The proposed Bachelor of Software Engineering (Artificial Intelligence) (AI) course is part of a suite of courses offering software engineering knowledge and skills with four specialisations: Artificial Intelligence, Cloud Computing, Network & Cybersecurity and Blockchain. The structure of these courses consists of four main areas of study: Software Engineering Core, Creative Technology and Specialisation subjects, as well as Electives. Thirteen subjects of the course will be common across the four specialisations.
Software Engineering Core subjects provide the basis of software engineering knowledge and skills; their difference lies in the professional software engineering requirements developed over the last 40 years. Software Engineering Core subjects distinguish themselves by providing fundamental knowledge and skills required in the software development industry and as stipulated requirements for professional accreditation by the Australian Computer Society. These subjects provide a strong foundation in analysis, design and development skills essential in AI applications progressively strengthened every term. On the other hand, Creative Technology subjects include highly desired industry content such as Human Centered Design and Project-Based Learning (PBL) style subjects, augmenting career attributes by empowering graduates to better navigate contemporary professional software engineering AI environments and improve career outcomes. Specialisation subjects make up the bulk of AI specific learning deepening understanding whilst electives provide students with choices to broaden their skills and knowledge. Elective spaces are interspersed throughout the course at level 100, 200 and 300. The elective bank subjects are assembled from a number of Torrens University undergraduate courses including design, games and business, providing opportunities to expand on the learning.
Twenty-one subjects represent 10 credit points whilst the last subject represents 30 credit points involving work-integrated learning, culminating in a major production of work. All subjects are available in Online, Face-to-Face and blended mode, providing ample study opportunity to serve the needs of students. Assessments are varied and include interspersed examinations, projects, reports, tests and collaborations as well as industry immersion in the final project. They are all clearly mapped to the learning outcomes.
Rationale: The Bachelor of Software Engineering, Artificial Intelligence (AI) is a qualification addressing industry demand for developers with skills that encompass machine learning, computer vision and natural language processing and speech recognition. Although the course is founded on strong traditional software engineering skills such as programming, maths and statistical knowledge it also embeds Ethics, Social Responsibility and Cognitive Psychology throughout the learning, adding highly desired skills to the profession of software engineering, which are increasingly the mainstay in the profession. In addition, machine learning, computer vision, natural language processing and speech recognition are subsets to the skills and knowledge development empowering graduates to enter confidently into professions relevant to an increasingly computerised automated and machine-like world whilst operating a variety of expert computer systems.
Due to the approach of the course, founded on extensive industry consultations, students are equipped with a mixture of creative application skills, cognitive and technical abilities. They can consolidate and synthesise artificial intelligence software engineering problems, in particular artificial intelligence, with contemporary solutions based on the vital understanding of the ethics, social responsibility and the psychological impacts. Embedded collaboration, negotiation and research skills lead to graduates that are accustomed to professional environments where teamwork and leadership ensure prosperous career trajectories.
The Bachelor of Software Engineering, Artificial Intelligence is developed to address the needs of a rapidly evolving industry. Graduates will have a combination of skills and knowledge additional to software engineering fundamentals enabling them to work in areas across the broad spectrum of the field of Artificial Intelligence. The specialised topics in AI are a branch of artificial intelligence based on the idea that systems can learn from data to identify patterns and make decisions with minimal human intervention. AI is used as a confluence in Natural Language Processing (with linguistics), speech recognition (also linguistics), computer vision (graphics & image processing), data mining and visualisation (graphics & image processing). All these applications require aptitude in research leading to a comprehensive understanding of the ethical implications of AI in a social context, emerging technologies and their impact on society. The course learning outcomes will ensure that graduates can participate and sometimes lead teams and manage traditional as well as creative projects in AI.
Course Learning Outcomes
Students will be able to:
- CLO 1 – Demonstrate an awareness of the software engineering body of knowledge with a view towards ethics of practice in a global and sustainable context.
