Mathematics and Statistics Learning Centre
The Mathematics and Statistics Learning Centre (MSLC) fosters innovation in teaching and learning, and provides academic and administrative support for undergraduate and postgraduate teaching in the School of Mathematics and Statistics. We also directly support undergraduate students with course advice and preparatory resources.
The MSLC provides various resources to help students learn mathematics and statistics.
Resources for academics
The Learning and Teaching resources below have been developed by the MSLC to support teaching & learning in the School.
 Conceptual Learning with Interactive Applets  applets for calculus, probability, statistics and other areas
 Chocs and Blocks  an interactive activity investigating sampling and variability
 Sudoku experiment  a tutorial and lab activity covering study design and data collection
MSLC staff make contributions to the Australian mathematics and statistics education landscape through the scholarship of teaching and learning.
The MSLC runs an Occasional Seminar Series, which incorporates talks by MSLC and School staff as well as invited external speakers. If you are interested in giving a talk as part of this Series, please contact Dr Anthony Morphett, a.morphett@unimelb.edu.au. For seminar announcements, subscribe to the mailing list here.
Ongoing projects
Investigating the use of electronic whiteboards in smallgroup classes for teaching statistics
Dr Christopher Duffy, Dr Paul Fijn, and Dr Robert Maillardet
This research aims to understand how to use electronic whiteboards effectively for smallgroup teaching in statistics subjects. This will be considered by evaluating the extent to which they improve student selfefficacy (attitude and confidence with statistics), foster student engagement, and increase student knowledge of statistics.
Evaluating student engagement with and perceptions of a flipped classroom design for a large statistics subject
Dr Paul Fijn and Dr Alba Santin Garcia
This research is investigating a class taught with a “flipped classroom” design, with main lecture content delivered outside of class (primarily videos) and a heavy focus on interactive and exploratory tasks within the classroom. The focus is on evaluating which modes and tasks students engage with most, and how that translates into effective learning outcomes.
Understanding different conceptualisations of mathematical communication from the first year undergraduate perspective
Dr Alba Santin Garcia and Dr TriThang Tran
This research aims to understand the different ways in which first year mathematics students experience communication in mathematics. To achieve this, we will interview first year mathematics students from a range of different subjects. The results of the study are intended to inform approaches to improving mathematical communication in our firstyear classes.
Learning in Circles (LINC)
Dr Christine Mangelsdorf, Dr Anthony Morphett, Prof Antoinette Tordesillas, Dr TriThang Tran and Dr Binzhou Xia
This research investigates whether studying a realworld application of calculus embedded throughout the semester affects how students see relationships between different mathematical topics in MAST10006 Calculus 2.
Next Generation Tutorial Room Development
In 2021, the Faculty of Science, on behalf of the School of Mathematics and Statistics, invested $180,000 in the creation of two pilot Next Generation tutorial rooms, each equipped with six largescreen interactive whiteboard devices. These devices permit students to undertake integrated and seamless workflow between written and computer work, overcoming the artificial separation between computer work in labs and tutorial work in a smallgroup learning setting. In 2022, subject development to make use of these upgraded spaces took place in MAST10006 (Calculus 2), MAST10022 (Linear Algebra: Advanced) and MAST10010 (Data Analysis 1). In 2023, further work is planned for MAST10006 (Calculus 2), MAST10010 (Data Analysis 1) and MAST20005 (Statistics). Subject development work on this project to date has been undertaken by a number of MSLC staff in 2022: Dr Alegra Dajic, Dr Paul Fijn, Dr Shelly Levin, Mr Matthew Mack, Dr Anthony Morphett, Dr Robert Maillardet, Dr TriThang Tran, and Dr Paul Williams. Development work has also been undertaken by Dr Lawrence Reeves.
Strengthening Learning in Mathematics and Statistics using Video Consultations
Project Lead: Dr Rob Maillardet
Video Consultations emulate, in a purely online form, the teaching and learning collaborations that occur in oneonone live consultations with students. They have been successfully used in Mathematics and Statistics. To date students have completed over 10,600 Video Consultation sessions lasting in total approximately 7,000 hours. Video consultations are over an order of magnitude cheaper per study hour than live consultations, a cost which reduces over time given their longevity. This project established a dedicated School recording studio and funded selected talented research students and sessional teaching staff to prepare and implement high quality new Consultations across 13 subjects with enrolments of approximately 9400, thus doubling the School’s resource pool of Video Consultations.
