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 comprehensive 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. Students will gain an understanding of the impact of accounting information systems on business practices and also on the risks that are created by this reliance on information systems.
The aim of this unit is to provide students with an overview of the use and ways in which accounting information systems, data and artificial intelligence have changed the way in which entities function.
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.
Evaluate the development of accounting technologic...
Learning Outcome 01
Examine the use of artificial intelligence (AI) an...
Learning Outcome 02
Implement ML algorithms to accounting information ...
Learning Outcome 03
Analyse and validate Blockchain, IoT and big data ...
Learning Outcome 04
Prove how technology advancements are used to enha...
Learning Outcome 05
Process accounting transactions and financial stat...
Learning Outcome 06
Content
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
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 are required to 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.
Each of these assessment pieces has been designed to empower students, lead to greater equity and deepen students’ skillsets by virtue of their design. They are assessments that are constructed to integrate the unit’s instruction and curriculum. If learning mode is online, Assessment will be conducted online.
Overview of assessments
Assessment Task 1: Oral critique This assessment ...
Assessment Task 1: Oral critique
This assessment task focuses on the development of technological developments, including artificial intelligence and machine learning, and how these developments impact members of society including the poort and vulnerable.
Submission Type: Individual
Assessment Method: Oral critique
Artefact: Oral critique (equivalent 1000 words)
25%
Assessment Task 2: Practical Accounting Software ...
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
25%
Assessment Task 3: Final Exam This task requires ...
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: Exam
Artefact: Exam (equivalent 2000 words).
50%
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 assist with ‘real-world’ preparedness. The unit also uses a scaffolding technique that builds a student’s skills and prepares them for the next phase of the learning process.
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. These are: “Attendance” mode, “Multi” mode and “Online” mode. This unit is offered in three 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.
Multi-Mode
In a multi-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.
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.
Gelinas, UJ, Dull, RB Wheeler, P. & Hill, MC. 2017 Accounting information systems, 11th edn, Cengage Learning, Mason, OH.
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, Summers, SL, Wood, DA. 2020 Accounting Information Systems, Global Edition15th edition, Pearson, USA.