COMU1130 Data & Society
The Complete Study & Assessment Guide · Semester 2 2026COMU1130 Overview
- University of Queensland
- Semester 2 2026
- Level 1
- Coursework assessment
COMU1130 Data & Society is a Level 1 University of Queensland course about the technical, economic and social organisation of data-driven platforms.
- Assessment route Weekly application quizzes lead into a critical essay and a group data-driven solution presentation.
- Quiz condition Submit at least five quiz worksheets to pass the Weekly Quizzes component.
- Core challenge Connect platform promises to data practices, stakeholder conflicts and public-value consequences.
What you get in the full COMU1130 Study & Assessment Guide
- 10 chapters, 42 pages — every assessed topic, week by week
- 23 original practice questions, each with a model answer
- 10 worked examples, solved the way COMU1130 marks
- 15 computed figures, redrawn from the course’s own numbers
- Before-you-submit checklist for the Essay on Platformization (35%) — what markers reward, what costs marks
Free on this page: assessment table, chapter map, one worked example, key terms, FAQ. The rest is in the full study & assessment guide.
How COMU1130 is assessed
| Component | Weight | Format |
|---|---|---|
| Weekly Quizzes | 30% | Paper worksheets completed in tutorials; highest five completed scores are aggregated |
| Essay on Platformization | 35% | Individual 1,800-word critical essay submitted through Turnitin; due 9 October 2026 at 16:00 |
| Your Data-Driven Smart Solution | 35% | Group project presented in class for 8 to 10 minutes in Week 13 |
The three published rows sum to 100%. Weekly Quizzes require at least five submitted worksheets to pass that assessment component. Check the current course site for operational submission and presentation instructions.
What other students are asking about COMU1130
- Where can I find lecture notes or study guides for COMU1130 at UQ?
- The COMU1130 study guide in AskSia Library covers Data & Society in 10 chapters, with the assessment table, a chapter-by-chapter topic map, key terms and a worked example, free to read on this page.
- Is there a final exam in COMU1130?
- No final exam is listed in the offering this AskSia Library guide is built from; COMU1130 is assessed by Weekly Quizzes 30%, Essay on Platformization 35%, Your Data-Driven Smart Solution 35%.
- What does COMU1130 cover on platform anatomy and market sides?
- In AskSia Library's COMU1130 guide, the Platform Anatomy and Market Sides chapter explains data, algorithms, interfaces, ownership, user agreements, business models, network effects and multi-sided coordination.
- What does COMU1130 cover on datafication, commodification and selection?
- In AskSia Library's COMU1130 guide, the Datafication, Commodification and Selection chapter explains translation into data, relevance ranking, value capture, feedback effects and careful proxy analysis.
- What does COMU1130 cover on participatory and algorithmic culture?
- In AskSia Library's COMU1130 guide, the Participatory and Algorithmic Culture chapter explains user contribution, computational classification, recommendation, personalisation, representation and cultural feedback.
Current COMU1130 dates
| Date | Item | Control |
|---|---|---|
| 9 October 2026 at 16:00 | Essay on Platformization | Published Semester 2 essay due date |
| Week 13 | Your Data-Driven Smart Solution | Published presentation week |
The current Semester 2 2026 course materials publish these timings. Confirm submission arrangements on the course site.
What COMU1130 covers
The guide builds platform mechanics first, tests them across social sectors, then turns the analysis into critical essays and responsible design.
Data, Platforms and Stakeholders
data and governance, platform intermediaries, stakeholder interests, technical and economic layers, and critical mechanism questionsData become socially consequential when institutions collect, classify and use them to make decisions.
- data and social decision-making
- platforms as programmable intermediaries
- stakeholder contributions and interests
- technical, economic and discursive layers
- mechanism verbs for critical analysis
Platform Anatomy and Market Sides
data, algorithms, interfaces, ownership, user agreements, business models, network effects and multi-sided coordinationPlatform power is assembled from data, algorithms, interfaces, ownership, business models and user agreements. This chapter opens that anatomy and then maps multi-sided services without collapsing every participant into the word user.