WebWork Resources for MAST10006
Project Lead: Dr TriThang Tran
WebWork is an online homework system, designed with the needs of mathematics subjects. This project’s goals are to develop WebWork modules that supplement the lecture content, in a way the allows lecturers to focus more on mathematical communication. The modules can be completed asynchronously. In practical terms, we are developing online, selfguided WebWork problems, which students will be directed to complete prior, or after a lecture, depending on the problem. Generally, the problems are short, simple, and target basic skills and misconceptions in the subject. Students typically only learn good mathematical writing in tandem with learning new mathematical concepts. The redesign would mean lectures can draw on the developed online resources as inspiration for discussion on writing and presentation. Moreover, by moving more routine problems out of the lectures, this frees up some lecture time for lecturers to use to discuss and target aspects of communication. For example, a student might see limit calculations in a WebWork module, that primes them for discussion in lectures about the importance of the justifications required in steps of the calculation. The project has the added benefit of allowing students to practise using WebWork, prior to needing to use it for assignments. This aims to reduce the number of technical issues that arise during assignments.
Lightboard Studio
Project Lead: Dr Rob Maillardet
Technical lead: Keenan Hellyer (Biosciences)
A lightboard supports video recording whilst facing the audience and writing on a glass board filled with light. This project established two Lightboard Studios (in Peter Hall and Old Geology North) which are fully automated onetouch installations for staff use without professional support. Custom software was also developed from scratch to support three recording modes: lightboard only, lightboard with overlays, and lightboard with overlays plus a separate second video stream.
Past projects
Flipped Classroom Resources for Real Analysis (MAST20026)
Project Lead: Dr Christopher Duffy
The dualdelivery nature of 2022 provided an opportunity to develop subject materials that give students greater agency in how they work through subject material. Work in this project built upon a set of notes first created in 2021. The final output of this project is a full set of flipped classroom materials (readings and lecture plans) for lecturers delivering an introductory subject in real analysis.
Student Maths and Stats Help (SMASH)
Project Leads: Dr Anthony Morphett, Prof Deb King and Ms Adriana Zanca
The Student Maths And Stats Help (SMASH) maths skills dropin service provided support for students from across the university to get help with numeracy and quantitative skills needed for their studies. It helped students improve their mathematical skills, develop basic quantitative and statistical reasoning skills and address gaps or weaknesses in their mathematical knowledge. Support was provided by students employed as peer leaders. SMASH was staffed and managed by students, with studentsaspartners in the delivery and management of the programme. SMASH ran from 20172022, two years longer than originally budgeted due to running under budget over the first three years of operation.
MAST90045 Lab Class Development
Project Lead: Dr Yuji Saikai
The project was intended to trial realtime collaborative coding exercises using CoCalc, a webbased computing facility, in MAST90045. Learning to code, just like learning mathematics, inherently requires individual exercises, for which the existing computer laboratory classes are designed. The downside is difficulty in sharing and discussing individual attempts. In a physical laboratory, students sitting next to each other may glance at others’ work but not work of those sitting apart. It is even more difficult for remote students. CoCalc provides webbased coding environment in which multiple people can work simultaneously and share their code as well as outputs realtime. The use of such environment can mitigate the solitary nature of coding exercises and enhance the advantage of small classes—participatory discussion. The benefit is even greater for remote students. In addition to the positive feedback through the SSLC survey, throughout the semester, the instructors and students had lively coding practices, which otherwise would have been difficult to achieve. Since the result was very positive, CoCalc will be employed in 2023 again for further development.
MAST10007 Lab Class Development
Project Leads: James Clift and Nick SgroTraikowski
Support and supervision: Dr Christine Mangelsdorf
Many of the MAST10007 Linear Algebra computer lab class activities had not been updated for many years, and student feedback indicated that some of the activities were repetitive and did not engage students. James Clift and Nick SgroTraikowski redeveloped several of the MAST10007 computer lab activities over semester 2, 2022. They wrote several new computer lab activities and substantially updated several others. They focussed on incorporating interactivity and visualisation into the computer lab activities, which they achieved by creating several new interactive applets using GeoGebra and MATLAB. Student feedback about the new computer lab activities was positive, and increased class attendance indicates that the activities were successful in improving student engagement in the computer lab classes.
