Credit points


Campus offering

No unit offerings are currently available for this unit


ACCT100 Introduction to Accounting OR ACCT210 Accounting Foundations

Teaching organisation

150 hours over a twelve-week semester or equivalent study period

Unit rationale, description and aim

Accounting information systems (AIS), big data analytics and artificial intelligence (AI) play essential roles in today’s business. Artificial intelligence, blockchain, Internet of Things (IoT) and big data analytics are the among top ten emerging technologies in accounting. These emerging technologies have given firms a low-cost platform to create convenient, data-intuitive product and services including AI. Accounting information systems allow for smart accounting utilised by a wide variety of businesses. The unit takes an extensive view of accounting information systems, data analytics and the application of artificial intelligence (AI) and machine learning (ML) that emphasise the accountants’ roles in the use, management, design, and evaluation of systems.  This unit provides students with the skills to use accounting software for financial transactions as well as how to apply AI, ML, Blockchain, IoT and big data analytics to real-life cases. This unit provides students with a variety of technological skills to advance all members of the society including the poor and vulnerable.

The aim of this unit is to ensure that students will benefit from knowing about information technology and information systems relevant to accounting for a successful accounting career.

Learning outcomes

To successfully complete this unit you will be able to demonstrate you have achieved the learning outcomes (LO) detailed in the below table.

Each outcome is informed by a number of graduate capabilities (GC) to ensure your work in this, and every unit, is part of a larger goal of graduating from ACU with the attributes of insight, empathy, imagination and impact.

Explore the graduate capabilities.

On successful completion of this unit, students should be able to:

LO1 - Assess the development of accounting technological advancements and how the role of the  responsible custodian of data and producer of useful information, data protection and privacy regulations advance and support all members of society including the poor and vulnerable (GA1, GA5)

LO2 - Analyse the use of artificial intelligence (AI) and machine learning (ML) for business  (GA4, GA5)

LO3 - Apply ML algorithms to accounting information to predict product / business segment / organisational performance (GA5, GA10)

LO4 - Apply Blockchain, IoT and big data analytics in the business (GA5, GA8)

LO5 - Evaluate how technology advancements are used to enhance the efficiency and effectiveness of communication including the use of graphs, system flowcharts, data flow diagrams and dashboards and entity relationship diagrams (GA5, GA9)

LO6 - Generate accounting transactions and financial statements using accounting software (GA5)

Graduate attributes

GA1 - demonstrate respect for the dignity of each individual and for human diversity 

GA4 - think critically and reflectively 

GA5 - demonstrate values, knowledge, skills and attitudes appropriate to the discipline and/or profession 

GA8 - locate, organise, analyse, synthesise and evaluate information

GA9 - demonstrate effective communication in oral and written English language and visual media 

GA10 - utilise information and communication and other relevant technologies effectively.


Topics will include:

  • accounting technological advancements
  • Artificial Intelligence
  • Machine Learning and its algorithms including decision trees, random forests, neural networks. bagging and boosting ensemble techniques
  • Blockchain technologies and application
  • Internet of Things (IoT) evolution, landscape and application
  • Big data analytics
  • accounting information systems and business process
  • Accounting software
  • Using accounting software for business transactions and reporting

Learning and teaching strategy and rationale

ACU’s teaching policy focuses on learning outcomes for students. Our teaching aims to engage students as active participants in the learning process while acknowledging that all learning must involve a complex interplay of active and receptive processes, constructing meaning for oneself, and learning from others. ACU promotes and facilitates learning that is autonomous and self-motivated, is characterised by the individual taking satisfaction in the mastering of content and skills and is critical, looking beneath the surface level of information for the meaning and significance of what is being studied.

The schedule of the workshop is designed in such a way that students can achieve intended learning outcomes sequentially. Teaching and learning activities will apply the experiential learning model, which encourages students to apply higher-order thinking. The unit ensures that learning activities involve real-world scenarios that in turn assist with ‘real-world’ preparedness. The unit also uses a scaffolding technique that builds a student’s skills and prepares them for the next learning process phase.

This unit is structured with required upfront preparation before workshops, and most students report that they spend an average of one hour preparing before the workshop and one or more hours after the workshop practicing and revising what was covered. The online learning platforms used in this unit provide multiple forms of preparatory and practice opportunities for students to prepare and revise. It is up to individual students to ensure that the out-of-class study is adequate for the optimal learning outcomes and successes.

Mode of delivery: This unit is offered in different modes to cater to the learning needs and preferences of a range of participants and maximise effective participation for isolated and/or marginalised groups.

