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Influence of Data Analytics Education on the Analytical Skills of Accounting Students in Nigerian Universities

Abstract

The increasing use of data analytics in accounting and financial decision-making has transformed the competencies expected of accounting graduates. Modern accounting professionals increasingly rely on data analytics tools to examine large volumes of financial and non-financial data, identify patterns, detect anomalies, evaluate business performance, support auditing, and provide evidence for managerial decision-making. Consequently, accounting students require analytical skills that extend beyond traditional accounting knowledge to include data interpretation, critical thinking, problem-solving, pattern recognition, and technology-assisted analysis. However, accounting education in some Nigerian universities may provide limited opportunities for students to receive structured and practical data analytics training. Data analytics education provides an opportunity to strengthen students' ability to analyse accounting information and apply analytical techniques to real-world financial situations. Against this background, this study investigates the influence of data analytics education on the analytical skills of accounting students in Nigerian universities. The study will be anchored on Human Capital Theory, Constructivist Learning Theory, and Technology Acceptance Model (TAM). Human Capital Theory explains how investment in relevant education and technical competencies can improve students' productivity, professional capabilities, and employability. Constructivist Learning Theory emphasizes active learning, practical application, problem-solving, and knowledge construction through meaningful learning experiences, providing a basis for examining how data analytics education may develop accounting students' analytical abilities. The Technology Acceptance Model explains how students' perceived usefulness and perceived ease of use of data analytics technologies may influence their engagement with analytics-based learning and their effective use of digital analytical tools. Collectively, these theoretical perspectives provide a suitable framework for explaining how data analytics education may influence the analytical skills of accounting students in Nigerian universities. The study will adopt a quantitative quasi-experimental or analytical cross-sectional research design. The study population will comprise undergraduate accounting students enrolled in selected public and private universities across Nigeria. A multistage sampling technique will be used to select geopolitical zones, states, universities, faculties or departments, levels of study, and eligible accounting students. Data analytics education will be assessed using indicators such as exposure to data analytics courses, frequency and duration of analytics training, spreadsheet-based analysis, statistical analysis, accounting data visualization, data cleaning, data interpretation, dashboard development, use of accounting analytics software, audit analytics, financial analytics, data-driven decision-making exercises, practical projects, case studies, and lecturer-guided analytics activities. Analytical skills will be assessed using indicators such as data interpretation, critical thinking, problem-solving, pattern recognition, anomaly detection, financial analysis, trend analysis, evaluation of accounting information, evidence-based decision-making, logical reasoning, data visualization interpretation, and ability to derive meaningful conclusions from accounting datasets. Data will be collected using structured questionnaires, standardized analytical-skills assessment tools, practical data-analysis exercises, accounting data interpretation tests, case-based assessments, and pre-test and post-test assessments where a quasi-experimental intervention is adopted. Descriptive statistics will be used to summarize students' demographic and academic characteristics, exposure to data analytics education, technologies used, practical experience, and analytical skill levels. Inferential statistical techniques, including chi-square tests, paired and independent t-tests, correlation analysis, and multiple regression analysis where appropriate, will be used to determine the influence of data analytics education on analytical skills. Where a quasi-experimental design is adopted, analytical-skill scores before and after the educational intervention may be compared with those of a comparison group to determine changes associated with the intervention. Diagnostic tests will also be conducted to assess the reliability, validity, and robustness of the findings. The study is expected to find that data analytics education has a significant positive influence on the analytical skills of accounting students in Nigerian universities. Students exposed to structured, practical, and accounting-focused data analytics education are expected to demonstrate stronger abilities in data interpretation, financial analysis, problem-solving, pattern recognition, anomaly detection, critical thinking, and evidence-based decision-making than students with limited exposure. Practical exercises involving financial datasets may enable students to move beyond theoretical accounting calculations and develop the ability to identify meaningful relationships and trends within accounting information. Exposure to spreadsheets, visualization tools, statistical techniques, dashboards, and accounting analytics applications may also improve students' technological confidence and readiness for data-driven accounting environments. However, inadequate access to computers and analytics software, limited internet connectivity, insufficiently trained lecturers, large class sizes, curriculum constraints, and limited practical datasets may reduce the effectiveness of data analytics education. The study therefore expects practical, current, industry-relevant, and technology-supported data analytics education to contribute significantly to improved analytical skills among accounting students in Nigerian universities. The study is expected to contribute to the literature on accounting education, data analytics education, accounting analytics, analytical skills, digital accounting, educational technology, data-driven decision-making, accounting information systems, and higher education in Nigeria. The findings will provide useful information to the National Universities Commission, universities, faculties of management sciences, accounting departments, accounting educators, professional accounting bodies, employers, curriculum developers, educational technology providers, and policymakers regarding strategies for strengthening analytical competencies among future accounting professionals. The study will also provide evidence-based recommendations for integrating data analytics into accounting curricula, improving access to analytics software and computer laboratories, strengthening lecturers' capacity in accounting analytics, incorporating practical accounting datasets and case studies into teaching, promoting university-industry collaboration, and ensuring that accounting graduates possess the analytical competencies required for effective professional practice in Nigeria's increasingly data-driven accounting environment.

Keywords: Data analytics education, analytical skills, accounting students, accounting education, accounting analytics, data interpretation, digital accounting, critical thinking, data-driven decision-making, Nigerian universities, higher education, Nigeria.