Selected presentations by MSLC staff
Chris Duffy (2023) Welcoming Students to the Community of Mathematicians, First Year in Mathematics Network Annual Workshop, University of Queensland
Chris Duffy, Ashlee Pearson, Valerie CotroneiBaird, Gab CorboPerkin (2023) Tutor and demonstrator professional development  Lessons from across the university, University of Melbourne Learning and Teaching Conference
Chris Duffy and Paul Fijn (2022) Next Generation Tutorial Spaces, Faculty of Science Learning and Teaching Gathering
Paul Fijn, Cindy Huang, Susan James and Dominic Maderazo (2022) Engagement Beyond the Curriculum, Mathematical Association of Western Australia (MAWA) Annual Conference
Alba Santin Garcia (2022) Whiteboard tutorials @ Melbourne Uni, Maths and Stats Teaching Seminar, University of Technology Sydney
Anthony Morphett (2022) Exploring infectious disease models with handshakes, Mathematical Association of Victoria (MAV) Annual Conference
John Banks, Paul Fijn, Robert Maillardet, Anthony Morphett, Rosie Pingitore, Alba Santin Garcia, TriThang Tran (2021) Active learning groupwork based online tutorials, Herenga Delta 2021: The 13th Southern Hemisphere Conference on the Teaching and Learning of Undergraduate Mathematics and Statistics
Tutoring with the School of Mathematics and Statistics
Each year, the School of Mathematics and Statistics hires casual tutors to deliver tutorials, labs, workshops and undertake assignment marking. The roles and responsibilities of casual tutors in the School of Mathematics and Statistics are outlined in the following position descriptions. These position descriptions outline selection criteria and other requirements for these positions.
Tutor (Tutorial/Lab/Workshop)
Tutor (Marking)
Successful applicants are required to hold a valid Victorian Working with Children Check (WWWC).
In most cases, tutors who are successful in attaining a tutor position to deliver a tutorial will also be assigned a marking position.
See below for information about specific semesters, including important dates and tentative available positions.
For questions about tutoring in the School of Mathematics and Statistics please see the MSLC Contact page for contact details.

Semester 1, 2024
Applications for tutoring in Semester 1 2024 are welcome from past and prospective tutors. Priority will be given to current postgraduate students in the School of Mathematics and Statistics.
On Monday 20 November 2023 (12pm, Old Geology lecture theatre 1) there will be an inperson information session on casual teaching in the School of Mathematics and Statistics. This session will outline the roles and responsibilities of tutors in the School and outline the hiring process. This session is intended for postgraduate students who are considering applying to tutor with the School of Mathematics and Statistics in 2024. Attendance at this session is not required for prospective casual staff to be awarded a position, nor does attending this session guarantee a position.
Important Dates
Information Session on Casual Teaching in the School of Mathematics and Statistics Monday 20 November 2023, 1:00pm, Old Geology lecture theatre 1 Applications through the Casual Tutor Recruitment System (CTRS) Monday 8 January  Friday 19 January 2024 Selection interviews (new tutors only) Monday 29 January/Tuesday 30 January 2024
In person on Parkville campusAnnouncement of preliminary outcome of applications Friday 2 February 2024 Mandatory training for new tutors Tuesday 20 February 2024: online training modules
Wednesday 21 February 2024, 9:00am1:00pm: new tutor training, in person on Parkville campus
Wednesday 20 March 2024, 4:156:15pm: new tutor checkin, in person on Parkville campusMandatory subject meetings Thursday 22 February  Thursday 29 February 2024
(dates and times vary for particular subjects)Semester 1 Teaching Period Monday 26 February  Friday 24 May 2024 Applications are welcome via the Casual Tutor Recruitment System (CTRS). All past applicants are recommended to regularly update their CTRS profile to reflect their current experiences, student status and qualifications.
Available Positions (tentative)
Please see the University of Melbourne Handbook for more information on subjects below. The number of positions available for each subject will depend on enrolments.
Calculus 1 (MAST10005)
Tutors in this subject are assigned to tutorials (timetabled as p01 classes) or workshops (timetabled as w01 classes).