- CLO 2 – Apply creative skills to identify and solve complex commercial software engineering problems innovatively with independence.
- CLO 3 – Master cognitive and technical skills required to review, analyse, consolidate and synthesise knowledge in the domain of artificial intelligence.
- CLO 4 – Illustrate knowledge of research principles, critical thinking and technical skills to evaluate and synthesise specialist software engineering information projects.
- CLO 5 – Apply coherent and advanced knowledge from the domain of artificial intelligence in diverse contexts including social impact, emerging technologies and entrepreneurship.
- CLO 6 – Select and apply abstraction, mathematics and software engineering fundamentals to the analysis, design and operation of a model using appropriate methods and tools.
- CLO 7 – Demonstrate appropriate leadership within a team through contributing/leading the planning, preparation, management, organisation and composition using an effective project management strategy while demonstrating effective communication, collaboration and negotiation skills.
- CLO 8 – Communicate proficiently in the context of software engineering across a variety of audiences utilising appropriate tools and interfaces.
- CLO 9 – Demonstrate effective leadership skills required in the technology sector that tackle societal problems and lead to positive change.
Graduate employment opportunities:
The Bachelor of Software Engineering (Artificial Intelligence) provides graduates with the capability to seek junior level employment in either generalist or niche roles found within the software development industry. The specialisation of artificial intelligence gives them a competitive advantage in what they can provide to an employer or client:
- Assistant Software Engineer
- Associate Software Engineer
- Junior Software Developer
- Junior Data Scientist
- Business Intelligence Developer
- Junior Machine Learning Engineer
- Junior Computer Vision Engineer
- Software Engineer
- Software Developer
|Qualification Title||BACHELOR OF SOFTWARE ENGINEERING (ARTIFICIAL INTELLIGENCE)|
|Study Options – Domestic Australian students||Full-time Blended*
*Blended – face to face on campus plus facilitated online
|Study Options – International students||Full-time Blended*
*Blended – face to face on campus plus facilitated online (no more than 25% online)
|Start Dates||February, June, September
For specific dates visit the website
|Course Length||Full-time: 3 years
Accelerated: 2 years
Part-time: 6 years maximum
|Payment Options – Domestic Australian students||Upfront payment
This means tuition fees will be invoiced each trimester and payment is required on or before the due date.
FEE-HELP is Australian Government’s loan scheme for higher education degree courses.
Further information within this Course Information Sheet
It can assist you in paying for all, or part of, your course fees. Repayments commence via the tax system once your income rises above a minimum threshold. Just like with any other debt, a FEE-HELP debt is a real debt that impacts your credit rating.
|Payment Options – International students||Upfront payment
This means tuition fees will be invoiced each trimester and payment is required on or before the due date.
Further information within this Course Information Sheet
|Course study requirements||Each subject involves 10 hours of study per week, comprising 3 hours of facilitated study and 7 hours self-directed study.||Assessment||Project/Application/Research Proposal, Process/Research Documentation, Application Outcome, Reflective Journal/Blog, Report/Essay, Presentation/Pitch, Examinations/Tests/Quizzes, Research, Collaboration, Individual self-directed major project, Work integrated learning project work, Software development for social enterprise|
|Delivered by||Media Design School at Torrens University Australia|
|Provider||Torrens University Australia Ltd is registered as a self-accrediting Australian university by the Tertiary Education Quality and Standards Agency (TEQSA).||CRICOS Course Code||No CRICOS|
|Provider obligations||Torrens University is responsible for all aspects of the student experience, including the quality of course delivery, in compliance with the Higher Education Standards 2015||Accrediting body||Torrens University Australia Ltd|
|Course Fees||For details, refer to the website.||Any other fees||For details, refer to the website.|
Essential requirements for admission: No additional requirements
The table below gives an indication of the likely peer cohort for new students in this course. It provides data on students who commenced in this course in the most relevant recent intake period, including those admitted through all offer rounds and international students studying in Australia.
|Applicant background||Trimester one / Full year intake |
|Number of students||Percentage of all students|
|(A) Higher education study
(includes a bridging or enabling course)
|(B) Vocational education and training (VET) study||1||20%|
|(C) Work and life experience
(Admitted on the basis of previous achievement not in the other three categories)
|(D) Recent secondary education:
Notes: “<5” – the number of students is less than 5.
