Data Analytics Course Research Articles

Leveraging Oral Assessments to Enhance Learning and Integrity in Teaching Data Analytics

The rapid advancement of generative AI tools, like ChatGPT, has significantly impacted academic integrity in higher education. This paper explores the integration of oral assessments with traditional project evaluation in data analytics courses to address these challenges. While oral assessments cannot completely prevent cheating, they enable examiners to probe students' understanding more deeply. We present an overview of our assessment design and processes rather than detailed student results. We compare the implementation of oral assessments in a fully online professional course and a face-to-face undergraduate course. Moreover, we compare results before and after oral assessment training and explore AI’s role in efficiently generating individualised questions. Our findings demonstrate that oral assessments reduce academic dishonesty, enhance comprehension, and increase assessment rigour.

The role and impact of ChatGPT in educational practices: insights from an Australian higher education case study

Artificial intelligence (AI) tools, notably ChatGPT, are increasingly recognised for their transformative potential in higher education. This study employs a detailed case study approach complemented by a survey, delving into ChatGPT’s impact on pedagogical practices, student engagement, and academic performance. It involved 74 undergraduate and postgraduate students enrolled in data analytics courses in Australia. The quantitative analysis highlights ChatGPT’s role in providing personalised and on-demand support, which is highly valued among users for its flexibility and responsiveness, meeting a critical demand in educational settings. Notably, the study identifies a medium effect size (η2=0.173\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\eta ^2 = 0.173$$\\end{document}) in perceived benefits, indicating that ChatGPT accounts for approximately 17.3% of the variance in improved academic outcomes. However, challenges such as ChatGPT’s limited understanding of complex queries and the lack of human interactions are primary concerns, with a medium effect size (η2=0.289\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\eta ^2 = 0.289$$\\end{document}) suggesting significant areas for improvement. Furthermore, statistical analyses reveal a clear relationship between the frequency of ChatGPT usage and the perception of its benefits, underscoring the transformative potential for users who have integrated it into their academic practices. Despite these challenges, the differential impact on users versus non-users highlights the potential for ChatGPT to foster more engaging and effective educational practices. The findings advocate for targeted strategies to epitomise ChatGPT’s integration into educational settings, emphasising the need for ongoing research and the development of comprehensive guidelines to navigate its complexities and maximise its educational benefits.

ChatGPT in a programming course: benefits and limitations

This paper explores the potential use and limitations of ChatGPT in a programming course, specifically focusing on its evaluation in a Data Analytics course due to its broad applications. The study reveals that ChatGPT offers valuable assistance to teachers in creating class materials, facilitating teaching-learning activities, and designing assessments. Students also benefit from the technology, as it can generate R programming codes and serve as a learning companion. However, limitations were identified, including the generation of incorrect reference materials, the tendency to produce pattern-like content, and potential misuse. The paper encourages replication of the study in other courses to uncover additional strengths and limitations, contributing to the development of ethical guidelines for responsible utilization of this exciting technology.

Divvying Up Data: A Data Analytics Case

ABSTRACT This case asks students to assume the role of a newly hired (data) analyst for Divvy, Chicago’s bike share program. In this role, they use data analytics software to perform data analysis and data visualization techniques. This case can be implemented in multiple parts, and it is scalable for the unique needs of instructors and their students. Introductory topics include assessing the properties of the data, describing data validation techniques, creating calculated variables, and constructing basic data visualizations. Advanced topics include joining additional data sources, developing and investigating research questions, and creating dashboards to tell a meaningful story about the data. The case is unique because it enables students to interact with a large, real-world dataset, and it is ideal for accounting information systems or data analytics courses.