Tutorials are small group active learning classes. Students work in groups, on whiteboards, while the tutor actively provides feedback to each group throughout the lesson. Tutors are expected to mark assignments for students in their tutorials. Workshops consist of larger groups, where students practice core skills of the subject. Depending on class size, there may be one or two tutors assigned to a workshop.Calculus 2 (MAST10006)
Tutors in this subject are assigned to tutorials (timetabled as p01 classes).
Tutorials are small group active learning classes. Students work in groups, on whiteboards, while the tutor actively provides feedback to each group throughout the lesson. Tutors are expected to mark assignments for students in their tutorials.
NB: This subject is piloting our NextGen tutorial rooms, which replaces whiteboards with digital touch screen whiteboards in some classes.Linear Algebra (MAST10007)
Tutors in this subject are assigned to tutorials (timetabled as p01 classes) or computer labs (timetabled as p02 classes).
Tutorials are small group active learning classes. Students work in groups, on whiteboards, while the tutor actively provides feedback to each group throughout the lesson. Tutors are expected to mark assignments for students in their tutorials. In computer labs, students learn to use MATLAB.Accelerated Mathematics 1 (MAST10008)
Tutors in this subject are assigned to tutorials (timetabled as p01 classes) or computer labs (timetabled as p02 classes).
Tutorials are small group active learning classes. Students work in groups, on whiteboards, while the tutor actively provides feedback to each group throughout the lesson. Tutors are expected to mark assignments for students in their tutorials. In computer labs, students learn to use MATLAB.Experimental Design and Data Analysis (MAST10011)
Tutors in this subject are assigned to twohour NextGen tutorials (timetabled as consecutive p01 and p02 classes).
NextGen Tutorials are small group active learning classes, that make use of digital touch screen whiteboards. Students work in groups, on NextGen digital whiteboards, while the tutor actively provides feedback to each group throughout the lesson. Students use R in these classes. Tutors are expected to mark assignments for students in their tutorials.Introduction to Mathematics (MAST10012)
Tutors in this subject are assigned to tutorials (timetabled as p01 and p02 classes).
Tutorials are small group active learning classes. Students work in groups, on whiteboards, while the tutor actively provides feedback to each group throughout the lesson. Students in this subject attend two tutorials per week – one tutorial on Monday/Tuesday, and another on Thursday/Friday.
Tutors are expected to mark assignments for students in their Monday/Tuesday tutorials.Linear Algebra: Advanced (MAST10022)
Tutors in this subject are assigned to onehour NextGen tutorials (timetabled as p01 and p02 classes).
NextGen Tutorials are small group active learning classes, that make use of digital touch screen whiteboards. Students work in groups, on NextGen digital whiteboards, while the tutor actively provides feedback to each group throughout the lesson. Students use MATLAB in these classes. Students in this subject attend two tutorials per week – one tutorial on Monday/Tuesday/Wednesday morning, and another on Wednesday afternoon/Thursday/Friday. Tutors are expected to mark assignments for students in their Monday/Tuesday/Wednesday morning tutorials.Probability (MAST20004)
Tutors in this subject are assigned to 2hour tutorial/computer lab classes (timetabled as consecutive p01 and p02 classes). The first hour of the class is a tutorial and the second hour of the class is a computer lab class.
Tutorials are small group active learning classes. Students work in groups, on whiteboards, while the tutor actively provides feedback to each group throughout the lesson. Tutors are expected to mark assignments for students in their tutorials. In computer labs, students learn to use R.Probability for Statistics (MAST20006)
Tutors in this subject are assigned to 2hour tutorial/computer lab classes (timetabled as consecutive p01 and p02 classes). The first hour of the class is a tutorial and the second hour of the class is a computer lab class.
Tutorials are small group active learning classes. Students work in groups, on whiteboards, while the tutor actively provides feedback to each group throughout the lesson. Tutors are expected to mark assignments for students in their tutorials. In computer labs, students learn to use R.Vector Calculus (MAST20009)
Tutors in this subject are assigned to tutorials (timetabled as p01 classes).
