ACTL2131 Probability and Mathematical Statistics - 2020

Term 2
6 Units of Credit
Risk & Actuarial Studies
The course outline is not available for current semester. To view outlines from other year and/or semesters visit the archives

1. Course Details

Summary of Course

This course covers probability and statistics topics relevant to actuarial studies. Topics covered include univariate and multivariate random variables, moments, moment generating functions, marginal and conditional distributions, sampling distributions, estimation methods, hypothesis tests, and linear regression. Examples relevant to actuarial studies, finance, and insurance are used to illustrate the application of the topics covered.

Teaching Times and Locations

Please note that teaching times and locations are subject to change. Students are strongly advised to refer to the Class Timetable website for the most up-to-date teaching times and locations.

View course timetable

Course Policies & Support

Course Aims and Relationship to Other Courses

This course covers probability and statistics at an introductory yet mathematically rigorous level with a strong foundation in mathematics. The assumed knowledge of the course is a good understanding of mathematics as covered in a full year of university calculus and linear algebra. There is a document on Moodle with some assumed knowledge of mathematics and linear algebra. Consult the Course Coordinator if you do not have the required mathematical background.

ACTL2131 Probability and Mathematical Statistics will have applications in other courses in the actuarial major. More advanced models are covered in ACTL2102 Foundations of Actuarial Models. The course contains necessary knowledge for the more advanced coverage in ACTL3151 Life Contingencies and ACTL3162 General Insurance Techniques.

The course corresponds to the actuarial professional subject CT3 Probability and Mathematical Statistics. Students achieving Credit or higher grades will be recommended for exemption from the professional examination. Exemptions from professional actuarial examinations require above average performance in the equivalent University course.

Students should have a solid background in mathematics and are assumed to be able to use a computer to analyse financial problems. You should be able to use a word processing package (such as WORD) and a spreadsheet (such as EXCEL) as well as the statistical software R.

2. Staff Contact Details

Position Title Name Email Location Phone Consultation Times
Lecturer-in-chargeDrVincent TuSchool of Risk and Actuarial Studies, Business School, East Wing, Level 6 +61 2 9385 7103TBA
Course coordinatorDrKatja Ignatieva
Who should I contact?
  1. Questions about the video lectures and lectorials: Vincent Tu during the lectorials and consultation times;
  2. Questions about tutorial problems: the tutors during the tutorials and consultation times (will be announced prior to mid-term and final exams);
  3. Administrative enquiries about the course: Katja Ignatieva, by e-mail;
  4. Enquiries about coursework programs in Actuarial Studies: School office (;
  5. Enrolment: Business School Student Centre.

3. Learning and Teaching Activities

Approach to Learning and Teaching in the Course

The approach adopted in this course is one of assisted self-study. The approach adopted in this course is called a flipped classroom. While reading this subsection, please refer to the course schedule.

The main rationale for this new structure is to bring the face-to-face (online this term) time later in the learning process when students are more comfortable with the materials, and more likely to interact and ask questions. The first conceptual encounter with the materials happens at home when students watch the video lectures. They then move to a “lectorial”, whereby everyone is gathered in the lecture room. The word combines lectures — because they are run by the lecturer, and with the whole group, and tutorial — because their goal is not to “lecture” students, but to discuss a module at a higher conceptual level, and to cement students’ learning with other activities (such as guest lectures, discussions, advanced exercises). Finally, students move on to practicing their knowledge with tutorial exercises. At this stage, tutorial sessions provide some face-to-face (online this term) on a weekly basis, to personalised help from the tutor.

Course materials are organised in 4 modules. Students are responsible to learn topics with the following materials:

  • Prescribed textbooks (and recommended books for additional support)
  • Video lectures available on the course website
  • Tutorial exercises with solutions
  • Past quizzes and exams for advanced exercises.

Additionally, students who are not familiar with the software R should complete the module “R you ready?” (with videos, exercises and documents) which is available on the course website to all ACTL students.

The philosophy underpinning this course and its Teaching and Learning Strategies are based on Guidelines on Learning that Inform Teaching at UNSW. Specifically, the lectures, tutorials and assessment have been designed to appropriately challenge students and support the achievement of the desired learning outcomes. A climate of inquiry and dialogue is encouraged between students and teachers and among students (in and out of class). The lecturers and tutors aim to provide meaningful and timely feedback to students to improve learning outcome. This is not a course where you can become proficient just by observing.