N/A – Students not accepted in this category.
N/P – Not published: the number is hidden to prevent calculation of numbers in cells with less than 5 students.
|Applicants with higher education study||
|Applicants with vocational education and training (VET) study||
|Applicants with work and life experience||Demonstrated ability to undertake study at the required level:
|Applicants with recent secondary education (within the past two years) with ATAR or equivalent
(for applicants who will be selected wholly or partly on the basis of ATAR)
|Minimum ATAR required for consideration: 60|
|English Language Proficiency
(applicable to international students, and in addition to academic or special entry requirements noted above)
|Equivalent IELTS 6.0 (Academic) with no skills band less than 5.5|
ATAR profile for those offered places wholly or partly on the basis of ATAR in [T1 2019]
|(ATAR-based offers only, across all offer rounds)
[Note: this table relates to all students made an offer on the basis of ATAR alone or ATAR in combination with other factors. To ensure comparability across all providers, the “ATAR” (or OP) figures used must reflect the original unadjusted figures without the consideration of equity or other adjustments. “Selection Rank” figures (if used) will reflect the same cohort but including the consideration of adjustment factors.
Students selected on the basis of special consideration or otherwise not on the basis of their ATAR should not be included in this table.]
|ATAR (OP in QLD)
(Excluding adjustment factors) *
[NB: Raw ATAR profile for all students offered a place wholly or partly on the basis of ATAR]
|Highest rank to receive an offer||N/A|
|Median rank to receive an offer||N/A|
|Lowest rank to receive an offer||N/A|
Notes: * L/N – indicates low numbers if less than 5 ATAR-based offers made
Other admission options
(For applicants who will be selected on a basis other than ATAR)
Applicants in any category whose study, work or life experiences have been impacted by disability, illness or family disruption will be given special consideration for admission. Each application will be considered on its merit, based on the evidence supplied by the applicant attesting to the circumstances of the applicant. Applicants for special entry may need to complete written or numerical tasks to assist with assessing eligibility for admission.
How to apply
- Through a TAC – http://www.uac.edu.au/
- Via direct application to the institution
Advanced standing/academic credit/recognition of prior learning (RPL)
You may be entitled to credit for prior learning, whether formal or informal. Formal learning can include previous study in higher education, vocational education, or adult and community education. Informal learning can include on the job learning or various kinds of work and life experience. Credit can reduce the amount of study needed to complete a degree.
Applicants admitted based on prior higher education study may be eligible for Advanced Standing in the form of credit and/or recognition of prior learning (RPL) under the Torrens University Australia Credit Policy.
- Students with completed subjects may be eligible for specified credit and/or elective exemptions
- Students who have completed a qualification at AQF level 5 (diploma) or above may be eligible for block credit (where a block credit agreement exists)
- Students with a mix of formal study and informal and/or non-formal learning may be eligible for recognition of prior learning in addition to any credit approved.
Credit will not be applied automatically. Applicants must apply for credit and/or RPL as early as possible prior to each study period, with applications not accepted after week 2.
For further information about credit and recognition of prior learning please see /apply-online/course-credits.
Where to get further information
Course Structure and Course Rules
|The course structure comprises 19 core subjects and 3 elective subjects over Levels 100, 200, and 300, as follows:|
|Level 100||6 core +||1 elective|
|Level 200||8 core +||1 elective|
|Level 300||5 core +||1 elective|
To be awarded the Bachelor of Software Engineering (Artificial Intelligence), students must complete 240 credit points over 22 subjects as outlined in the course structure above. Each subject has a value of 10 credit points, with one subject having a value of 30 credit points (ATW306 – Advanced Technology Work Integrated Learning).
|Bachelor of Software Engineering (Artificial Intelligence)|
|The Bachelor of Software Engineering (Artificial Intelligence) is three years in duration for a full-time student, or six years duration for a part-time student. Each year consists of three Study Periods, also known as Trimesters.