Tutorials are small group active learning classes. Students work in groups, on whiteboards, while the tutor actively provides feedback to each group throughout the lesson. Tutors are expected to mark assignments for students in their tutorials.Real Analysis (MAST20026)
Tutors in this subject are assigned to tutorials (timetabled as p01 and p02 classes).
Tutorials are small group active learning classes. Students work in groups, on whiteboards, while the tutor actively provides feedback to each group throughout the lesson. Students in this subject attend two tutorials per week – one tutorial on Monday/Tuesday, and another on Thursday/Friday.
Tutors are expected to mark assignments for students in their Monday/Tuesday tutorials.Engineering Mathematics (MAST20029)
Tutors in this subject are assigned to tutorials (timetabled as p01 classes).
Tutorials are small group active learning classes. Students work in groups, on whiteboards, while the tutor actively provides feedback to each group throughout the lesson. Tutors are expected to mark assignments for students in their tutorials.Analysis of Biological Data (MAST20031)
Tutors in this subject are assigned to tutorials (timetabled as T01 classes) or computer labs (timetabled as p01 classes).
Tutorials are active learning seminars, cotaught with a lecturer and two tutors. Computer labs are are cotaught with two tutors. Students use R in these classes. Tutors are expected to mark assignments for students in their computer lab classes.Vector Calculus: Advanced (MAST20032)
Tutors in this subject are assigned to tutorials (timetabled as p01 classes).
Tutorials are small group active learning classes. Students work in groups, on whiteboards, while the tutor actively provides feedback to each group throughout the lesson. Tutors are expected to mark assignments for students in their tutorials.Real Analysis: Advanced (MAST20033)
Tutors in this subject are assigned to tutorials (timetabled as p01 and p02 classes).
Tutorials are small group active learning classes. Students work in groups, on whiteboards, while the tutor actively provides feedback to each group throughout the lesson. Students in this subject attend two tutorials per week – one tutorial on Monday/Tuesday, and another on Thursday/Friday.
Tutors are expected to mark assignments for students in their Monday/Tuesday tutorials.Algebra (MAST30005)
Tutors in this subject are assigned to tutorials (timetabled as p01 classes).
Tutorials are small group active learning classes. Although larger than our first and second year tutorials, students are still encouraged to work in groups on whiteboards, where the learning spaces allow for this. Tutors are expected to mark assignments for students in their tutorials.Graph Theory (MAST30011)
Tutors in this subject are assigned to tutorials (timetabled as p01 classes).
Tutorials are small group active learning classes. Although larger than our first and second year tutorials, students are still encouraged to work in groups on whiteboards, where the learning spaces allow for this. Tutors are expected to mark assignments for students in their tutorials.Techniques in Operations Research (MAST30013)
Tutors in this subject are assigned to tutorials (timetabled as p01 classes).
Tutorials are small group active learning classes. Although larger than our first and second year tutorials, students are still encouraged to work in groups on whiteboards, where the learning spaces allow for this. Tutors are expected to mark assignments for students in their tutorials.Probability for Inference (MAST30020)
Tutors in this subject are assigned to tutorials (timetabled as p01 classes).
Tutorials are small group active learning classes. Although larger than our first and second year tutorials, students are still encouraged to work in groups on whiteboards, where the learning spaces allow for this. Tutors are expected to mark assignments for students in their tutorials.Complex Analysis (MAST30021)
Tutors in this subject are assigned to tutorials (timetabled as p01 classes).
Tutorials are small group active learning classes. Students work in groups, on whiteboards, while the tutor actively provides feedback to each group throughout the lesson. Tutors are expected to mark assignments for students in their tutorials.Linear Statistical Models (MAST30025)
Tutors in this subject are assigned to computer labs (timetabled as p01 classes).
Computer Labs are small group active learning classes. Students use statistical software in these classes. Tutors are expected to mark assignments for students in their classes.Applied Mathematical Modelling (MAST30030)
Tutors in this subject are assigned to tutorials (timetabled as p01 classes).
Tutorials are small group active learning classes. Although larger than our first and second year tutorials, students are still encouraged to work in groups on whiteboards, where the learning spaces allow for this. Tutors are expected to mark assignments for students in their tutorials.Biological Modelling and Simulation (MAST30032)
Tutors in this subject are assigned to computer labs (timetabled as p01 classes).