- data and algorithms
- interfaces and user agreements
- ownership and value capture
- multi-sided participant groups
- network effects and dependency
Datafication, Commodification and Selection
translation into data, relevance ranking, value capture, feedback effects and careful proxy analysisThree linked mechanisms explain how platforms reorganise social activity. Datafication translates lived actions into processable signals. Selection orders visibility through ranking and recommendation.
- translation from activity to data
- observed and inferred signals
- ranking and relevance criteria
- attention and data as value
- feedback between outputs and behaviour
Participatory and Algorithmic Culture
user contribution, computational classification, recommendation, personalisation, representation and cultural feedbackPlatform culture combines user contribution with computational sorting. People post, click, rate and share, producing the signals through which algorithms classify and recommend. Those ranked encounters then influence later expression and taste.
- participatory contribution
- algorithmic classification
- recommendation feedback
- personalisation and prediction
- representation and exploration
News, Hyperlinks and Platform Gatekeepers
news bundling, hyperlink trails, platform feeds, editorial authority, public attention and civic relevanceNews once arrived as an editorial bundle with visible priorities. Search, hyperlinks and independent publishing unbundle that package, while platform feeds rebundle attention through profiles, prediction and infrastructure.
- editorial bundling
- hyperlink verification
- news unbundling
- platform feed selection
- public-interest relevance
Gig Work and Algorithmic Management
flexibility, information asymmetry, ratings, risk transfer, precarity, institutional context and partial autonomyGig platforms can widen entry and schedule flexibility while retaining authority over information, allocation, pricing and evaluation. This chapter uses competing metaphors to prevent one-sided analysis.
- entrepreneurial access
- information asymmetry
- rating and allocation systems
- risk transfer and precarity
- permissive platform authority
Health Data and Platform Promises
data donation, personal and collective benefit, sensing, inference, representation, consent and secondary useHealth platforms commonly join a personal promise with a collective one. Individuals receive monitoring, insight or support, while shared data are said to advance research or public health.
- personal and collective benefit
- sensing and self-reporting
- health inference and uncertainty
- data donation and consent
- secondary use and beneficiaries
Education Platforms and Public Values
learning analytics, personalisation, teacher autonomy, equality, curriculum, platform ecosystems and contestable predictionEducation platforms distribute material, monitor progress and promise personalised learning, but education is also a public institution tied to autonomy, equality, affordability and citizenship.
- learning datafication
- adaptive selection
- teacher and student autonomy
- equality and affordability
- institutional ecosystem governance
Platform Narratives and Critical Essays
liberation, market reform, data enlightenment, social construction, evidence chains, theses and counter-casesA critical essay begins with how a platform diagnoses an old problem and presents a data-driven solution.
- liberation narrative
- neoliberal market reform
- data-driven enlightenment
- social construction approach
- thesis and evidence chains
Algorithmic Ethics and Digital Disconnection
convening, probability, time, institutional norms, digital labour, unequal opt-out capacity and ethical judgementAlgorithmic ethics concerns relationships among code, institutional practices and norms. This chapter asks how systems convene groups, assign probabilities and organise time, then connects those questions to digital disengagement.
- code, practices and norms
- convening and group boundaries
- probability and intervention
- time and attention
- digital disconnection labour
It begins with data as a resource for decision-making and governance, then treats platforms as programmable intermediaries that organise relationships among users, complementors, advertisers, institutions and public authorities. The course vocabulary makes platform power concrete. Datafication translates activities and relationships into processable data.
Selection ranks content, services and people through algorithms and interfaces. Commodification captures value from attention, information, transactions or access. Participatory culture explains user contribution, while algorithmic culture explains the computational sorting that determines which contributions become visible. The course then tests those mechanisms across social sectors.
News platforms unbundle the editorial package and rebuild attention through links, profiles and ranked feeds. Gig-work platforms may expand flexible access while controlling information, allocation and evaluation. Health platforms combine personalised benefits with claims about public research, raising questions about consent, representation and secondary use.