Core subjects – compulsory subjects that you must complete
Elective subject – subject you can choose from the Elective Bank below
Prerequisite subject – a subject you must complete before undertaking another subject
RPL – Should you have any Recognition of Prior Learning (RPL) credits that make you eligible for exemptions, please fill in an Application for Course Credit form.
Please note: Not all subjects run each Study Period. If your suggested subjects are unavailable, we encourage you to take the immediate preceding or following subject(s) where possible.
|How to read the below Suggested Study Pattern (as a Full-Time Student):
8 subjects per year make up a full time study load, following a 3 subjects-3 subjects-2 subjects pattern across the year’s three Study Periods (Trimesters).
As an example: Following the below pattern, the subjects in your first year would be:
Your first Study Period: MAT101, ISE102 and CAI104 (3 subjects)
Your second Study Period: Elective, ADS103 and MSA106 (3 subjects)
Your third Study Period: PST107 and ICG202 (2 subjects)
Studying Part Time? You would still follow the below sequence from top to bottom, but with fewer subjects per Study Period. Any questions? Contact email@example.com
Suggested Study Pattern
|Year||Level||Type||Subject Code||Subject Name||Prerequisite||Completed|
|Year 1||Study Period 1|
|100||Core||ISE102||Introduction to Software Engineering|
|100||Core||CAI104||Concepts in Artificial Intelligence|
|Study Period 2|
|100||Core||ADS103||Algorithms & Data Structures|
|Study Period 3|
|100||Core||PST107||Probabilities & Statistics|
|200||Core||ICG202||Introduction to Computer Graphics||MAT101, ISE102, ADS103|
|Year 2||Study Period 4|
|200||Core||IDS201||Introduction to Data Science|
|200||Core||AAI202||Applications of Artificial Intelligence||CAI104|
|200||Core||NDS203||Networking & Database Systems||MAT101, ISE102, ADS103|
|Study Period 5|
|200||Core||CLR204||Classification & Regression||PST107|
|200||Core||PBT205||Project Based Learning Studio: Technology|
|Study Period 6|
|200||Core||HCD206||Human Centred Design|
|Year 3||Study Period 7|
|300||Core||MLP301||Machine Learning Principles||CRL204|
|300||Core||DMV302||Data Mining & Visualisation|
|Study Period 8|
|300||Core||NLP303||Natural Language Processing & Speech Recognition||MP301|
|Study Period 9|
|300||Core||ATW306||Advanced Technology – Work Integrated Learning|
|Please note – not all subjects are available for each Study Period. If your suggested subjects are unavailable, please take the subject that is immediately preceding or following that subject|
Electives available to students may be chosen from the elective bank below. One elective must be taken at each level. Students may study an elective that is not in the list above, pending Program Director approval.