Computer Labs are small group active learning classes. Students use statistical software in these classes. Tutors are expected to mark assignments for students in their classes.Optimisation for Industry (MAST90014)
Tutors in this subject are assigned to computer labs (timetabled as p01 classes).
Computer Labs are small group active learning classes. Students use a range of software to solve optimisation problems. Tutors are expected to mark assignments for students in their classes.Thinking and Reasoning with Data (MAST90044)
Tutors in this subject are assigned to computer labs (timetabled as p01 classes).
Computer Labs are small group active learning classes. Students use statistical software in these classes. Tutors are expected to mark assignments for students in their classes.Systems Modelling and Simulation (MAST90045)
Tutors in this subject are assigned to computer labs (timetabled as p01 classes).
Computer Labs are small group active learning classes. Students use statistical software in these classes. Tutors are expected to mark assignments for students in their classes.Elements of Probability (MAST90057)
Tutors in this subject are assigned to 2hour tutorial/computer lab classes (timetabled as consecutive p01 and p02 classes). The first hour of the class is a tutorial and the second hour of the class is a computer lab class.
Tutorials are small group active learning classes. Students work in groups, on whiteboards, while the tutor actively provides feedback to each group throughout the lesson. Tutors are expected to mark assignments for students in their tutorials. In computer labs, students learn to use R. The subject content is the same as MAST20004 Probability.Data and Decision Making (MAST90072)
Tutors in this subject are assigned to computer labs (timetabled as p01 classes).
Computer Labs are small group active learning classes. Students use statistical software in these classes. Tutors are expected to mark assignments for students in their classes.Methods of Mathematical Statistics (MAST90105)
Tutors in this subject are assigned to computer labs (timetabled as p01 classes) or workshops (timetabled as w01 classes).
Computer Labs are small group active learning classes. Students use statistical software in these classes. Workshops are problem based classes. Tutors are expected to mark assignments for students in their workshops.Inference for SpatioTemporal Processes (MAST90122)
Tutors in this subject are assigned to computer labs (timetabled as p01 classes).
Computer Labs are small group active learning classes. Students use statistical software in these classes. Tutors are expected to mark assignments for students in their classes.Statistical Modelling for Data Science (MAST90139)
Tutors in this subject are assigned to computer labs (timetabled as p01 classes).
Computer Labs are small group active learning classes. Students use statistical software in these classes. Tutors are expected to mark the assignments and computer lab test for students in their classes. As a 25 point subject, tutors in this subject will have a larger marking load.Statistics for Bioinformatics (BINF90001)
Tutors in this subject are assigned to computer labs (timetabled as p01 classes).
Computer Labs are small group active learning classes. Students use statistical software (R in particular) in these classes. Tutors are expected to mark assignments for students in their classes. 
Semester 2, 2024
Applications for tutoring in semester 2 are welcome from past and prospective tutors. Priority will be given to current postgraduate students in the School of Mathematics and Statistics.
Important Dates
Applications through the Casual Tutor Recruitment System (CTRS) Dates to be confirmed Inperson selection interviews (new tutors only) Dates to be confirmed Announcement of preliminary outcome of applications Dates to be confirmed Mandatory training for new tutors Dates to be confirmed Mandatory subject meetings Thursday 18 July  Thursday 25 July 2024 (dates and times vary for particular subjects) Semester 2 Teaching Period Monday 22 July  Friday 18 October 2024 Applications are welcome via the Casual Tutor Recruitment System (CTRS). All past applicants are recommended to regularly update their CTRS profile to reflect their current experiences and qualifications.

Summer semester, 2024
Due to the nature of the summer semester, summer tutoring positions are only available to tutors who have recently tutored. Applications from new tutors will not be considered. Current tutors will be invited to apply.
See the University academic calendar for summer semester dates.
These pages contain information about undergraduate mathematics and statistics options to help you plan your course. Please choose from the options below.

Firstyear Mathematics and Statistics
Information for students choosing firstyear subjects.

Majors in mathematical and statistical sciences
Information for students wishing to specialise in mathematics, statistics and related areas.

Diploma in Mathematical Sciences
Study mathematics and statistics alongside another major with the concurrent diploma.

Mathematics and Statistics electives or breadth
Study mathematics and statistics to complement another discipline.