You will need to get involved in class - evaluating information, asking and answering questions. You also must learn to organize your independent study and practice enough problems to gain a thorough understanding of concepts and how to apply them.

This course is very condensed and the subjects each week require that you know the material of past weeks. Therefore, falling behind will lead to less effective lectures and tutorials and is thus not recommended.

The course puts emphasis on statistics, i.e., parameter estimation, hypothesis testing and linear regression (week 5-10). In order to understand statistics, one should have a good knowledge of probability theory (week 1-4). To ease the workload, you are expected to make exercises during the tutorials. The number of examples in the lecture notes has increased substantially. In the lecture you are required to make attempts to these examples, some of them are to do at home.

Learning Activities and Teaching Strategies

It is expected that the students will take a pro-active approach to learning. The course is organised in the following learning activities.


During the time periods of self-study, students should cover the readings, video lectures and tutorials for the associated module. A required learning strategy for this course is to have read all prescribed readings, watched the associated video lectures and attempted the tutorial exercises before lectures.


The purpose of lectorials is to provide a logical structure for the topics that make up the course, to emphasize the important concepts and methods of each topic, and to provide relevant examples to which the concepts and methods are applied. Lectorials provide opportunities to ask questions about the associated modules.


The more you read the more you know, but the more you practice the more you learn and understand. So the key to the understanding of this course is problem-solving.

The purpose of tutorials is to enable you to raise questions about difficult topics or problems encountered in your studies. Students must not expect another lecture but must attempt the questions themselves in groups and the tutor will answer questions to the group, not to the whole class.

A required learning strategy for the tutorials (on which provision of the course materials is based) is:

  • Prior to making an attempt on the exercises, review your lecture notes and videos.
  • Prior to the tutorial, make an attempt to the exercises you should make before the tutorial (see course schedule).
  • During the tutorial, make an attempt to the exercises you should make in the tutorial (see course schedule).
  • After the tutorial, make an attempt to the exercises you should after the tutorial (see course schedule).
  • If you have questions about the tutorial exercises, ask them from your tutor. If you think you have a good understanding of the material, you should try and answer the questions of your peers. This will give you feedback on your ability to explain the material and hence how well you know the material.
  • Check your answer using the tutorial solution.

The “Get introduced” (explanation of course concepts in the lecture) and “Try it out” (examples in the lecture) are part of the lecture. If you are not able to make a satisfactory attempt to the examples in the lecture, this is feedback that you should revise the lecture material in depth after the lecture. The “Try again” (tutorial exercises) and “Get feedback” (answers from tutor/ tutorial solutions) are part of the tutorial.

The tutorial questions are designed to be attempted in groups (typically of 3-4 students). This collaborative working is advised since it allows learning from peers and allows the more advanced student with a possibility to test whether s/he knows the material in depth and is thereby able to explain it to peers. If the group is unable to solve a question, it can ask for help from the tutor. The tutor will not provide the answer, but would help you in the direction of the solution. This is because you should practice and learn by doing, rather than seeing the solution. The solution manual should not be part of attempting a question, but to verify whether your attempt was correct.

5. Course Resources


The prescribed textbooks for the course are:

  • [FT] The Faculty of Actuaries and The Institute of Actuaries (2002), Formulae and tables for examinations of the Faculty of Actuaries and The Institute of Actuaries. (The formulae book you can use, if unannotated, in quizzes and exams for actuarial courses.)

Suggested textbooks for the course are:

  • [W+] Wackerly, D.D., Mendenhall, W. & Schaeffer, R.L (2008), Mathematical Statistics with Applications, Duxbury, 7th ed. Well respected introductory textbook. Not as difficult as [JR].
  • [JR] Rice, J.A. (2007), Mathematical Statistics and Data Analysis, Duxbury Press, 3ed. Well regarded and comprehensive textbook. More technical than [W+].

You are advised to have one (or both) of these textbooks in this course. Depending on your mathematical background you should choose either [W+] or [JR].

Optional readings are:

  • [CT] The Actuarial Education Company (2010), CT3 Combined Materials Pack, ActEd. (This is the Institute of Actuaries study material for the CT3 exam. Only the syllabus can be downloaded for free.)
  • [H] Hossack, I., Pollard, J. & Zehnwirth, B. (1999), Introductory Statistics with Applications in General Insurance, Cambridge University Press, 2ed. (Want to see how statistics in used in the most statistical actuarial practice area, general insurance? Here’s your starting place.)