Education platforms promise scale and personalisation while changing teacher autonomy, curriculum and equality. Later work turns toward platform narratives, social construction, algorithmic ethics and the unequal possibility of digital disconnection. Assessment is entirely coursework. Weekly Quizzes are worth 30%, the Essay on Platformization is worth 35%, and Your Data-Driven Smart Solution is worth 35%, for a total of 100%.
The quiz component requires at least five submitted worksheets to pass that component, and the highest five completed scores are aggregated. The essay rewards a coherent argument that uses course concepts, platform evidence and stakeholder analysis. The group project requires a rationale, a datafication strategy, a simple supervised-learning scenario and a critical evaluation of data limitations and bias.
The most useful study habit is to trace a complete causal chain: identify the promise, name the mechanism, locate the rule-setting power, compare stakeholder outcomes and finish with a qualified judgement.
Worked example · free
Trace a platform promise into a defensible judgement
- 1Identify students, the University, the operator and any later data recipients.
- 1Name the behaviour translated into data and the score or category produced.
- 1Explain how the score directs attention, advice or access.
- 1Compare the promised benefit with representation, consent and information limits.
- 1State a qualified judgement and a safeguard aimed at the mechanism.
Key terms
- Platform
- A programmable architecture that organises interactions among participant groups through data, interfaces, rules and business arrangements.
- Datafication
- The translation of activities, relationships or qualities into processable and comparable data.
- Commodification
- The transformation of attention, data, activity, objects or ideas into forms from which economic value can be captured.
- Selection
- The algorithmic and interface-based ordering of content, people, services or advertisements according to platform criteria.
- Algorithmic culture
- The delegation of cultural sorting, classification and hierarchy to computational processes and the habits formed around them.
- Participatory culture
- A cultural arrangement in which ordinary users can create, share and respond rather than remain only receivers.
- Multi-sided market
- A market in which a platform connects distinct participant groups whose presence creates value for one another.
- Information asymmetry
- A condition in which one actor holds decision-relevant information that another actor cannot equally access.
- Platformization
- The reorganisation of social practices and institutions around platform infrastructures, mechanisms, business models and governing rules.
- Public value
- A collectively important principle such as equality, autonomy, accountability, affordability, privacy or access.
- Gatekeeping
- The power to shape which information, people or opportunities become visible and accessible.
- Technological determinism
- The view that technology drives social outcomes largely through its own internal logic rather than social construction and context.
- Social construction
- An approach that explains technology through the groups, criteria, interests and institutions that shape its design and acceptance.
- Digital disengagement
- The attempt to reduce or interrupt digital participation, often involving additional labour, tools and unequal access to offline alternatives.
COMU1130 FAQ
How is the course assessed?
The published assessment has three components. Weekly Quizzes are worth 30%, the Essay on Platformization is worth 35%, and Your Data-Driven Smart Solution is worth 35%. The rows sum to 100%. The course materials describe coursework tasks rather than a final examination.
What is the quiz submission condition?
Students must submit at least five quiz worksheets to pass the Weekly Quizzes assessment component. The course materials say that failure to meet that requirement results in zero for the quiz component, regardless of individual worksheet scores. The highest five completed scores are aggregated.
What makes the course difficult?
The difficulty is moving beyond familiar opinions about technology. Strong work must define a concept accurately, connect it to a concrete data or interface mechanism, compare stakeholder interests and make a qualified judgement. Broad claims about platforms being good, bad or disruptive do not show the required causal analysis.
What does the critical essay require?
The essay selects one platformization question and one social sector, develops a coherent 1,800-word argument, uses at least four course concepts, references claims in APA style and examines narratives, stakeholder conflicts, democratic consequences and personal reflection. Platform marketing language should be distinguished from independent evaluation.
What should the group project explain?
The proposal should establish a real demand, identify an everyday pattern and collectable data, explain a simple supervised-learning application scenario, and critically evaluate representation, data limitations, model bias and possible harm. A useful presentation makes the chain from activity to data, prediction, action and benefit visible.
