Solving Task, Portfolio, Examination (invigilated)
EV01 Bachelor of Engineering (Honours)
- Display leadership, creativity, and initiative in both self-directed and collaborative contexts of professional engineering practice.
Relates to: Portfolio - Manage projects to solve complex engineering problems, using appropriate information, engineering methods, and technologies.
Relates to: Problem Solving Task, Portfolio, Examination (invigilated) - Demonstrate coherent knowledge and skills of physical, mathematical, statistical, computer, and information sciences that are fundamental to professional engineering practice.
Relates to: Problem Solving Task, Portfolio, Examination (invigilated)
MS01 Bachelor of Mathematics
- Formulate and model problems in mathematical terms and apply appropriate mathematical, statistical and computational techniques to solve practical and abstract problems.
Relates to: Problem Solving Task, Portfolio, Examination (invigilated) - Demonstrate aptitude in computer programming, and familiarity with industry-leading programming languages and relevant specialised mathematical, statistical and generative artificial intelligence software and tools.
Relates to: Problem Solving Task, Portfolio, Examination (invigilated) - Demonstrate critical thinking and problem solving skills across a range of applied mathematical and statistical contexts, and adaptivity in applying learned techniques in new or unfamiliar contexts.
Relates to: Problem Solving Task, Portfolio, Examination (invigilated) - Work both independently and collaboratively in diverse teams, including cross-cultural and cross-disciplinary teams.
Relates to: Portfolio
MV01 Bachelor of Mathematics
- Formulate and model problems in mathematical terms and apply appropriate mathematical, statistical and computational techniques to solve practical and abstract problems.
Relates to: Problem Solving Task, Portfolio, Examination (invigilated) - Demonstrate aptitude in computer programming, and familiarity with industry-leading programming languages and relevant specialised mathematical, statistical and generative artificial intelligence software and tools.
Relates to: Problem Solving Task, Portfolio, Examination (invigilated) - Demonstrate critical thinking and problem solving skills across a range of applied mathematical and statistical contexts, and adaptivity in applying learned techniques in new or unfamiliar contexts.
Relates to: Problem Solving Task, Portfolio, Examination (invigilated) - Work both independently and collaboratively in diverse teams, including cross-cultural and cross-disciplinary teams.
Relates to: Portfolio
Unit Outline: Semester 2 2026, Online
| Unit code: | MXB161 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | Admission to (MS01 or MV01 or MV05 or MV06 or DS01 or IX30 or SE20 or SE30 or SE40 or SE70 or ST20 or SE07 or ID29) or 48 credit points of study in current course |
Overview
This unit introduces you to techniques of computation and simulation across a range of application areas in Science, Technology, Engineering and Mathematics (STEM). Computation and simulation are cornerstones of modern practice across STEM; practitioners skilled in these areas can explore behaviours of real-world systems that would be impractical or impossible to undertake using only theoretical or experimental means. In this introductory unit, you will develop your computation and simulation skills through individual and collaborative problem-solving activities. Depending on your course, further exploration may be available through a minor in this field.
Learning Outcomes
On successful completion of this unit you will be able to:
- Demonstrate knowledge of a range of data formats, and the techniques to manipulate such data.
- Use programming skills to implement computational solutions to problems using MATLAB.
- Apply problem solving and critical thinking skills in adapting, extending and synthesising learned techniques to solve real world problems.
- Collaborate and use effective teamwork skills in a multidisciplinary team environment.
Content
Introduction to the multipdisciplinary field of Computational Science. MATLAB as a tool for data manipulation, input/output and visualisation. Programming fundamentals as implemented in MATLAB. Analysis of spatial data including latitude/longitude and x-y coordinate data. Image formats and data types, image manipulation and processing, animations, input/output of images and animations. Sound formats and data types, sound manipulation and processing, time/frequency analysis of signals, input/output of sounds. Random walk simulations, 1D and 2D lattice-based, extensions and applications including diffusion. Cellular automata, 1D and 2D grids, Conway's Game of Life
Learning Approaches
This unit is available for you to study in either on-campus or online mode. You can expect to spend 10 - 15 hours per week involved in preparing for and attending scheduled classes, preparing and completing assessment tasks as well as independent study and consolidation of your learning.