|Subject Code||Subject Name||Prerequisite|
|ICC104||Introduction to Cloud Computing||–|
|IDO107||Introduction to DevOps||–|
|BIZ104||Customer Experience Management||–|
|GDP102||Game Design Principles||–|
|GPF104||Game Production Foundation||–|
|COU103A||Human Development Across the Lifespan||–|
|SOC102A||Understanding Societies: An Introduction to Social Analysis||–|
|COMR2008||Principles of Accounting||–|
|ECON2002||Principles of Economics||–|
|FINA2006||Principles of Finance||–|
|CDT200A||Design Thinking 1||–|
|DID200A||Interface Development 1||DIG103A (prerequisite)|
|ACCT2000||Financial Accounting||COMR2008 (prerequisite)|
|CDC200A||Message, Meaning, Media||–|
|COMR2002||Business Information Systems||–|
|LAW201A||Business and Law||–|
|CDC301A||Business by Design||–|
|MKT301A||Marketing Strategy||MKT101A (prerequisite)|
|BIZ301||Organisational Creativity and Innovation||–|
|MGT301A||Ethics and Sustainability||–|
|CDC300A||Culture of Change||–|
|MKT304A||Brand and Product Management||–|
|WEL303A||Human Rights and Social Advocacy||–|
|CDT301A||Inspiration to Implementation||–|
|Subject Code||Subject Title||Subject Descriptor|
|MAT101||Maths 1||This subject introduces students to foundational mathematical concepts necessary for specialisation subjects in their degree. Main topics covered are – Linear Algebra, Discrete Maths and Geometry. The delivery consists of theoretical elements, a demonstration, and then the lecturers allow students to put these skills into practice. The students collaborate and share mathematical problem-solving approaches during frequent in-class discussions and are expected to provide these solutions for class reviews.|
|ISE102||Introduction to Software Engineering||This subject provides an introduction to the information and skills needed to begin working in software engineering. This subject will cover the concepts of object-oriented programming with a particular focus on learning to use the C++ programming language. An understanding of C++ will form the basis of the necessary skills needed for developing professional and complex software packages such as video games.|
|CAI104||Concepts in Artificial Intelligence||The goal of this subject is to familiarise the student with the basic concepts of artificial intelligence and the problems AI is used to solve. The course content is organised around the three main areas of AI: Search, Logic and Learning. Topics covered include basic search, heuristic search, adversarial search, constraint satisfaction, logical agents, logic and inference, knowledge representation, probabilistic reasoning, knowledge in learning, learning probabilistic models, reinforcement learning and ethics of AI.|
|ADS103||Algorithms & Data Structures||Students learn the fundamental data structures and algorithms that are needed to solve common software engineering problems. Students improve their learning throughout this subject by working on a large number of projects. They solve common problems by designing, developing, implementing, testing, and enhancing a collection of data structures and algorithms.|
|ICG202||Introduction to Computer Graphics||Students are introduced to the fundamental topics of core computer graphics, 3D graphics programming and the rendering pipeline. Topics included are transformation pipeline, device states, primitive rendering, basic camera systems, lighting, texturing, alpha techniques as well as software engineering design principles and testing strategies. By the end of the subject, students create a project utilizing low-level 3D graphics concepts as introduced in the class.|
|MSA106||Microservices Architecture||In this subject students learn the fundamentals and core concepts of Service Oriented Architecture and characteristics of microservices. They compare microservice architecture with monolithic style, emphasising why the former is better for continuous delivery. They also deal with operational complexities that are created while managing, monitoring, logging and updating microservices, and learn about the tools used to successfully manage, deploy and monitor applications based on microservice.|
|PST107||Probabilities & Statistics||This subject provides an elementary introduction to probability and statistics with applications.
In probability, students will learn about probability and distribution theory by defining probability and then studying its key properties. The subject will also introduce concepts of random variables, outcomes of random experiments and data analysis techniques using the statistical computing package R or SPSS.
In statistics, students will study data and uncertainty. Students will learn how to use statistics in the design of effective experiments and in determining the type of data collected. Underlying these techniques is the assumption that these data are samples of a random variable that follows a probability distribution describing their behaviour.