How should generative AI be handled?
Follow the permission and acknowledgement instructions for the particular task, verify every claim and source, and be prepared to explain the submission independently. The course materials warn that unreferenced use may constitute misconduct and require detailed comprehension of submitted work rather than passive acceptance of generated text.
Questions students ask in each COMU1130 chapter
- Why is a platform more than a neutral tool?
- A platform not only enables action. It selects categories, defines defaults, controls access to data and ranks visibility. Those choices organise opportunity and behaviour, so technical mediation also performs a governing role. Chapter 01 · Data, Platforms and Stakeholders
- What belongs in platform anatomy?
- A complete anatomy includes data collection, algorithmic processing, interface design, ownership, the business model and contractual rules. Their interaction explains the service more accurately than treating an algorithm as an isolated cause. Chapter 02 · Platform Anatomy and Market Sides
- How is datafication different from digitisation?
- Digitisation converts an existing item into digital form. Datafication renders aspects of life measurable and comparable, often creating new categories and behavioural traces that did not previously operate as formal data. Chapter 03 · Datafication, Commodification and Selection
- Does participation give users cultural control?
- Participation gives users the capacity to contribute, but platforms still classify and rank contributions. Control over expression and control over circulation are related but distinct forms of power. Chapter 04 · Participatory and Algorithmic Culture
- What does unbundling mean for news?
- Readers can reach a single story through search or links without encountering the outlet's full editorial package. This expands navigation while weakening the newsroom's control over sequence, context and revenue. Chapter 05 · News, Hyperlinks and Platform Gatekeepers
- Why is flexibility not the opposite of control?
- A worker may choose when to log in while the platform still sets prices, withholds job information, ranks performance and controls access. Autonomy and dependence operate across different decisions. Chapter 06 · Gig Work and Algorithmic Management
- What is the double promise of health platforms?
- The service promises an individual benefit such as insight or monitoring and a collective benefit such as research. Critical analysis asks whether the same data flow actually serves both and who captures later value. Chapter 07 · Health Data and Platform Promises
- Why is privacy not the only education issue?
- Platforms also influence curriculum, teacher discretion, affordability, performance categories and institutional dependence. Privacy matters, but it sits within a wider struggle over what education is for and who governs it. Chapter 08 · Education Platforms and Public Values
- What should the essay thesis contain?
- It should name the platform narrative, explain the mechanism through which it operates, compare stakeholder consequences and state the condition under which the promise holds or fails. This gives each body section analytical work. Chapter 09 · Platform Narratives and Critical Essays
- Why is algorithmic ethics relational?
- An outcome depends on code, organisational routines, professional choices and social norms. Ethical analysis therefore asks how these elements combine rather than locating responsibility in a mathematical procedure alone. Chapter 10 · Algorithmic Ethics and Digital Disconnection
How to prepare for the assessments
Use three linked study modes. First, build a compact mechanism glossary in your own words: platform anatomy, datafication, selection, commodification, participatory culture, algorithmic culture, multi-sided markets and public values. For each term, record one verb that makes it observable, such as ranks, prices, verifies, predicts or withholds.
Second, practise weekly quiz responses in a claim, concept, application and judgement pattern. The published criterion rewards correct understanding and clear application, so every definition should be tied to a practical feature and consequence. Third, prepare assessments cumulatively.
For the essay, collect evidence under five headings: diagnosed sector problem, platform promise, operational mechanism, stakeholder conflict and consequence. Draft a qualified thesis before writing paragraphs, then test it against the strongest plausible benefit. For the group project, keep the data chain concrete: everyday activity, data field, labelled example, supervised-learning output, user action and promised benefit.
Add representation and consent checks at the design stage rather than at the end. Across all tasks, distinguish company language from your own evaluation, use evidence for each link, and replace vague claims about power with precise mechanism verbs. This makes revision faster and keeps ethical recommendations connected to the actual system being analysed.