CQUniversity Unit Profile
COIT20253 Business Intelligence using Big Data
Business Intelligence using Big Data
All details in this unit profile for COIT20253 have been officially approved by CQUniversity and represent a learning partnership between the University and you (our student).
The information will not be changed unless absolutely necessary and any change will be clearly indicated by an approved correction included in the profile.
General Information

Overview

Big data is a popular term used to describe the exponential growth and availability of structured and unstructured data. In this unit, you will explore big data within the context of business intelligence. In this unit, you will learn concepts of business intelligence, alignment of big data to business intelligence and how big data technologies can be used in building organisational business intelligence. You will learn how big data is changing businesses and how organisations can take advantage of big data in decision making. You will learn how organisations are integrating non-traditional unstructured data with the traditional structured enterprise data to do the business intelligence analysis. In order to understand these, you will learn big data analytical tools and technologies to help solve authentic business problems and make effective business decisions.

Details

Career Level: Postgraduate
Unit Level: Level 9
Credit Points: 6
Student Contribution Band: 8
Fraction of Full-Time Student Load: 0.125

Pre-requisites or Co-requisites

Prerequisites: COIT20250 e-Business Systems, COIT20245 Introduction to Programming and COIT20247 Database Design and Development. Anti-Requisites: If you have completed unit COIT20236 then you cannot take this unit.

Important note: Students enrolled in a subsequent unit who failed their pre-requisite unit, should drop the subsequent unit before the census date or within 10 working days of Fail grade notification. Students who do not drop the unit in this timeframe cannot later drop the unit without academic and financial liability. See details in the Assessment Policy and Procedure (Higher Education Coursework).

Offerings For Term 2 - 2018

Brisbane
Distance
Melbourne
Sydney

Attendance Requirements

All on-campus students are expected to attend scheduled classes – in some units, these classes are identified as a mandatory (pass/fail) component and attendance is compulsory. International students, on a student visa, must maintain a full time study load and meet both attendance and academic progress requirements in each study period (satisfactory attendance for International students is defined as maintaining at least an 80% attendance record).

Class and Assessment Overview

Recommended Student Time Commitment

Each 6-credit Postgraduate unit at CQUniversity requires an overall time commitment of an average of 12.5 hours of study per week, making a total of 150 hours for the unit.

Class Timetable

Bundaberg, Cairns, Emerald, Gladstone, Mackay, Rockhampton, Townsville
Adelaide, Brisbane, Melbourne, Perth, Sydney

Assessment Overview

1. Written Assessment
Weighting: 35%
2. Presentation
Weighting: 25%
3. Practical and Written Assessment
Weighting: 40%

Assessment Grading

This is a graded unit: your overall grade will be calculated from the marks or grades for each assessment task, based on the relative weightings shown in the table above. You must obtain an overall mark for the unit of at least 50%, or an overall grade of ‘pass’ in order to pass the unit. If any ‘pass/fail’ tasks are shown in the table above they must also be completed successfully (‘pass’ grade). You must also meet any minimum mark requirements specified for a particular assessment task, as detailed in the ‘assessment task’ section (note that in some instances, the minimum mark for a task may be greater than 50%). Consult the University’s Grades and Results Policy for more details of interim results and final grades.

Previous Student Feedback

Feedback, Recommendations and Responses

Every unit is reviewed for enhancement each year. At the most recent review, the following staff and student feedback items were identified and recommendations were made.

Feedback from Have Your Say Moodle Evaluation.

Feedback

Lectures are clear and knowledge passed is useful and contextualised for real case scenarios. Help and support is provided.

Recommendation

Keep providing the support and delivering useful and contextualised knowledge to our students' and continue to help them in their journey to the completion of the course. Continue with the good practices of teaching.

Feedback from Have Your Say Moodle Evaluation.

Feedback

More practical use of Hadoop Application.

Recommendation

More examples of Hadoop applications will be provided. The unit contents are enhanced on a regular basis to fit version changes of Hadoop.

Unit Learning Outcomes
On successful completion of this unit, you will be able to:
  1. Apply concepts and principles of big data to evaluate and explain how large volume of structured and unstructured data are managed in an organisation
  2. Analyse critically and reflect on how organisations are including non-traditional valuable data with the traditional enterprise data to do the business intelligence analysis
  3. Critically analyse and evaluate different big data technologies used for decision making in an organisation
  4. Develop big data strategy for data-centric organisations to meet client requirements
  5. Apply big data architecture, tools, and technologies for decision making and problem solving in the organisational context.