|IDS201||Introduction to Data Science||The aim of this subject is to provide students with fundamental knowledge of data, questions, and tools that a data scientist deals with. Students will not only be introduced to the ideas behind turning data into information but will also be introduced to the data scientist’s toolbox. Topics include: data scientist skills and responsibilities in a business including planning, performing and presenting projects; data science code of ethics; data manipulation tools and techniques.|
|AAI202||Applications of Artificial Intelligence||This subject builds on the skills and knowledge students acquired from Concepts of Artificial Intelligence (AI). The subject begins by exploring different classifications of AI (e.g. Expert Systems, Planning and Robotics, Natural Language Processing (NLP) and Speech Recognition, Machine Learning, and Computer Vision) and their current applications. Students will be presented with case studies focusing on the overview of the development of NLP, Speech Recognition and Computer Vision (most commonly used applications of AI and Machine Learning). This subject also covers the AI for Good movement and how AI is being used to address economic and socially relevant problems.|
|NDS203||Networking & Database Systems||This subject introduces students to core concepts of Networking and Database Systems. Students learn fundamentals of Database Management Systems and network topology including network architecture. They are introduced to relational database models and learn fundamentals of structured query language (SQL). Students will apply these concepts through completing multiple software engineering projects.|
|CLR204||Classification & Regression||This subject introduces students to the statistical models for regression and classification necessary for more specialised subjects in this degree. The main topics covered are Classification Algorithms and Regression Algorithms; the practical use of both methods, how to evaluate the proposed models and how to choose between the different available methods.
Theoretical lectures about the main concepts to be studied are followed by demonstrations of the different applications. Then the students are asked to apply the learned concepts on different classification and regression problems.
|PBT205||Project Based Learning Studio: Technology||This subject provides students with an opportunity to work collaboratively on a series of projects, enhancing skills such as project management, time management, prioritisation, resilience and a gamut of interpersonal skills within a team of people across multiple specialisations. Additionally, students will be challenged to find creative solutions to product development and small-scale rapid prototypes. Students will engage in peer learning through agile development and processes. This learning experience will enhance self-development and enable continuous learning.|
|HCD206||Human Centred Design||This subject helps students explore several important fields of general inquiry pertaining to significant intellectual issues related to human beings so they can view everyday problems and formulate solutions in new ways. Broadly, the subject covers the theory of knowledge, human cognition, ethical and moral values, analysis of human history, critical analysis, appreciation of literature and arts and social interaction among human beings through a technological context. Human Centered Design is to give students an appreciation of the factors that influence human behavior and interactions so that they can apply specialised skills to help solve problems that affect diverse societies.|
|CEN207||Creative Enterprise||This subject introduces students to the fundamentals of entrepreneurship and the concept of entrepreneurial mindset in the technology sector. It stimulates new ways of thinking about enterprising behaviour in a multi-disciplinary manner. Students will learn to identify opportunities, creatively solve problems, network, communicate persuasively and work effectively in a team. In addition, this subject will empower students to propose new ventures that focus on social change for good.|
|MLP301||Machine Learning Principles||This subject aims to introduce students to the applications of machine learning, such as robotics, data mining, computer vision, bioinformatics and natural language processing, but will also discuss risks and limitations of machine learning. The subject also covers machine learning concepts and techniques such as supervised and unsupervised machine learning techniques; learning theory, reinforcement learning and model performance improvement.
This subject requires students to have programming skills and knowledge in probability, statistics, regression, and classification.
|DMV302||Data Mining & Visualisation||The aim of this subject is to teach students data mining techniques for both structured and unstructured data. Students will be able to analyse moderate-to-large sized datasets, data preparation, handling missing data, modelling, prediction and classification. Students will also be able to communicate complex information in results of data analytics through effective visualisation techniques.|
|NLP303||Natural Language Processing & Speech Recognition||This subject extends students’ skills and knowledge learned in Machine Learning Principles and Applications of Artificial Intelligence. It discusses application of statistical and other machine learning algorithms to intelligently analyse written and spoken language. It begins with discussion of foundation concepts in natural language processing (NLP) and speech recognition such as language modelling, formal grammars, statistical parsing, machine translation, and dialog processing. Students will then be presented with modern NLP and speech recognition quantitative techniques. Students will be working around different examples applying techniques and NLP toolkits.|
|DLE305||Deep Learning||This subject builds on the skills and knowledge students acquired from Machine Learning Principles and focuses on deep learning. It introduces students to foundational topics on neural networks, its applications to sequence modelling, computer vision, generative models and reinforcement learning. Focus will be given on learning how to model and train neural networks to implement a variety of computer vision applications. Students will be presented with practical examples of how to develop applications using deep learning.