Integrating data analytics into the auditing curriculum: Insights and perceptions from early-career auditors

This study aims to help audit educators determine how, where, and to what extent audit data analytics (ADA) should be integrated into existing auditing curriculum to better prepare students for the auditing profession. We interview early-career auditors to understand their experience with ADA and add their perspective to the literature on audit curriculum changes. This group performs many of the tasks where ADA techniques would be applied and can provide valuable insight to audit educators based on their work experience and recent completion of auditing and/or data analytics courses. Responses indicate that early-career auditors rely more routinely on traditional ADA tools (e.g., Excel) and that the use of advanced ADA tools (e.g., Power BI and Alteryx) is relatively limited. Furthermore, firm-provided training on advanced ADA tools focuses on navigating tools rather than interpretation of ADA output. We also find that, regardless of the ADA tool used, the skillset used most frequently is interpretation of ADA output to identify anomalies and determine the impact on audit testing, which could be emphasized in existing audit courses. Most participants expressed that rather than removing content from the audit course to make room for data analytics, faculty should integrate ADA within existing content, encouraging audit educators to change how they teach, rather than what they teach. Specifically, faculty can provide hands-on examples of ADA to supplement lectures on existing topics or assign case studies that use ADA tools. These findings have implications for immediate changes to integrate ADA into auditing courses that are more manageable than a complete overhaul of the audit curriculum.

A Review of Scholarly Discourses on Accounting Technical Skills for IR 4.0

With advancements in information technology and large-scale globalization of economies in the previous decade, the working landscape of professional accountants has changed. This study looked into how academia should react to the demand from the industry that graduates in accounting have higher-level technological skills applicable to an IR 4.0 workplace. The study performed a cross-sectional analysis of scholarly discourses in journal editorials and commentary sections. The first finding showed that Big Data and Disruptive Technologies could change the structure of the finance department from pyramidal to either diamond or pillar-shaped. Secondly, accounting graduates need technical skills in information systems, data analytics, and associated software tools. Lastly, it is recommended that Advanced Data Analytics course should be taught to university accounting students so that they are able to interpret other departments’ data science results. This study contributes to the perceived gap in the accounting curriculum as reported in professional and scholarly publications.

Teaching Advanced Data Analytics, Robotic Process Automation, and Artificial Intelligence in a Graduate Accounting Program

ABSTRACT Technological advances in data analytics, robotic process automation (RPA), and artificial intelligence (AI) are transforming businesses and redefining what skill sets are needed in the accounting profession. To prepare students, academic accounting departments must develop strategies to integrate data analytics and emerging technologies into the curriculum. This paper discusses the design process used in a graduate-level advanced data analytics course that incorporates RPA and AI at a public university in the northeastern region of the United States. The paper concludes with a statistical analysis of precourse/postcourse student reflections and feedback surveys as part of a quality assurance initiative. The results suggest strong evidence of student learning related to the course learning objectives.

Preparing Analytics-Enabled Professionals in Finance Using a Simultaneous Team-Teaching Approach: A Case Study

To meet the demands of industry, undergraduate business curricula must evolve to prepare analytics-enabled professionals in fields such as finance, accounting, human resource management, and marketing. In this article, we provide a case study of developing a rigorous, integrated finance and data analytics course that was delivered using a simultaneous team-teaching approach within a regional, teaching university. This case study describes developing the learning outcomes; defining the integrated teaching approach; identifying and developing course content and materials; and planning the course delivery. Next, we describe the course delivery, including student achievement of course learning outcomes, and a summary of student feedback from end-of-course evaluations is provided. Finally, we present lessons learned from delivering the course and provide considerations for future work such as scaling this course and replicating it in other business domains.

Incorporating Biblical Motivations into a Business Analytics Course

Business schools have been developing data analytics courses to meet industry demand. However, a main challenge is that business students lack motivations to learn analytics. In this article, we offer suggestions and scriptural examples on how the Bible can be a rich source for Christian faculty to provide motivation for students to master the learning objectives in analytics courses as the development of a sound mind is deeply rooted in the Bible and God’s Truth.

Engaging Students in Online Data Analytics Learning: Meaningful Learning in Higher Education

The implementation of full online teaching and learning activities in Higher Educational institutions invites tangible challenges to sustain students’ interest and engagement in the course, throughout the semester. Meaningful learning encourages students to develop concepts constructively in active learning that promote the integration of knowledge in daily life and potential career applications. Meaningful learning also applies richer application capabilities in synchronous or asynchronous learning systems. Visualization is a technic to support concept description and imagination and provide an additional effective layer for learning towards increased motivation. In this paper, a discussion of the active involvement of students using visualization in online data analytics courses at the tertiary level is presented. Based on functional context principles of visualized course content, recommendations to practitioners are discussed.

