Digital Literacy
What is Digital Literacy?
In an era of information overload, these courses equip you to engage critically and confidently with data and media. Learn to collect, analyse, and visualise data, conduct social research, and recognise the linguistic markers of misinformation and conspiracy theories. Whether you want to develop practical data skills, measure social phenomena, or challenge misleading narratives, these courses build the analytical and critical capabilities essential for informed citizenship and future careers.
Below, you can find a sample list of C4L courses within the Digital Literacy. Please note that they are subject to change. The finalised list of courses and details (including course descriptions, assessment and timetabling information) will be available ahead of enrolment opening in August 2026. Keep an eye on this page for more information!
They Don’t Want You to Know This: How to respond to conspiracy theories & disinformation
Misinformation and conspiracy theories have become prevalent in all areas of life, and cause major problems not just for the media in telling what is ‘true’, but also for politicians, businesses, scientific researchers, and even within social groups and families. The contents and communicative techniques of these theories are, perhaps surprisingly, age-old – ideas are repurposed and integrated into new material, and conspiracy theories frequently combine to create all-encompassing supertheories. A recent turn is the use of conspiracy theory as deliberate misinformation, which raises these issues from fringe beliefs to society-wide concerns. This course teaches students to recognise and challenge markers of disinformation, both in the remoulding of old ideas to serve a new ideology, and the ways in which purveyors of disinformation employ language in manipulative and deceitful ways
Applied Data Skills
'Applied Data Skills' at a glance:
This course is available in Semester 2, 2026. Course code: PSYCH1012 Credits: 20
What will I learn from this course?
This course provides an overview of the basic skills needed to turn raw data into informative summaries and visualisations presented in professional reports, presentations, and dashboards. The course will introduce you to R, a programming language that can help automate working with data. The course will cover importing and processing data from spreadsheets, producing data summaries of descriptive statistics in tables, creating beautiful and informative visualisations, and constructing reports that automatically update when the underlying data changes. You will also learn to use Generative AI to ethically and responsibly assist with coding.
While the technical focus is on data processing in R, the course is designed for students from all disciplines, including those with no prior programming experience. Examples and applications will highlight how data skills are used across sectors—such as public policy, health, education, creative industries, and social sciences.
How will I be assessed on this course?
Full details of each assessment will be provided in dedicated assessment briefs. The descriptions below are indicative summaries and are designed to support student understanding of overall course structure and expectations.
|
Sequence |
Assessment type (drop down menu) |
Group or Individual Assessment |
Weighting (indicate % or Pass/Fail |
|
Summative |
Weekly MCQs: There will be a weekly multiple-choice quiz administered on Moodle that tests and consolidates the functions and concepts learned that week. You will be given two attempts on each quiz. |
Individual |
10% |
|
Summative |
Backwards engineer: You will be given a dataset and a finished report on that data. Your task is to write the code that produces the report. You will be asked to peer review other submissions and will receive a mark for participation (7.5% for submitting, 7.5% for peer-review). A solution file and walkthrough video will be released as instructor feedback.
Coding is not a skill that can be learned by cramming. Additionally, we place a large emphasis on peer coding and therefore you must attend 75% of all classes to receive a grade for this assessment. |
Individual |
15% |
|
Summative |
Technical brief: You will be given a choice of two datasets and a technical brief for the content of the report. Your task is to write a fully reproducible report using R and RMarkdown that clearly and effectively presents and summarises the data to provides key insights. Individual feedback will be provided via a rubric on clearly defined marking criteria in addition to an overall written comment. |
Individual |
75% |
What unique learning experiences will I have on this course?
You will collaborate to solve real-world data challenges, culminating in an individual project that integrates technical and communication skills.
Who are the course leaders?
Professor Emily Nordmann & Prof Lisa DeBruine are leading this course.
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Measuring your social world
'Measuring your social world' at a glance:
This course is available in Semester 1, 2026. Course code: SSPS1001 QM1. Credits: 20
What will I learn from this course?
You will work through the process of data generation, collection, and basic data description. Examples will draw upon the topics of sustainability and challenges around climate change. You will begin with learning about how researchers conceptualise ideas and translate this into definable and measurable elements, and associated challenges when this is done without care. By the end, you will have an understanding of why the data generating process matters, how to collect data, and how to offer basic descriptions, including visualisations of your data to a non-expert audience.
How will I be assessed on this course?
|
Sequence |
Assessment type (drop down menu) |
Group or Individual Assessment |
Weighting (indicate % or Pass/Fail |
|
Formative Feedback |
Weekly tasks related to applying topics from class |
Group |
0% |
|
Summative: Lab book |
4 lab assignments: 1-make a mini dataset (criteria provided) 2-descriptive statistics of your mini dataset; including data visualisations 3-test your data; basic hypothesis tests 4-critique a mini report and the dataset used to produce it |
Individual |
75% |
|
Summative: Presentation |
Students select an element taught during the semester and present the lesson in a pre-recorded 5-10 minute presentation |
Individual |
25% |
What unique learning experiences will I have on this course?
Students are rarely, if ever, explicitly taught how to generate measures related to concepts that are important to them for research. This course engages with elements that are crucial to this, helping you to become more critical of the research you will read in honours courses, and to promote confidence in defining and measuring ideas that are important for you during your dissertations.
This course also offers a pathway to additional quantitative methods training in semester 2 with SSPS2001: Analysing your Social World. Leading to 20 weeks of supported data and analysis training.
Who is the course leader?
Dr Nicole Pamphilis is leading this course.
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