Knowledge in programming and understanding of machine learning concepts is required in this subject.
|ATW306||Advanced Technology – Work Integrated Learning||This subject is designed to provide students with professional experience in an area related to their specialisation. The aim of providing industry-specific opportunities is to enable students to develop skills that will enhance their prospects of gaining meaningful employment and building their career for the future.
Much of the benefit of work integrated learning comes from observation, practicing under supervision and reflection. Work Integrated Learning is an excellent way to broaden the students learning environment while they are studying. It allows them to see first-hand how what they are learning in their degree translates into practice, as well as how ‘real world’ practice relates to what they are learning at University.
This subject will develop work ready skills and boost students’ employability while they are studying.
There are two work integrated learning options available to students:
Option 1: Industry Placement
Students are offered the opportunity to work within a technology company as an intern or volunteer at a technology non-profit organisation. It encourages students to build long-term relationships with the tech industry and provides an opportunity for them to work with and learn from people who may end up becoming colleagues, bosses or mentors. It also provides a context in which to enhance their communication skills and work collaboratively in a professional arena. Students will undertake a series of industry-led tasks that are relevant to their field of study in order to understand the key concepts of working in and managing a professional technology team with emphasis placed on the operation of the environment.
Option 2: Industry Live Brief
This subject requires students to respond to criteria set within the context of an Industry Live Project. An understanding of research methodologies appropriate to professional practice and the documentation of personal creative investigation will be explored. Students will also further investigate and examine entrepreneurial and commercial opportunities through collaborative work practice. The subject is delivered from a cross specialisation perspective and draws on both specialised and common software engineering practices.
Students are required to work both independently and as part of a collaborative team in order to conduct research, analyse and define project parameters and deliver innovative solutions that expand the notion of an industry live brief.
BBCD delivers this course at the following campus locations:
- Sydney: Level 1, 46-52 Mountain Street, Ultimo NSW Australia 2007
- Melbourne: 196 Flinders Street, Melbourne, VIC 3000
Campus Facilities and Services
All campuses are designed to provide students with professional spaces in which to learn and work. They have been planned with student study needs in mind with well-equipped accessible learning spaces as well as student breakout areas for group work and spending time with friends.
Facilities and Services include:
- The Customer Service Hub – our friendly and experienced staff can give help and advice about courses, your enrolment and campus life, including all services and activities on campus.
- Counsellors are available for students to consult with on a range of personal issues
- Student wireless access throughout the Campus
- Student break-out and relaxed study spaces for group work
- Student lounge areas – most with microwaves, kitchenette facilities and vending machines
- The Learning Hub, home to the Learning Support Team, encompasses Learning Skills Advisors, Learning Technology Advisors, and Library & Learning Skills Officers. It provides an integrated, holistic support program for students throughout the study lifecycle within a library/collaborative study environment.
The service includes:
- Support and workshops with highly qualified staff in the areas of Academic skills, Library skills, and Technology skills, both on campus and online.
- Physical and digital resources relevant to studies, such as books, journals, multimedia, databases
- Self-check kiosks for library loans and print and copy facilities
Our Success Coaches are industry and education experts who leverage your strengths to align your learning with your broader life purpose. With a focus on career goals, and trained in Gallup Strength methodologies, your Success Coach will take a strengths-based approach to helping you set your learning and career goals.
Partnering with you for the duration of your studies, the Success Coach is here to make sense of all of the learning experiences, including readiness for and securing of work integrated learning, placements, internships and opportunities in internal enterprises. All of our coaches are industry professionals, which will give you that inside edge you’ll need to be successful in your chosen career.
Irrelevant of how you like to learn, our coaches are there for you. Coaching can take place online, or on campus. Our main priorities are to make sure that you are always well connected and motivated, that you are successfully completing your desired subjects, and that you gain valuable knowledge and experience through participation and engagement, whilst always aligning to your natural talents.