Australian Computer Society (ACS) recognises the Skills Framework for the Information Age (SFIA). SFIA is in use in over 100 countries and provides a widely used and consistent definition of ICT skills. SFIA is increasingly being used when developing job descriptions and role profiles.

ACS members can use the tool MySFIA to build a skills profile at https://www.acs.org.au/professionalrecognition/mysfia-b2c.html

This unit contributes to the following workplace skills as defined by SFIA. The SFIA code is included:

  • Research(RSCH)
  • Data Management (DATM)
  • Emerging Technology Monitoring (EMRG)
  • Data Analysis (DTAN)
  • Application Support (ASUP) 
  • Analytics (INAN)

Alignment of Learning Outcomes, Assessment and Graduate Attributes
N/A Level
Introductory Level
Intermediate Level
Graduate Level
Professional Level
Advanced Level

Alignment of Assessment Tasks to Learning Outcomes

Assessment Tasks Learning Outcomes
1 2 3 4 5
1 - Written Assessment - 35%
2 - Presentation - 25%
3 - Project (applied) - 40%

Alignment of Graduate Attributes to Learning Outcomes

Graduate Attributes Learning Outcomes
1 2 3 4 5
1 - Knowledge
2 - Communication
3 - Cognitive, technical and creative skills
4 - Research
5 - Self-management
6 - Ethical and Professional Responsibility
7 - Leadership

Alignment of Assessment Tasks to Graduate Attributes

Assessment Tasks Graduate Attributes
1 2 3 4 5 6 7
1 - Written Assessment - 35%
2 - Presentation - 25%
3 - Project (applied) - 40%
Textbooks and Resources

Textbooks

Supplementary

Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today's Businesses latest (2013)

Authors: Minelli Michael, Dhiraj Ambiga, Chambers Michele
Wiley CIO Series
Hoboken, New Jersey, Hoboken, New Jersey, , USA
ISBN: ISBN 978-1-118-14760-3
Binding: eBook
Supplementary

Big Data: Understanding How Data Powers Big Business latest (2013)

Authors: Schmarzo, Bill
Wiley
Crosspoint Boulevard, Indianapolis Crosspoint Boulevard, Indianapolis , USA
ISBN: 978-1-118-73957-0
Binding: eBook
Supplementary

Business Intelligence Guidebook: From Data Integration to Analytics 1st edition (2014)

Authors: Rick Sherman
Morgan Kaufman
ISBN: ISBN-13: 978-0124114616
Binding: eBook
Supplementary

Business Intelligence Strategy and Big Data Analytics latest (2016)

Authors: Steve Williams
Elsevier Science;
Publisher ID 36487 Publisher ID 36487
ISBN: ISBN 9780128094891
Binding: eBook
Supplementary

Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die 2016 (2017)

Authors: E. Siegel
Wiley
ISBN:
Binding: Paperback

Additional Textbook Information

Conference/ Journal Articles:

Students are encouraged to read peer reviewed journal articles and conference papers. Google Scholar provides a simple way to broadly search for scholarly literature. From one place, you can search across many disciplines and sources: articles, theses, books, abstracts and court opinions, from academic publishers, professional societies, online repositories, universities and other web sites. Please visit CQU Library at https://www.cqu.edu.au/student-life/library and start searching for books, journal articles and more.

IT Resources

You will need access to the following IT resources:
  • CQUniversity Student Email
  • Internet
  • Unit Website (Moodle)
  • Hadoop (requires 8 GB RAM)
  • Oracle VM Virtual Box
  • Use Talend Platform for Big Data integration (30 days trial is free) http://www.talend.com/products/big-data
Referencing Style

All submissions for this unit must use the referencing style: Harvard (author-date)

For further information, see the Assessment Tasks.

Teaching Contacts
Meena Jha Unit Coordinator
m.jha@cqu.edu.au
Schedule
Week 1 Begin Date: 09 Jul 2018

Module/Topic

An Overview of Business Intelligence, Analytics, and Decision Support, Introduction to Big Data.

Chapter

CRO provided on Moodle.

Chapter 1 from Big Data, Big Analytics : Emerging Business Intelligence and Analytic Trends for Today's Businesses. Authors: M Minelli, M Chambers, a Dhiraj.

