Using unsupervised machine learning to model tax practice learning theory - Howard Miller, Alfred (International Journal of Engineering and Technology, 2018)
The aim of this study was to utilize unsupervised machine learning framework to explore a dataset comprised of assessed output by Bachelors of Business, Taxation learners over four successive semesters. The researcher sought to motivate deployment of an evidence-supported, data-driven approach to understand the scope of student learning from a bachelor’s degree in business class taxation class, as a tool for accreditation reporting purposes. Outcomes from the data analysis identified four factors; two related to tax and two related to learning. These factors are, tax theory, and tax practice, along with practical learning and theoretical learning. Research motivated a grounded theory paradigm that explained taxation class learner’s scope of acquired knowledge. The resulting four factor model is a result of the study. The emergent paradigm further explains accounting student’s readiness for career success upon graduation and provides a novel way to meet outcomes reporting requirements mandated by programmatic business accreditors such as required by the Accreditation Council for Business Schools and Programs (ACBSP).