All students will be required to complete ATW306 – Advanced Technology Work Integrated Learning.
This subject is designed to provide students with professional experience in an area related to their specialisation. The aim of providing industry-specific opportunities is to enable students to develop skills that will enhance their prospects of gaining meaningful employment and building their career.
Much of the benefit of work integrated learning comes from observation, practicing under supervision and reflection. Work Integrated Learning is an excellent way to broaden the students’ learning environment while they are studying. It allows them to see first-hand how what they are learning in their degree translates into practice, as well as how ‘real world’ practice relates to what they are learning at University.
This subject will develop work-ready skills to boost students’ employability while they are studying in their final trimester. Students are assessed via the learning management system based on their e-journal, collaboration and their industry submission.
Students will be required to complete a total of 30 hours per week in WIL, of which 21 hours will be spent on the work placement (option 1) or collaboration (option 2) and 9 hours will be self-study.
There are two work integrated learning options available to students:
Option 1: Industry Placement
Students are offered the opportunity to work within a technology company as an intern or volunteer at a technology non-profit organisation. It encourages students to build long-term relationships with the tech industry and provides an opportunity for them to work with and learn from people who may end up becoming colleagues, managers or mentors. It also provides a context in which to enhance their communication skills and work collaboratively in a professional arena. Students will undertake a series of industry-led tasks that are relevant to their field of study in order to understand the key concepts of working in and managing a professional technology team with emphasis placed on the operation of the environment
Option 2: Industry Live Brief
Industry live brief, also known as an industry project engages students in an activity where the parameters of success are set by the client. Academic staff and industry provide supervision for students, while industry provides, mentorship in addition. Numerous technology firms have ideas and opportunities they would like to explore and prototype; this is where students or student teams connect with industry to achieve scale with minimal risk.
An understanding of research methodologies appropriate to professional practice and the documentation of personal creative investigation is explored. Students also further investigate and examine entrepreneurial and commercial opportunities through collaborative work practice. The subject fosters a cross-specialisation perspective and draws on both specialised and common software engineering practices.
Students are required to work both independently and as part of a collaborative team that includes industry representatives to conduct research, analyse and define project parameters and deliver innovative solutions that expand the notion of an industry live brief.
A positive student experience
Torrens values the importance of a positive student experience, and therefore has robust processes to resolve student complaints. The Student Complaints Policy, and associated procedures, can be access from the website.
Paying for your qualification
We offer two payment options for this course:
If you want to complete your qualification debt-free you can choose to pay as you go. This means tuition fees will be invoiced each trimester and payment is required on or before the due date using EFTPOS, credit card or Flywire.
FEE-HELP is Australian Government’s loan scheme for higher education degree courses.
It can assist you in paying for all, or part of, your course fees. Repayments commence via the tax system once your income rises above a minimum threshold ($54,869 in 2016-17). Just like with any other debt, a FEE-HELP debt is a real debt that impacts your credit rating.
Further information about FEE-HELP, including eligibility, is available at:
Austudy and Abstudy
Frequently Asked Questions
Is Course Credit available?
Yes, course credit is available upon application and academic approval. This credit can take the form of credit transfer, block credit, or Recognition of Prior Learning (RPL). For further information, consult our friendly Course and Careers Advisor, or visit the website.
Are any payment options or financial assistance available?
Billy Blue Bachelor Degree courses are eligible for FEE HELP (Australian students only). FEE-HELP is Australian Government’s loan scheme for higher education degree courses. It can assist you in paying for all, or part of, your course fees. Repayments commence via the tax system once your income rises above a minimum threshold ($54,869 in 2016-17). Just like with any other debt, a FEE-HELP debt is a real debt that impacts your credit rating.
What materials and equipment will I need to provide?
Internet access is required for software activation and validation of subscription, as well as to online services.