Chapter 1 from Business Intelligence and Analytics: Systems for Decision Support. Authors: R Sharda, D Delen, E Turban

Events and Submissions/Topic

Week 2 Begin Date: 16 Jul 2018

Module/Topic

Foundation and Technologies for Decision Making: conceptual foundations of decision-making; Simon's four phases of decision-making; different types of DSS classifications.

Industry Examples of Big Data

Chapter

Chapter 2 from Business Intelligence and Analytics: Systems for Decision Support. Authors: R Sharda, D Delen, E Turban

Chapter 2 from Big Data, Big Analytics : Emerging Business Intelligence and Analytic Trends for Today's Businesses. Authors: M Minelli, M Chambers, a Dhiraj.


Events and Submissions/Topic

Week 3 Begin Date: 23 Jul 2018

Module/Topic

Big Data Technologies: Overview of Hadoop; MapReduce; scripting language.

Chapter

Chapter 3 from Big Data, Big Analytics : Emerging Business Intelligence and Analytic Trends for Today's Businesses. Authors: M Minelli, M Chambers, a Dhiraj.

Events and Submissions/Topic

Week 4 Begin Date: 30 Jul 2018

Module/Topic

Information Management and Business Reporting, Visual Analytics, Business Performance Management: business reporting and its historical evolution; importance of data/information visualization; different types of visualization techniques; value visual analytics brings to BI/BA; big data computation; big data computational limitations

Chapter

Chapter 4 from Big Data, Big Analytics : Emerging Business Intelligence and Analytic Trends for Today's Businesses. Authors: M Minelli, M Chambers, a Dhiraj.

Chapter 4 from Business Intelligence and Analytics: Systems for Decision Support. Authors: R Sharda, D Delen, E Turban

Events and Submissions/Topic

Week 5 Begin Date: 06 Aug 2018

Module/Topic

Predictive Modeling: classification versus regression; evaluating predictive models and cross validation; algorithms for predictive modelling.

Chapter

Chapter 6 from Business Intelligence and Analytics: Systems for Decision Support. Authors: R Sharda, D Delen, E Turban

Events and Submissions/Topic

Vacation Week Begin Date: 13 Aug 2018

Module/Topic

Revise all lectures and tutorials

Chapter


Events and Submissions/Topic

Week 6 Begin Date: 20 Aug 2018

Module/Topic

Business Analytics, Text Analytics, Text Mining, and Sentiment Analysis: need for text mining; differentiate between text mining, web mining and data mining; different application areas for text mining. Understanding decision theory business intelligence and analytics from big data to big impacts.

Chapter

Chapter 5 from Big Data, Big Analytics : Emerging Business Intelligence and Analytic Trends for Today's Businesses. Authors: M Minelli, M Chambers, a Dhiraj.

Chapter 7 from Business Intelligence and Analytics: Systems for Decision Support. Authors: R Sharda, D Delen, E Turban

Events and Submissions/Topic

Assessment 1: Written Assessment Due: Week 6 Monday (20 Aug 2018) 11:45 pm AEST
Week 7 Begin Date: 27 Aug 2018

Module/Topic

Web Analytics, Web Mining, and Social Analytics: Understand the Internets of Web search; Web usage mining and learn its business application.Creating Big Data Strategy: Identify end goal; proven big data strategy; big data strategy document;

Chapter

CRO provided on Moodle

Chapter 8 from Business Intelligence and Analytics: Systems for Decision Support. Authors: R Sharda, D Delen, E Turban

Events and Submissions/Topic

Week 8 Begin Date: 03 Sep 2018

Module/Topic

Prescriptive Analytics and Model Based Decision Making Optimization and Multi-Criteria Systems: Concepts of analytical decision modelling; interaction of prescriptive model with data and the user; key issues of multi criteria decision making;

Chapter

Chapter 9 from Business Intelligence and Analytics: Systems for Decision Support. Authors: R Sharda, D Delen, E Turban

Events and Submissions/Topic

Week 9 Begin Date: 10 Sep 2018

Module/Topic

Modelling and Analysis: Heuristic Search Methods and Simulation: problem solving search methods; simulation; visual interactive simulation.

Chapter

Chapter 10 from Business Intelligence and Analytics: Systems for Decision Support. Authors: R Sharda, D Delen, E Turban

Events and Submissions/Topic

Assessment 2: Presentation Due: Week 9 Monday (10 Sept 2018) 11:45 pm AEST
Week 10 Begin Date: 17 Sep 2018

Module/Topic

Automated Decision Systems and Expert Systems: era of big data artificial intelligence and information systems in an organization

Chapter

CRO provided on Moodle

Chapter 11 from Business Intelligence and Analytics: Systems for Decision Support. Authors: R Sharda, D Delen, E Turban

Events and Submissions/Topic

Week 11 Begin Date: 24 Sep 2018

Module/Topic

Big Data Reference Architecture and Patterns for Analytics: relational vs non relational architecture; case studies; tips for designing big data solutions .