Formulae & Tables

Students will only be allowed to bring into the examinations for the Actuarial courses the text "Formulae and Tables for Actuarial Examinations". This text must not be annotated. All students in the actuarial courses should purchase a copy of this text if they wish to use this in the final examinations for this course. The text is available from the UNSW bookstore, the UK Institute of Actuaries or from ActEd Australia. Visit the ActEd website.

6. Course Evaluation & Development

Feedback is regularly sought from students and continual improvements are made based on this feedback. At the end of this course, you will be asked to complete the myExperience survey, which provides a key source of student evaluative feedback. Your input into this quality enhancement process is extremely valuable in assisting us to meet the needs of our students and provide an effective and enriching learning experience. The results of all surveys are carefully considered and do lead to action towards enhancing educational quality.

Each course in actuarial studies at UNSW is reviewed each session by the course co-ordinator using student evaluative feedback from UNSW's myExperience Process. Student feedback is taken seriously, and continual improvements are made to the course based on such feedback. Significant changes to the course are communicated to students taking the course. Your input into improving future offerings of the course is highly valued.

In the past years, students have requested that more examples, which help to understand the lecture material, are discussed during the lectures. Further to that, the majority of students have indicated that they prefer to have the actual theory and theoretical concepts recorded in order to be able to review lecture material at any convenient time. Following this formal feedback in myExperience as well as informal discussions, a flipped classroom approach was adopted.

Student's feedbacks are important. They help us identify the strengths and weakness of the course content and teaching strategies. The process of providing feedback is anonymous.

7. Course Schedule

Note: for more information on the UNSW academic calendar and key dates including study period, exam, supplementary exam and result release, please visit:
Week Activity Topic Assessment/Other
Week 1: Lectorial 1

Revision: Probability space; Calculating with probability; Counting

Module 1.1 Mathematical methods:

Random variables; Measures of location; Measure of dispersion; Moments (central/non-central); Generating functions

Tutorial 1

Based on week 1 material

Week 2: Lectorial 2

Module 1.2: Univariate Distributions

Bernoulli Distribution; Binomial Distribution; Geometric Distribution; Negative Binomial Distribution; Poisson Distribution; Exponential Distribution; Gamma Distribution; Normal Distribution; Uniform Distribution; Beta Distribution; Weibull Distribution; Pareto Distribution

Tutorial 2

Based on week 2 material

Week 3: Lectorial 3

Module 1.3: Joint and Multivariate Distributions

Bivariate distribution functions; Mean, variance, covariance & correlation; Conditional Distributions; Bivariate Normal Distribution; Law of iterated expectation; Conditional Variance Identity; Multivarate distribution functions

Module 1.4: Sampling and Summarizing Data

Numerical methods; Graphical methods; Sample mean, variance, covariance

Tutorial 3

Based on week 3 material

Week 4: Lectorial 4

Module 1.4: Sampling and Summarizing Data

Properties of sample mean and sample variance; Order statistics

Module 1.5: Functions of Random Variables

The CDF technique; The Jacobian transformation technique; The MGF technique; Convolution; Special sampling distributions; Properties of sample mean and sample variance

Tutorial 4

Based on week 4 material

Week 5: Lectorial 5

Module 2.1: Estimation techniques

Introduction/Definitions; Method of Moments; Maximum Likelihood Estimation (MLE); Bayesian Estimation

Module 2.2: Limit Theorems

Chebyshev's inequality; Convergence concepts; Law of Large Numbers; Central Limit Theorem

Tutorial 5

Based on week 5 material

Week 6: Lectorial 6

Module 2.3: Evaluating Estimators

UMVUE's; Cramer-Rao Low Bound; Consistent and sufficient statistics; Confidence Intervals; Properties of MLEs

Tutorial 6

Based on week 6 material

Week 7: Lectorial 7

Module 3.1: Statistical test procedure

Selection of the null hypothesis, alternative hypothesis; Type I and II errors; Power of the test; P-value; Rejection region; Best critical region; Neyman Pearson Lemma; Uniformly most powerful test; Generalized Likelihood Ratio test


Tutorial 7

Based on week 7 material

Week 8: Lectorial 8

Module 3.2: Parametric tests

Two-sample test of means; Two-sample test of variances; k-sample test; Wald test; Hypothesis test for population correlation