Teaching Experiential Data Analytics Using an Election Simulation

Data Analytics has grown dramatically in importance and in the level of business deployments in recent years. It is used across most functional areas and applications, some of the latter including market campaigns, detecting fraud, determining credit, identifying assembly line defects, health services and many others. Indeed, the realm of analytics has famously grown to include major league sports and even U.S. election campaigns. Universities have raced to include data analytics in their curricula as the need for data scientists has become more acute. Unfortunately, many data science courses in college curricula suffer from various deficiencies: some lack a hands-on component, others are insufficiently experiential, and yet others leave students with too few transferable skills. This article describes an experiential approach to teaching data analytics at the college level that uses an election simulation, MISSimulation.com—to communicate key data science concepts in a competitive setting. Many universities actively use the simulation, combined with analytic tools such as Tableau and Excel, to implement team competition. We explore key techniques used and knowledge learned during the typical teaching of a data analytics course using the simulation. We end with pedagogical review of data analytics skills transferred during the course and student feedback.

Assessing the impact of digital education and the role of the big data analytics course to enhance the skills and employability of engineering students.

This study aims to explore the role of digital education in the development of skills and employability for engineering students through researching the role of big data analytics courses. The empirical study proposes the hypothesis that both soft and hard skills have positive effects on human capital, individual attributes, and the career development dimensions of engineering students. This is achieved through constructing a framework of three dimensions of engineering students' employability and two competency development dimensions of big data analytics courses. A questionnaire survey was conducted with 155 college engineering students and a structural equation model (SEM) was used to test the hypotheses. The results found that courses on big data analytics have a positive impact on engineering students' abilities in both hard skills (p < 0.01) and soft skills (p < 0.001) dimensions, while soft skills have a more significant impact on engineering students' employability. The study has practical and theoretical implications that further enriches the knowledge base on engineering education and broadens our understanding of the role of digitalization in enhancing the skills and employability of engineering students.

Text mining analysis on students’ expectations and anxieties towards data analytics course

Text mining analysis on students’ expectations and anxieties towards data analytics course

Harnessing Students’ Conceptual Understanding for Authentic Data Analytics Skills: Envisioning Malaysian Supply and Demand

The emergence of Data Science driven by the need to deal with data systematically and intelligently puts modern statistical practice as an important weapon. In line with the development of Data Science, the rebranding of statistics education took place in institutions of higher learning that used new labels such as data analytics courses to replace the old name of statistics courses. The objective of this paper is to highlight the implementation of teaching and learning of data analytics on three issues namely the development of the field of data analytics in the country, the challenges of implementing data analytics teaching and learning at the tertiary level and the direction of data analytics teaching and learning. Based on the shift in the paradigm of data analytics pedagogy to practical and interactive integration of conceptual understandings, recommendations for the teaching and learning of data analytics are discussed.

Developing Big Data Analytics Course for Non-ICT Major University Students

<p class='IJASEITAbtract'>In the fourth industrial revolution's education era, there is no boundary between majors or subjects, and it is common for university students to enrol in information and communication technology (ICT)-related courses as convergence education blends different disciplines. Today's job market is getting more competitive and requiring higher skills in ICT and computational thinking. Since non-ICT major students rarely have programming experiences and knowledge in regular classes, teaching a big data analytics course for non-ICT major university students is not easy. Thus, it is vital to develop a curriculum that comprises easy-to-follow and easy-to-understand modules. In this paper, we develop a big data analytics course for non-ICT major university students. The proposed big data analytics course for non-ICT major students comprises two parts: (1) basic programming skill modules with step-by-step guidelines and (2) extension to big data analytics modules with laboratory exercises, with the five principal programming modules based on the Python programming language. First, our investigation discusses the suggestions and limitations of the big data analytics course for non-ICT major university students. Then, we recommend programming languages, integrated development environments (IDEs), and useful tools that help learners perform programming exercises and milestone projects. The learning objectives and course design models are carefully selected based on Bloom's taxonomy with six thinking levels and five.