This unit engages you in your learning through a theory-to-practice approach, and is by its design a multi-disciplinary learning experience. The unit is taught in the form of weekly topics, with the delivery of topic content followed by opportunities to complete practical exercises to develop your programming and problem-solving skills in MATLAB, both individually and in group work.
Each week comprises an online interactive lecture which will introduce the application area along with any new required programming techniques, a computer-based individual practical where you hone the knowledge and skills, and a computer-based group practical where you will engage in collaborative activity with your peers in the form of multi-disciplinary groups. Your weekly progress in MATLAB programming competency and understanding of weekly topic content will be supported by formative online quizzes embedded in each weekly worksheet.
As a first year unit, your learning will be carefully guided and scaffolded by the teaching staff, but you will be expected to develop some self-directed learning capabilities to facilitate your transition from dependent to independent learner. Additional free support relating to this unit is available through the STIMulate peer program.
Feedback on Learning and Assessment
Formative feedback will be provided through online quizzes and though weekly written feedback provided on group worksheets.
Summative feedback will be provided throughout the semester with results posted via Canvas.
Assessment
Overview
The assessment items in this unit are designed to determine your level of competency in meeting the unit learning outcomes while providing you with a range of tasks with varying levels of skill development and difficulty.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Problem Solving Task
You will be required to solve specific coding problems assigned to you to determine your level of competency in MATLAB programming and to measure your understanding the of weekly topic content. These coding problems will be of a similar nature to the online formative quizzes embedded in each weekly module. Your submission will take the form of short MATLAB code files, which will be marked for their adherence to the requirements set out in the specific question.
The ethical and responsible use of generative artificial intelligence (GenAI) tools is authorised in this assessment. See the relevant assessment details in Canvas for specific guidelines.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Portfolio
The portfolio comprises the work undertaken by your group in the weekly group practical classes. These consist of your refined computational solutions to the real-world applications introduced in the weekly group worksheets. Throughout the semester you will receive formative feedback on your group's initial attempt at the worksheet solutions, and will have until the end of semester to act upon this feedback for the final, summative submission.
Furthermore, in the final weeks of semester, your group will develop a computational solution to a novel application of your choosing to also include in the portfolio. In all, your portfolio will comprise MATLAB Live Script files that present your group’s solution to the provided application questions, as well as the novel application of your group’s choosing.
The ethical and responsible use of generative artificial intelligence (GenAI) tools is authorised in this assessment. See the relevant assessment details in Canvas for specific guidelines.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Examination (invigilated)
A written examination that requires you to demonstrate programming knowledge and computational problem solving skills, drawing on all material covered throughout the semester.
The use of generative artificial intelligence (GenAI) tools is prohibited during this assessment.
The examination will require attendance at a local testing centre. For students enrolled as internal or on-campus, the local testing centre will be on QUT campus. For students enrolled as online, QUT Examinations will provide local testing centre information.
Academic Integrity
Academic integrity is a commitment to undertaking academic work and assessment in a manner that is ethical, fair, honest, respectful and accountable.
The Academic Integrity Policy sets out the range of conduct that can be a failure to maintain the standards of academic integrity. This includes, cheating in exams, plagiarism, self-plagiarism, collusion and contract cheating. It also includes providing fraudulent or altered documentation in support of an academic concession application, for example an assignment extension or a deferred exam.
You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.
Breaching QUT’s Academic Integrity Policy or engaging in conduct that may defeat or compromise the purpose of assessment can lead to a finding of student misconduct (Code of Conduct – Student) and result in the imposition of penalties under the Management of Student Misconduct Policy, ranging from a grade reduction to exclusion from QUT.
Resources
All learning materials for this unit will be made available in the Canvas unit site. There is no set text for this unit.
Risk Assessment Statement
There is minimal health and safety risk in this unit. It is your responsibility to familiarise yourself with the Health and Safety policies and procedures applicable within campus areas and laboratories.
Standards/Competencies
This unit is designed to support your development of the following standards\competencies.
Engineers Australia Stage 1 Competency Standard for Professional Engineer
1: Knowledge and Skill Base
Relates to: Problem Solving Task, Portfolio, Examination (invigilated)
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.DS01 Bachelor of Data Science
- Demonstrate a broad and coherent knowledge of the principles, concepts and techniques of the data science discipline, with depth of knowledge in at least one area developed through a major.