Attendance Mode

In a weekly attendance mode, students will require face-to-face attendance in specific physical location/s. Students will have face-to-face interactions with lecturer(s) to further their achievement of the learning outcomes. The online learning platforms used in this unit provide multiple forms of preparatory and practice opportunities for students to prepare and revise.

Blended Mode

In a blended mode, students will require intermittent face-to-face attendance determined by the School. Students will have face-to-face interactions with lecturer(s) to further their achievement of the learning outcomes. This unit is structured with required upfront preparation before workshops. The online learning platforms used in this unit provide multiple forms of preparatory and practice opportunities for students to prepare and revise.

Online Mode

In an Online mode, students are given the opportunity to attend facilitated synchronous online seminar classes with other students and participate in the construction and synthesis of knowledge, while developing their knowledge. Students are required to participate in a series of online interactive workshops which include activities, knowledge checks, discussion and interactive sessions. This approach allows flexibility for students and facilitates learning and participation for students with a preference for virtual learning.

ACU Online

This unit uses an active learning approach to support students in the exploration of knowledge essential to the discipline. Students are provided with choice and variety in how they learn. Students are encouraged to contribute to asynchronous weekly discussions. Active learning opportunities provide students with opportunities to practice and apply their learning in situations similar to their future professions. Activities encourage students to bring their own examples to demonstrate understanding, application and engage constructively with their peers. Students receive regular and timely feedback on their learning, which includes information on their progress.

Assessment strategy and rationale

Assessments are used primarily to foster learning. ACU adopts a constructivist approach to learning that seeks alignment between the fundamental purpose of each unit, the learning outcomes, teaching and learning strategy, assessment and the learning environment. In order to pass this unit, students must demonstrate competence in all learning outcomes and achieve an overall score of at least 50% . Using constructive alignment, the assessment tasks are designed for students to demonstrate their achievement of each learning outcome. If learning mode is online, Assessment will be conducted online.

Overview of assessments

Brief Description of Kind and Purpose of Assessment TasksWeightingLearning OutcomesGraduate Attributes

Assessment Task 1: Portfolio of Engagement

For weeks 2–12, students will actively participate in online discussion forums and online activities. Students will be evaluated on a combination of their engagement in the unit via discussion board questions, responses to postings, presentation videos, and evidence of successful engagement in online activities.

Submission Type: Individual

Assessment Method: online engagement and completion of regular learning tasks


LO1, LO2

GA1, GA4, GA5

Assessment Task 2: Practical Accounting Software application

Students are required to demonstrate effective use of accounting software to record accounting transactions and prepare financial statements.

Submission Type: Individual

Assessment Method: Employment of cloud-based Accounting software

Artefact: Accounting software


LO5, LO6

GA5, GA9

Assessment Task 3: Final Exam

This task requires students to analyse Blockchain and IoT and to apply Machine Learning and Big Data analytical tools. Students also utilise relevant communications tools to enhance the efficiency and effectiveness of their output.

Submission Type: Individual

Assessment method: Laboratory exam

Artefact: Technological and communication output (equivalent 2000 words).


LO3, LO4, LO5

GA5, GA8, GA9, GA10

Representative texts and references

Beutel, J., List, S. & Von Schweinitz, G. 2019. Does machine learning help us predict banking crises? Journal of financial stability, 45, 100693.

Carmona, P., Climent, F. & momparler, A. 2019. Predicting failure in the U.S. banking sector: An extreme gradient boosting approach. International review of economics & finance, 61, 304-323.

Guida, T. 2019. Big Data and Machine Learning in Quantitative Investment, Newark, UK, John Wiley & Sons, Incorporated. Available from: ProQuest Ebook Central.

Hull, J. 2020. Machine learning and finance. Journal of Risk Management in Financial Institutions, 13, 104-105.

Jagtiani, J. & Lemieux, C. 2019. The roles of alternative data and machine learning in fintech lending: Evidence from the LendingClub consumer platform. Financial management, 48, 1009-1029.

Lopez De Prado, M. 2018. Advances in Financial Machine Learning, Newark, USA, John Wiley & Sons, Incorporated.

Parkes A, Considine B, Olesen K & Blount Y 2018, Accounting Information Systems, 5th edn, John Wiley & Sons, Australia, Milton, Qld. ISBN: 978-0-730-36913-4 

Polyzos, S., Samitas, A. & Katsaiti, M.-S. 2020. Who is unhappy for Brexit? A machine-learning, agent-based study on financial instability. International review of financial analysis, 72.

Romney MB., Steinbart, PJ., 2020, Accounting Information Systems, Global Edition, 15th edn, Pearson, USA. ISBN 9781292353364.

Russell, S & Norvig, P 2017, Artificial Intelligence: a modern approach, 3rd edn, Pearson Education

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