Task 1: Big Data Visualisation & Challenges (20 Marks, 1000 Words, CLO2 & CLO3) Task 1.1 (10 Marks, 500 Words): Explain, with an example from your data visualisation project, how visualisation concepts help identify trends and patterns

Assessment 2 Problem Solving (Data Visualisation Project Progress Report & Individual Reflective Journal)

This document provides all information about the assessment requirements, including detailed instructions, resources, and the Criterion Reference Assessment (CRA) Rubric used for grading.

Task Overview

Assessed Course Learning Outcomes (CLOs):

• CLO2: Assess and adapt contemporary and innovative data visualisation approaches to gain insight and value that support effective data-driven decision-making.
• CLO3: Appraise and present complex and big data in visual form that is readily understandable by specialist and non-specialist audiences using data visualisation methods, tools, and techniques.
• CLO4: Interact and collaborate effectively in teams to design and implement a data visualisation project.

Task Rationale

Assessment 2: Problem Solving (Project Report & Individual Journal) involves:

• Reporting the progress of the team in completing a data visualisation project.
• Maintaining an individual reflective journal documenting the effectiveness of interaction and collaboration of team members.

Purpose:
Completing this assessment enables students to develop as well-informed individuals who are:

• Critical and creative thinkers
• Effective communicators and collaborators
• Employable and enterprising professionals in their chosen discipline

Task Instructions

Task 1: Big Data Visualisation & Challenges (20 Marks, 1000 Words, CLO2 & CLO3)

• Task 1.1 (10 Marks, 500 Words): Explain, with an example from your data visualisation project, how visualisation concepts help identify trends and patterns in your dataset to support organisational decision-making.
• Task 1.2 (10 Marks, 500 Words): Discuss challenges organisations face in undertaking a big data visualisation initiative. Highlight strategies to address data privacy and security for sensitive data in your dataset.

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Task 2 – Big Data Visualisation Key Principles, Approaches & Data Modelling (20 Marks, 900 Words, CLO2)

• Discuss key principles and approaches your team will use to design the Big Data Visualisation project.
• Emphasise the importance of data modelling in designing an effective dashboard, providing an example from your project.

Task 3: Big Data Visualisation Project Power BI (30 Marks, 500 Words, CLO4)

• Task 3.1 (10 Marks, 250 Words): Provide a data dictionary (Variable, Data Type, Description) for the dataset used. Include Table 3.1 captioned as “Data Dictionary (File Name)” and explain the purpose of each variable.
• Task 3.2 (20 Marks, 250 Words): Describe your planned Power BI dashboard including four preliminary views. Include screenshots and submit the packaged Power BI workbook (.pbix).

Task 4 – Individual Reflective Journal, Peer Review & Active MS Teams Participation Each team member must maintain a journal listing activities, dates, duration, type of activity, and how participation contributed to completing Assessment 2.

• Evidence must reflect individual participation and MS Teams discussions.
• Collaborative team contribution is mandatory; marks may be reduced for inactivity.

Report Structure

1. Cover Page
2. Title & Table of Contents
3. Body of Report with relevant Task headings and sub-headings:
   • Task 1
   • Task 2
   • Task 3
   • Task 4
4. References
5. Appendices

Presentation Guidelines:

• Font: 12 pt Times New Roman
• Line spacing: 1.5
• Tables & Figures with captions
• Clear, concise writing targeting middle to senior management
• Free from spelling & grammatical errors
• Referencing: Harvard AGPS style (in-text citations & full reference list)
• Note: Reference list and Appendices are not included in the word count.

Task Snapshot

• Length: 2800 Words (+/- 10%) excluding references & appendices
• Weighting: 35%
• Marks: 100

Acceptable AI Use Level

• Level 1 – AI Assisted Structure Checking: AI tools may be used for grammar, coherence, and flow checks.
• Prohibited: Using AI to generate new content.
• Requirement:
  • Indicate AI tools used at the start of the assessment.
  • Include prompts used.
  • Save pre- and post-AI draft copies for verification.

Academic Integrity

• Students must follow UniSQ Academic Integrity policies.
• Original work is required; collaboration outside your group is only allowed for clarification.
• Prohibited: Copying from other students, tutors, or AI tools to produce content.

Assessment Marking

• Refer to the Assessment 2 CRA Rubric for detailed grading criteria.

Submission Instructions

• Team Leader: Submit:
  1. Report (.docx) for Tasks 1, 2, 3
  2. Power BI workbook (.pbix) with four preliminary views
• Individual Team Members: Submit Assessment 2 Journal.xlsx

File Naming Convention:

Turnitin

• Automatic plagiarism checking upon submission.
• Originality report indicates text matches in the Turnitin database; all matches must be reviewed.
• Editing is not possible after submission.

Moderation

• All assessing staff discuss and compare judgements before final marks are released.

Assessment Policies & Procedures

• Information on extensions, late submissions, academic integrity, and marking is available on the USQ StudyDesk Assessment page.
• Extensions are granted only under compassionate or compelling circumstances according to USQ procedures.

Referencing / Citations

• Use Harvard AGPS style
• Minimum 10 scholarly sources, majority no older than 5 years
• Include in-text citations for all points or evidence

Key Resources

• Study Modules 1–6
• Assessment 2 Forum
• Assessment 2 Walkthrough Recording
• Active MS Teams collaboration

Brief Summary of Requirements

Assessment Name: Problem Solving – Data Visualisation Project Progress Report & Individual Reflective Journal

Purpose:

The assessment aims to evaluate students’ ability to plan, implement, and reflect on a Big Data Visualisation project, demonstrating teamwork, individual contribution, and the use of data visualisation tools like Power BI.

Key Tasks & Requirements:

1. Task 1 – Big Data Visualisation & Challenges (1000 Words, 20 Marks)
   • Explain how visualisation identifies trends/patterns to support decision-making (500 words).
   • Discuss challenges in big data visualisation, including data privacy and security (500 words).
2. Task 2 – Key Principles, Approaches & Data Modelling (900 Words, 20 Marks)
   • Discuss data visualisation principles and approaches used in the project.
   • Highlight importance of data modelling with examples for dashboard design.
3. Task 3 – Power BI Project (500 Words, 30 Marks)
   • Task 3.1: Provide a data dictionary with variable descriptions and purposes (250 words).
   • Task 3.2: Describe planned Power BI dashboard, including four preliminary views with screenshots (250 words).
4. Task 4 – Individual Reflective Journal, Peer Review & MS Teams Participation (400 Words, 30 Marks)
   • Maintain a personal journal documenting contributions, dates, duration, activity type, and participation.
   • Peer review and active MS Teams collaboration are mandatory.

Report Structure & Presentation:

• Cover page, Table of Contents, Task-based headings, References, Appendices
• Font: 12 pt Times New Roman, Line spacing: 1.5, Tables/Figures with captions
• Harvard AGPS referencing style

Assessment Details:

• Length: 2800 words (+/- 10%), Weight: 35%, Marks: 100
• Acceptable AI use: Structure checking only (no content generation)
• Academic integrity must be maintained; Turnitin plagiarism checking applies