Chapter

CRO provided on Moodle.


Events and Submissions/Topic

Week 12 Begin Date: 01 Oct 2018

Module/Topic

Cloud Computing and Business Intelligence: emerging trends and future impacts of business analytic.

Chapter

Chapter 14 from Business Intelligence and Analytics: Systems for Decision Support. Authors: R Sharda, D Delen, E Turban

Events and Submissions/Topic

Assessment 3: Practical and Written Assessment Due: Week 12 Friday (5 Oct 2018) 11:45 pm AEST
Review/Exam Week Begin Date: 08 Oct 2018

Module/Topic

This unit does not have exam.

Chapter

Events and Submissions/Topic

Exam Week Begin Date: 15 Oct 2018

Module/Topic

Chapter

Events and Submissions/Topic

Term Specific Information

Contact information for Dr Meena Jha: Email: m.jha@cqu.edu.au Office: Level 2, 400 Kent Street, Sydney Campus; P +61 2 9324 5776 | X 55776. Please submit questions about the unit through the 'Q&A' discussion forum in Moodle - that way, everyone can benefit from the questions and answers. If you have any individual queries, please email Meena Jha on m.jha@cqu.edu.au and you will be replied within a day or so.

Assessment Tasks

1 Written Assessment

Assessment Title
Assessment 1: Written Assessment

Task Description

 Business Intelligence (BI) technologies provide historical, current, and predictive views of business operations based on the collection, extraction, and analysis of business data. More recently, “Big Data” and “Big Data Analytics” have further stirred the interest of researchers and practitioners alike. You have been requested to prepare a report focusing on one of the following topics:

1. Big Data for Supply Chain and Operations Management

2. Sports Analytics

3. Agricultural Analytics

4. Fraud Detection in Banking Sector

5. Big Data for Sentiment Analysis

The report should be well researched and written in accordance with Harvard referencing style.The assignment will be marked out of a total of 100 marks and forms 35% of the total assessment for the unit. ALL assignments will be checked for plagiarism by Turnitin. You have been requested to prepare a report. Your target audience is business executives, who have extensive business experience but limited ICT knowledge. They would like to be informed as to how new Big Data technologies may be beneficial for their business. Please note that standard report structure, including an executive summary, must be adhered to.

The main body of the report should include the following topics.

1. Data Collection and Storage

  • Data collection system (what kind of data should be collected and how)
  • Storage system (what are the requirements of the storage and how to achieve them)

2. Data in Action

  • Consumer-centric product design (what is it and how to do it)
  • Recommendation system (what is it and how to do it)

3. Business continuity

  • How online business can survive in case of power outage or other disasters?

This is an individual assessment. The length of the assignment is 3000 words. You are required to do extensive reading of more than 10 appropriate and relevant chosen topics in Big Data application. Please do in-text referencing of all chosen readings. Newspaper and magazine reports should be limited to a maximum of 2. A comprehensive report covering all key aspects of the topic selected is required. Report should be extremely well supported with relevant case studies. Any assumptions made are clearly noted. DO NOT use Wikipedia as a reference. The use of unqualified references will result in the deduction of marks.

The report structure should be clear, easy to read and logical, directly addressing the questions. Suitable headers should be used throughout the report. Good use of graphics and charts should be made.

No spelling, punctuation or grammatical errors. In this assessment, you should aim to make use of recent primary journal articles and case reports, to make your work as up-to-date as possible and to obtain the best possible mark.


Assessment Due Date

Week 6 Monday (20 Aug 2018) 11:45 pm AEST


Return Date to Students

Week 8 Monday (3 Sept 2018)

Assignment 1 feedback will be made available to students.


Weighting
35%

Assessment Criteria

Assessment Marking Criteria: Weighted out of 35%

  1. Report formatting (font, header and footer, table of content, numbering, referencing) 5 Marks
  2. Professional communication (correct spelling, grammar, formal business language used) 5 Marks
  3. Executive summary 10 Marks
  4. Report introduction 10 Marks
  5. Data Collection and Storage 20 Marks
  6. Data in Action 30 Marks
  7. Business Continuity 10 Marks
  8. Conclusion and Recommendations 10 Marks

Total = 100.00


Referencing Style

Submission
Online

Submission Instructions
All assignments must be submitted to Moodle for marking.