Outcome-Based Education: A Case Study on Course Outcomes, Program Outcomes and Attainment for Big data Analytics Course

Abstract: The key aspect of Outcome-Based Education (OBE) is an assessment of learning outcomes. OBE assessment of the outcomes of the course is the most critical feature required to improve the quality of education. Learning outcomes are concrete, formal statements that state what students are expected to learn in a course. Program Outcomes (POs) are the knowledge, skills, and attitudes that students should have at the end of the course. POs can be measured through Course Outcomes (COs) which are broad statements indicating knowledge, skills acquired at the end of the course. The results of each course are based on COs and POs. An innovative method is needed for assessing the COs and POs. This paper details the CO-PO matrix analysis and CO-PO attainment analysis for Big data analytics course. This study aims to give an effective strategy for evaluating COs and POs, beginning with the formulation of COs using Bloom's Taxonomy. In this methodology by using the students' performance in internalassessment, end exam, assignments, and course exit feedback; calculate the attainment of the course. The proposed method assists in the creation of effective lesson plans, high-quality question papers, and effective rubrics for course evaluation. The outcomebased approach necessitates a paradigm shift in thecurriculum process and how the learner is empowered to achieve outcomes. Keywords: Attainment, Course Outcomes, Outcome Based Education, Program Outcomes, Program Evaluation, Student Evaluation.

A Data Analytics Elective Course for a Master of Science in Accountancy Program

Abstract The American Accounting Association and the International Association to Advance Collegiate Schools of Business, responding to calls from business and the accounting profession, have encouraged the teaching of data analytics in accounting courses. To help accounting educators integrate this new topic into their curricula, we report on how we developed a data analytics elective course for our Master of Science in Accountancy program. This course introduces data analytics from an accounting perspective. It includes four parts: Data and its Significance as an Organizational Asset, Data Analytics Concepts, and Introduction to R, Individual Labs, and Group Case Work. We contribute to accounting information systems education by providing a starting point for faculty members developing an accounting-related data analytics course or integrating the material into existing curricula.

A framework for teaching an undergraduate data analytics class

The explosive growth of data analytics has led to a large demand for analytical skills which is outstripping the supply of this skillset. Business schools across the world are responding to this challenge by offering graduate and undergraduate programs in data analytics. There is a growing body of literature covering the graduate level programs, but very little literature covers the undergraduate courses. This article covers the basic topics, themes and universal issues in teaching the undergraduate introduction to the data analytics course. We provide an over-arching framework on how to deliver an introduction course. We examine three different introduction classes at three AACSB accredited schools. We conclude with common issues, mitigation plans, and lessons learned.

Using Data Analytics to Evaluate Policy Implications of Migration Patterns: Application for Analytics, AIS, and Tax Classes

ABSTRACTThis case uses IRS SOI migration data to help students understand the power of data analysis to identify potential policy issues faced by state governments. The data skills covered in this case are used by entry level accounting professionals in all fields: the Extract, Transform, and Load (ETL) process using Alteryx and creating data visualizations using Tableau. The case learning objectives are: (1) develop students' ETL skills; (2) develop students' data visualization skills; (3) develop students' critical-thinking skills, and (4) enhance students' effective oral and written communications skills. Pre- and post-learning assessment surveys and open-ended responses indicate that the case meets these learning objectives. Students easily related to policy questions surrounding migration as future employees considering their career options. As such, this case is appropriate for stand-alone data analytics courses in business or accounting, accounting information systems courses, undergraduate- and graduate-level tax classes, and M.B.A. courses.

A Model to Integrate Data Analytics in the Undergraduate Accounting Curriculum

ABSTRACTThis paper proposes a model to integrate data analytics into current undergraduate accounting curricula across existing courses rather than offering a stand-alone data analytics course. One of the advantages of curriculum integration is that students are introduced to data analysis in a progressive or sequential way. Furthermore, such an approach typically does not require additional credit hours to reflect the changes made to the accounting curriculum to introduce the emerging technologies used in the accounting profession. The model proposes course learning outcomes (CLOs) related to the data analytics applications linked to specific levels of study and accounting courses. In addition, teaching materials including the main textbook, supplemental reading materials, and case studies are mapped across accounting courses. This model is expected to be beneficial for accounting educators and members of curriculum committees when updating an accounting curriculum to include data analytics.