Module 3.3: Nonparametric tests

Fisher's exact test; Contingency table; Chi-squared goodness-of-fit test; Test for population correlation; r-sample multinomial test; One-sample sign test; Two-sample sign test

Module 3.4: Goodness-of-fit test

Anderson-Darling & Kolmogorov-Smirnov test

Tutorial 8

Based on week 8 material

Week 9: Lectorial 9

Module 4.1: Simple Linear Regression

Correlation coefficient; Assumptions Relationship between MLE and LSE; Partitioning the variability

Module 4.2: Testing in Simple Linear Regression

Inference on Slope (t-test); Inference on intercept (t-test); Conference intervals; Prediction intervals

Tutorial 9

Based on week 9 material

Week 10: Lectorial 10

Module 4.3: Multiple Linear Regression

Matrix notation; Assumptions; Statistical properties of LSEs; Inference on regression parameters; Inference on functions of regression parameters

Module 4.4: Modelling with Linear Regression

Confounding effects; Collinearity; Heteroscedasticity; Special Explanatory Variables: Interaction of explanatory variables; Special Explanatory Variables: Categorical explanatory variables; Model selection and validation

8. Policies and Support

Information about UNSW Business School protocols, University policies, student responsibilities and education quality and support.

Program Learning Outcomes

The Business School places knowledge and capabilities at the core of its curriculum via seven Program Learning Outcomes (PLOs). These PLOs are systematically embedded and developed across the duration of all coursework programs in the Business School.

PLOs embody the knowledge, skills and capabilities that are taught, practised and assessed within each Business School program. They articulate what you should know and be able to do upon successful completion of your degree.

Upon graduation, you should have a high level of specialised business knowledge and capacity for responsible business thinking, underpinned by ethical professional practice. You should be able to harness, manage and communicate business information effectively and work collaboratively with others. You should be an experienced problem-solver and critical thinker, with a global perspective, cultural competence and the potential for innovative leadership.

All UNSW programs and courses are designed to assess the attainment of program and/or course level learning outcomes, as required by the UNSW Assessment Design Procedure. It is important that you become familiar with the Business School PLOs, as they constitute the framework which informs and shapes the components and assessments of the courses within your program of study.

PLO 1: Business knowledge

Students will make informed and effective selection and application of knowledge in a discipline or profession, in the contexts of local and global business.

PLO 2: Problem solving

Students will define and address business problems, and propose effective evidence-based solutions, through the application of rigorous analysis and critical thinking.

PLO 3: Business communication

Students will harness, manage and communicate business information effectively using multiple forms of communication across different channels.

PLO 4: Teamwork

Students will interact and collaborate effectively with others to achieve a common business purpose or fulfil a common business project, and reflect critically on the process and the outcomes.

PLO 5: Responsible business practice

Students will develop and be committed to responsible business thinking and approaches, which are underpinned by ethical professional practice and sustainability considerations.

PLO 6: Global and cultural competence

Students will be aware of business systems in the wider world and actively committed to recognise and respect the cultural norms, beliefs and values of others, and will apply this knowledge to interact, communicate and work effectively in diverse environments.

PLO 7: Leadership development

Students will develop the capacity to take initiative, encourage forward thinking and bring about innovation, while effectively influencing others to achieve desired results.

These PLOs relate to undergraduate and postgraduate coursework programs.  Separate PLOs for honours and postgraduate research programs are included under 'Related Documents'.

Business School course outlines provide detailed information for students on how the course learning outcomes, learning activities, and assessment/s contribute to the development of Program Learning Outcomes.



UNSW Graduate Capabilities

The Business School PLOs also incorporate UNSW graduate capabilities, a set of generic abilities and skills that all students are expected to achieve by graduation. These capabilities articulate the University’s institutional values, as well as future employer expectations.

