Practical Demonstration | UQ Assessment Ideas Factory

Underpinned by the principles of authentic assessment, this task gives students an opportunity to be a ‘business analyst’ responding to an urgent email request from a manager. Students receive a previously unseen R dataset and a workplace-style briefing and must deliver a concise data summary and analytical report that informs a business decision for consideration by a client. The task simulates the type of evidence-based analysis commonly undertaken in professional analytics roles. It is completed individually under invigilated exam conditions, requiring students to independently demonstrate their analytical capabilities while working within realistic workplace time constraints.

This secure assessment builds directly on a previously submitted assessment task (Predictive Analytics Report) and a sequence of scaffolded learning activities throughout the semester in which students progressively develop and practise the required skills. Through tutorials, practical workshops and guided exercises, students repeatedly engage in data preparation, visualisation, predictive modelling and professional reporting. By the time students undertake the practical demonstration, they have rehearsed the workflow in authentic contexts, enabling them to transfer these skills to an unfamiliar dataset under exam conditions.

Reflected in the criteria sheet, students are assessed on their ability to summarise data using descriptive statistics, develop effective visualisations, build and interpret a predictive model, and communicate actionable insights in a concise professional report. These criteria mirror the work of business analysts who must rapidly analyse unfamiliar data, justify their analytical decisions, and communicate findings to support organisational decision-making.

Advantages

This assessment provides an authentic learning experience by placing students in the role of a business analyst responding to a workplace briefing. It also promotes an inclusive assessment approach which is achieved by focusing on the application of knowledge, skills, and professional judgement rather than memorisation and high-pressure recall. Drawing on familiar tools, workflows, and supports developed throughout the semester helps reduce exam anxiety and enables students to demonstrate achievement in ways that better reflect contemporary professional practice.

Challenges

Additional preparation is needed to create authentic business scenarios and develop suitable unseen datasets.

Tips for implementation

Scaffold the Practical Demonstration by providing students with a sequence of learning activities and an earlier authentic assessment, such as a Predictive Analytics Report.
Give students opportunities to practise and rehearse the complete analytics workflow through realistic business scenarios, a consistent reporting structure, and activities that mirror the conditions and processes of the final invigilated assessment.
Model and clearly communicate expectations regarding the use of digital and AI-enabled tools in professional practice. Employers expect analytics projects to be deployed using advanced AI tools embedded directly within the development environment (for example, GitHub Copilot for inline autocompletion inside R Studio or Microsoft Visual Studio Code).

Learning outcomes

LO3. Apply R for basic business analytics tasks, including data visualisation and predictive analytics.
LO4. Design an analytics approach by choosing the correct analytics tools for a given business problem.
LO5. Demonstrate how business analytics can inform and improve managerial decision making.

How it supports academic integrity

Students complete the task individually under invigilated exam conditions using an unseen dataset, providing strong evidence of independent achievement. Although basic AI chat tools are permitted for editing/coding of outputs, the task evaluates practical skills related to data exploration, predictive analytics, adaptability, and proficiency in using real-world tools under time constraints. Students are assessed on their ability to apply analytical judgement, problem-solving, and decision-making in response to AI generated outputs.

PLEASE NOTE: The academic integrity information displayed on this page is currently under review. Some examples and descriptions were developed before the widespread availability of generative AI tools and may not reflect current approaches to assessment security. When adapting an assessment idea, staff should consider how the design supports authorship, verifies student achievement of learning outcomes, and mitigates inappropriate use of AI and other forms of academic misconduct.

Details

CLASS SIZE: 100-500
YEAR LEVEL: Second year
ASSESSMENT SECURITY: Secure
SUPERVISION STATUS: Supervised – in-person
SUBMISSION MEDIUM: Digital product
ASSESSMENT DELIVERY MODE: Practical / demonstration
INTEGRITY CONTROLS: Identity verification
AUTHENTICITY CONTEXT: Work‑integrated / Work‑related
GRADUATE ATTRIBUTES: Courageous thinkers, Influential communicators
CONDITIONS: AI Required, Time limited
TAGS: exam conditions, authentic, inclusive, application