Relates to: ULO4, Problem Solving Task, Portfolio, Examination (invigilated) - Use appropriate statistical, computational, modelling, data management, programming and generative artificial intelligence techniques to develop solutions for deriving insights from data.
Relates to: ULO4, Problem Solving Task, Portfolio, Examination (invigilated) - Demonstrate critical thinking and problem-solving skills, as well as adaptivity in applying learned techniques in new and unfamiliar contexts.
Relates to: ULO4, Problem Solving Task, Portfolio, Examination (invigilated) - Work effectively both independently and collaboratively in diverse and interdisciplinary teams.
Relates to: Portfolio
EN01 Bachelor of Engineering (Honours)
- Display leadership, creativity, and initiative in both self-directed and collaborative contexts of professional engineering practice.
Relates to: ULO4, Portfolio - Manage projects to solve complex engineering problems, using appropriate information, engineering methods, and technologies.
Relates to: ULO3, Problem Solving Task, Portfolio, Examination (invigilated) - Demonstrate coherent knowledge and skills of physical, mathematical, statistical, computer, and information sciences that are fundamental to professional engineering practice.
Relates to: ULO1, ULO2, Problem Solving Task, Portfolio, Examination (invigilated)
EN29 Bachelor of Engineering Studies
- Evidence of displaying leadership, creativity, and initiative in both self-directed and collaborative contexts of professional engineering practice.
Relates to: ULO4, Portfolio - Evidence of being able to manage projects to solve some engineering problems, using appropriate information, engineering methods and technologies.
Relates to: ULO3, Problem Solving Task, Portfolio, Examination (invigilated) - Evidence of engaging with and applying regulatory requirements relating to safety, risk management and sustainability in professional engineering practice.
Relates to: ULO1 - Evidence of demonstrating coherent knowledge and skills of physical, mathematical, statistical, computer and information science.
Relates to: ULO2, Problem Solving Task, Portfolio, Examination (invigilated)
EV01 Bachelor of Engineering (Honours)
- Display leadership, creativity, and initiative in both self-directed and collaborative contexts of professional engineering practice.
Relates to: Portfolio - Manage projects to solve complex engineering problems, using appropriate information, engineering methods, and technologies.
Relates to: Problem Solving Task, Portfolio, Examination (invigilated) - Demonstrate coherent knowledge and skills of physical, mathematical, statistical, computer, and information sciences that are fundamental to professional engineering practice.
Relates to: Problem Solving Task, Portfolio, Examination (invigilated)
MS01 Bachelor of Mathematics
- Formulate and model problems in mathematical terms and apply appropriate mathematical, statistical and computational techniques to solve practical and abstract problems.
Relates to: Problem Solving Task, Portfolio, Examination (invigilated) - Demonstrate aptitude in computer programming, and familiarity with industry-leading programming languages and relevant specialised mathematical, statistical and generative artificial intelligence software and tools.
Relates to: Problem Solving Task, Portfolio, Examination (invigilated) - Demonstrate critical thinking and problem solving skills across a range of applied mathematical and statistical contexts, and adaptivity in applying learned techniques in new or unfamiliar contexts.
Relates to: Problem Solving Task, Portfolio, Examination (invigilated) - Work both independently and collaboratively in diverse teams, including cross-cultural and cross-disciplinary teams.
Relates to: Portfolio
MV01 Bachelor of Mathematics
- Formulate and model problems in mathematical terms and apply appropriate mathematical, statistical and computational techniques to solve practical and abstract problems.
Relates to: Problem Solving Task, Portfolio, Examination (invigilated) - Demonstrate aptitude in computer programming, and familiarity with industry-leading programming languages and relevant specialised mathematical, statistical and generative artificial intelligence software and tools.
Relates to: Problem Solving Task, Portfolio, Examination (invigilated) - Demonstrate critical thinking and problem solving skills across a range of applied mathematical and statistical contexts, and adaptivity in applying learned techniques in new or unfamiliar contexts.
Relates to: Problem Solving Task, Portfolio, Examination (invigilated) - Work both independently and collaboratively in diverse teams, including cross-cultural and cross-disciplinary teams.
Relates to: Portfolio