UNSW Graduate CapabilitiesBusiness School PLOs
Scholars capable of independent and collaborative enquiry, rigorous in their analysis, critique and reflection, and able to innovate by applying their knowledge and skills to the solution of novel as well as routine problems.
  • PLO 1: Business knowledge
  • PLO 2: Problem solving
  • PLO 3: Business communication
  • PLO 4: Teamwork
  • PLO 7: Leadership development

Entrepreneurial leaders capable of initiating and embracing innovation and change, as well as engaging and enabling others to contribute to change
  • PLO 1: Business knowledge
  • PLO 2: Problem solving
  • PLO 3: Business communication
  • PLO 4: Teamwork
  • PLO 6: Global and cultural competence
  • PLO 7: Leadership development

Professionals capable of ethical, self-directed practice and independent lifelong learning
  • PLO 1: Business knowledge
  • PLO 2: Problem solving
  • PLO 3: Business communication
  • PLO 5: Responsible business practice

Global citizens who are culturally adept and capable of respecting diversity and acting in a socially just and responsible way.
  • PLO 1: Business knowledge
  • PLO 2: Problem solving
  • PLO 3: Business communication
  • PLO 4: Teamwork
  • PLO 5: Responsible business practice
  • PLO 6: Global and cultural competence

While our programs are designed to provide coverage of all PLOs and graduate capabilities, they also provide you with a great deal of choice and flexibility.  The Business School strongly advises you to choose a range of courses that assist your development against the seven PLOs and four graduate capabilities, and to keep a record of your achievements as part of your portfolio. You can use a portfolio as evidence in employment applications as well as a reference for work or further study. For support with selecting your courses contact the UNSW Business School Student Centre.

Academic Integrity and Plagiarism

Academic Integrity is honest and responsible scholarship. This form of ethical scholarship is highly valued at UNSW. Terms like Academic Integrity, misconduct, referencing, conventions, plagiarism, academic practices, citations and evidence based learning are all considered basic concepts that successful university students understand. Learning how to communicate original ideas, refer sources, work independently, and report results accurately and honestly are skills that you will be able to carry beyond your studies.

The definition of academic misconduct is broad. It covers practices such as cheating, copying and using another person’s work without appropriate acknowledgement. Incidents of academic misconduct may have serious consequences for students.


UNSW regards plagiarism as a form of academic misconduct. UNSW has very strict rules regarding plagiarism. Plagiarism at UNSW is using the words or ideas of others and passing them off as your own. All Schools in the Business School have a Student Ethics Officer who will investigate incidents of plagiarism and may result in a student’s name being placed on the Plagiarism and Student Misconduct Registers.

Below are examples of plagiarism including self-plagiarism:

Copying: Using the same or very similar words to the original text or idea without acknowledging the source or using quotation marks. This includes copying materials, ideas or concepts from a book, article, report or other written document, presentation, composition, artwork, design, drawing, circuitry, computer program or software, website, internet, other electronic resource, or another person's assignment, without appropriate acknowledgement of authorship.

Inappropriate Paraphrasing: Changing a few words and phrases while mostly retaining the original structure and/or progression of ideas of the original, and information without acknowledgement. This also applies in presentations where someone paraphrases another’s ideas or words without credit and to piecing together quotes and paraphrases into a new whole, without appropriate referencing.

Collusion: Presenting work as independent work when it has been produced in whole or part in collusion with other people. Collusion includes:

  • Students providing their work to another student before the due date, or for the purpose of them plagiarising at any time
  • Paying another person to perform an academic task and passing it off as your own
  • Stealing or acquiring another person’s academic work and copying it
  • Offering to complete another person’s work or seeking payment for completing academic work

Collusion should not be confused with academic collaboration (i.e., shared contribution towards a group task).

Inappropriate Citation: Citing sources which have not been read, without acknowledging the 'secondary' source from which knowledge of them has been obtained.

Self-Plagiarism: ‘Self-plagiarism’ occurs where an author republishes their own previously written work and presents it as new findings without referencing the earlier work, either in its entirety or partially. Self-plagiarism is also referred to as 'recycling', 'duplication', or 'multiple submissions of research findings' without disclosure. In the student context, self-plagiarism includes re-using parts of, or all of, a body of work that has already been submitted for assessment without proper citation.

To see if you understand plagiarism, do this short quiz:


The University also regards cheating as a form of academic misconduct. Cheating is knowingly submitting the work of others as their own and includes contract cheating (work produced by an external agent or third party that is submitted under the pretences of being a student’s original piece of work). Cheating is not acceptable at UNSW.

If you need to revise or clarify any terms associated with academic integrity you should explore the 'Working with Academic Integrity' self-paced lessons available at:

For UNSW policies, penalties, and information to help you avoid plagiarism see: as well as the guidelines in the online ELISE tutorials for all new UNSW students: For information on student conduct see:

For information on how to acknowledge your sources and reference correctly, see: If you are unsure what referencing style to use in this course, you should ask the lecturer in charge.

