Using unsupervised machine learning to model tax practice learning theory

 
 
 
  • Abstract
  • Keywords
  • References
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  • 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).

     


  • Keywords


    Big Data Analysis; Computer Pattern Recognition; Taxation Learning Outcomes; Unsupervised Machine Learning; Tax Practice Learning Theory.

  • References


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Article ID: 13019
 
DOI: 10.14419/ijet.v7i2.4.13019




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