Using unsupervised machine learning to model tax practice learning theory

  • Authors

    • Alfred Howard Miller
    2018-03-10
    https://doi.org/10.14419/ijet.v7i2.4.13019
  • Big Data Analysis, Computer Pattern Recognition, Taxation Learning Outcomes, Unsupervised Machine Learning, Tax Practice Learning Theory.

  • Abstract

    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).

     

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  • How to Cite

    Howard Miller, A. (2018). Using unsupervised machine learning to model tax practice learning theory. International Journal of Engineering & Technology, 7(2.4), 109-116. https://doi.org/10.14419/ijet.v7i2.4.13019

    Received date: 2018-05-18

    Accepted date: 2018-05-18

    Published date: 2018-03-10