Student Responsibilities and Conduct

​Students are expected to be familiar with and adhere to university policies in relation to class attendance and general conduct and behaviour, including maintaining a safe, respectful environment; and to understand their obligations in relation to workload, assessment and keeping informed.

Information and policies on these topics can be found on the 'Managing your Program' website.


It is expected that you will spend at least ten to twelve hours per week studying for a course except for Summer Term courses which have a minimum weekly workload of twenty to twenty four hours. This time should be made up of reading, research, working on exercises and problems, online activities and attending classes. In periods where you need to complete assignments or prepare for examinations, the workload may be greater. Over-commitment has been a cause of failure for many students. You should take the required workload into account when planning how to balance study with employment and other activities.

We strongly encourage you to connect with your Moodle course websites in the first week of semester. Local and international research indicates that students who engage early and often with their course website are more likely to pass their course.

View more information on expected workload


Your regular and punctual attendance at lectures and seminars or in online learning activities is expected in this course. The Business School reserves the right to refuse final assessment to those students who attend less than 80% of scheduled classes where attendance and participation is required as part of the learning process (e.g., tutorials, flipped classroom sessions, seminars, labs, etc.).

View more information on attendance

General Conduct and Behaviour

You are expected to conduct yourself with consideration and respect for the needs of your fellow students and teaching staff. Conduct which unduly disrupts or interferes with a class, such as ringing or talking on mobile phones, is not acceptable and students may be asked to leave the class.

View more information on student conduct

Health and Safety

UNSW Policy requires each person to work safely and responsibly, in order to avoid personal injury and to protect the safety of others.

View more information on Health and Safety

Keeping Informed

You should take note of all announcements made in lectures, tutorials or on the course web site. From time to time, the University will send important announcements to your university e-mail address without providing you with a paper copy. You will be deemed to have received this information. It is also your responsibility to keep the University informed of all changes to your contact details.

Student Support and Resources

​The University and the Business School provide a wide range of support services and resources for students, including:

Business School EQS Consultation Program
The Consultation Program offers academic writing, literacy and numeracy consultations, study skills, exam preparation for Business students. Services include workshops, online resources, individual and group consultations. 
Level 1, Room 1035, Quadrangle Building.
02 9385 4508

Communication Resources
The Business School Communication and Academic Support programs provide online modules, communication workshops and additional online resources to assist you in developing your academic writing.

Business School Student Centre
The Business School Student Centre provides advice and direction on all aspects of admission, enrolment and graduation.
Level 1, Room 1028 in the Quadrangle Building
02 9385 3189

UNSW Learning & Careers Hub
The UNSW Learning & Careers Hub provides academic skills and careers support services—including workshops, individual consultations and a range of online resources—for all UNSW students. See their website for details.
Lower Ground Floor, North Wing Chancellery Building.
02 9385 2060

Student Support Advisors
Student Support Advisors work with all students to promote the development of skills needed to succeed at university, whilst also providing personal support throughout the process.
John Goodsell Building, Ground Floor.
02 9385 4734

International Student Support
The International Student Experience Unit (ISEU) is the first point of contact for international students. ISEU staff are always here to help with personalised advice and information about all aspects of university life and life in Australia.
Advisors can support you with your student visa, health and wellbeing, making friends, accommodation and academic performance.
02 9385 4734

Equitable Learning Services
Equitable Learning Services (formerly Disability Support Services) is a free and confidential service that provides practical support to ensure that your health condition doesn't adversely affect your studies. Register with the service to receive educational adjustments.
Ground Floor, John Goodsell Building.
02 9385 4734

UNSW Counselling and Psychological Services
Provides support and services if you need help with your personal life, getting your academic life back on track or just want to know how to stay safe, including free, confidential counselling.
Level 2, East Wing, Quadrangle Building.
02 9385 5418

Library services and facilities for students
The UNSW Library offers a range of collections, services and facilities both on-campus and online.
Main Library, F21.
02 9385 2650

Moodle eLearning Support
Moodle is the University’s learning management system. You should ensure that you log into Moodle regularly.
02 9385 3331

UNSW IT provides support and services for students such as password access, email services, wireless services and technical support.
UNSW Library Annexe (Ground floor).
02 9385 1333

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