Learning Outcomes Assessed
  • Identify and describe the principles and concepts of big data.
  • Evaluate and explain how large volume of structured and unstructured data are managed in an organization.
  • Examine how big data can be aligned to business intelligence for decision making.
  • Assess how organizations are including non-traditional valuable data with the traditional enterprise data to do the business intelligence analysis.
  • Evaluate and report the role of Knowledge Management Systems to support knowledge creation, gathering and sharing.
  • Effectively communicate business information needs and construct professional reports.


Graduate Attributes
  • Knowledge
  • Communication
  • Cognitive, technical and creative skills
  • Research
  • Self-management
  • Ethical and Professional Responsibility

2 Presentation

Assessment Title
Assessment 2: Presentation

Task Description

This is an individual presentation. This assessment contributes to 25% of the total marks in this unit. This presentation is associated with the Assessment 3. This assessment requires you to create Big Data strategy document. You will propose a research study that will involve investigating and developing Big Data Strategy for driving productivity. You are required to discuss your Big Data use case in tutorial class for feedback and approval by the lecturer/ Tutor in week 7.

The final presentation would be of about 15-minute duration and it will start from week 9 in tutorials. All students are required to give the presentation. Zoom Meeting will be organised by Unit Coordinator for DISTANCE Learning students.

You are required to choose any one of the following topics for presentation.

1. Big data and new decision-making techniques/models/approaches;

2. Organisational and cultural issues of the ‘Data-driven’ organisation;

3. Leveraging Big Data for enhancing decision making and creating new business models

4. Social networks for exploiting knowledge or creating intelligence;

You are required to give a presentation on how to create a Big Data Strategy and turning the strategy document into action. It is very important for all students to meet the due date of their respective presentation. Presentation will be assessed during the presentation time. You should focus on how to create a Big Data Strategy and turning the strategy document into action and the required Big Data technology.


Assessment Due Date

Week 9 Monday (10 Sept 2018) 11:45 pm AEST


Return Date to Students

Week 11 Monday (24 Sept 2018)


Weighting
25%

Assessment Criteria

Marking criteria for evaluating the Presentation:weighted 25%

1. Subject Knowledge (5 marks)

2. Explanations from evidence (5 marks)

3. Graphics, figures, tables included (5 marks)

4. Conclusions (5 marks)

5. Questions (5 marks)


Referencing Style

Submission
Online

Submission Instructions
All presentation in ppt should be submitted to Moodle.

Learning Outcomes Assessed
  • Identify and describe the principles and concepts of big data.
  • Evaluate and explain how large volume of structured and unstructured data are managed in an organization.
  • Examine how big data can be aligned to business intelligence for decision making.
  • Assess how organizations are including non-traditional valuable data with the traditional enterprise data to do the business intelligence analysis.
  • Evaluate and report the role of Knowledge Management Systems to support knowledge creation, gathering and sharing.
  • Effectively communicate business information needs and construct professional reports.


Graduate Attributes
  • Knowledge
  • Communication
  • Cognitive, technical and creative skills
  • Research
  • Self-management
  • Ethical and Professional Responsibility

3 Practical and Written Assessment

Assessment Title
Assessment 3: Practical and Written Assessment

Task Description

The assignment will be marked out of a total of 100 marks and forms 40% of the total assessment for the unit. Business Intelligence (BI) technologies provide historical, current, and predictive views of business operations based on the collection, extraction,and analysis of business data to improve decision has been the basis of several studies.

In this assignment you are required to write a research report focusing on one of the following topics (which you have already chosen for presentation and have discussed with your tutor)

1. Big data and new decision-making techniques/models/approaches;

2. Organisational and cultural issues of the ‘Data-driven’ organisation;

3. Leveraging Big Data for enhancing decision making and creating new business models

4. Social networks for exploiting knowledge or creating intelligence;

The report should be well researched. Business strategy should be mapped clearly to business initiatives, objectives and tasks. You are required to define technology stack, data sources, analytics architecture, and Master Data Management (MDM). You are required to address and outline the role social media plays in organisations during decision making process. You must focus and address the issues related to technological requirements to integrate social media data with traditional data sources. You are required to discuss Big Data Value creation process. The report should address the following:

1. Identify, create and discuss Business Strategy for a Big Data use case

2. Identify and align business initiatives, objectives and tasks with the developed Business Strategy.

3. Identify and discuss the required Technology Stack

4. Discuss Data Analytics and MDM to support DS&BI

5. Discuss support of NoSQL for Big Data Analytics.

6. Discuss different NoSQL Databases and its use in Big Data use case you have chosen.

7. Role of Social media in organisation's decision making process

8. Discuss Big Data Value creation process.

The length of the assignment is 3000 words. You are required to do extensive reading of more than 10 appropriate and relevant chosen topics in Big Data use case. Please do in-text referencing of all chosen readings. Newspaper and magazine reports should be limited to a maximum of 2. A comprehensive report covering all key aspects of the topic selected is required. Report should be extremely well supported with relevant case studies. Any assumptions made are clearly noted. The report structure should be clear, easy to read and logical, directly addressing the questions. Suitable headers should be used throughout the report. Good use of graphics and charts should be made. No spelling, punctuation or grammatical errors.


Assessment Due Date

Week 12 Friday (5 Oct 2018) 11:45 pm AEST


Return Date to Students

Exam Week Monday (15 Oct 2018)

On certification date as this unit does not have exam.


Weighting
40%

Assessment Criteria

Marking Criteria: Weighted out of 40%

1. Introduction (5 marks)

2. Identify, create and discuss business strategy for a Big Data use case. (10 marks)

3. Identify and align business initiatives, objectives and tasks with the developed business strategy. (10 marks)

4. Identify and discuss the required technology stack. (10 marks)

5. Discuss data analytics and MDM to support DS&BI. (10 marks)

6. Discuss support of NoSQL for big data analytics. (10 marks)

7. Discuss different NoSQL databases and its use in big data use case you have chosen.(10 marks)

8. Role of social media and human elements in organisations decision making process.(10 marks)

9. Discuss big data value creation process.(5 marks)

10. Conclusion (5 marks)

11. Quality of information (5 marks)

12. Grammar usage (5 marks)

13. References used (5 marks)


Referencing Style

Submission
Online

Submission Instructions
All assignments must be submitted to Moodle for marking.

Learning Outcomes Assessed
  • Identify and describe the principles and concepts of big data.
  • Evaluate and explain how large volume of structured and unstructured data are managed in an organization.
  • Examine how big data can be aligned to business intelligence for decision making.
  • Assess how organizations are including non-traditional valuable data with the traditional enterprise data to do the business intelligence analysis.
  • Evaluate and report the role of Knowledge Management Systems to support knowledge creation, gathering and sharing.
  • Effectively communicate business information needs and construct professional reports.


Graduate Attributes
  • Knowledge
  • Communication
  • Cognitive, technical and creative skills
  • Research
  • Self-management
  • Ethical and Professional Responsibility

Academic Integrity Statement

As a CQUniversity student you are expected to act honestly in all aspects of your academic work.

Any assessable work undertaken or submitted for review or assessment must be your own work. Assessable work is any type of work you do to meet the assessment requirements in the unit, including draft work submitted for review and feedback and final work to be assessed.

When you use the ideas, words or data of others in your assessment, you must thoroughly and clearly acknowledge the source of this information by using the correct referencing style for your unit. Using others’ work without proper acknowledgement may be considered a form of intellectual dishonesty.

Participating honestly, respectfully, responsibly, and fairly in your university study ensures the CQUniversity qualification you earn will be valued as a true indication of your individual academic achievement and will continue to receive the respect and recognition it deserves.

As a student, you are responsible for reading and following CQUniversity’s policies, including the Student Academic Integrity Policy and Procedure. This policy sets out CQUniversity’s expectations of you to act with integrity, examples of academic integrity breaches to avoid, the processes used to address alleged breaches of academic integrity, and potential penalties.

What is a breach of academic integrity?

A breach of academic integrity includes but is not limited to plagiarism, self-plagiarism, collusion, cheating, contract cheating, and academic misconduct. The Student Academic Integrity Policy and Procedure defines what these terms mean and gives examples.

Why is academic integrity important?

A breach of academic integrity may result in one or more penalties, including suspension or even expulsion from the University. It can also have negative implications for student visas and future enrolment at CQUniversity or elsewhere. Students who engage in contract cheating also risk being blackmailed by contract cheating services.

Where can I get assistance?

For academic advice and guidance, the Academic Learning Centre (ALC) can support you in becoming confident in completing assessments with integrity and of high standard.

What can you do to act with integrity?