Using unsupervised machine learning to model tax practice learning theory

  • Authors

    • Alfred Howard Miller
    https://doi.org/10.14419/ijet.v7i2.4.13019

    Received date: May 18, 2018

    Accepted date: May 18, 2018

    Published date: March 10, 2018

  • 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).

  • References

    1. Higher Colleges of Technology (2016). Retrieved from http://www.hct.ac.ae/ (2017).
    2. United Arab Emirates Ministry of Finance (2017). VAT, retrieved from https://www.mof.gov.ae/En/budget/Pages/VATQuestions.aspx
    3. ACBSP (2018). Baccalaureate-Graduate Degree Accreditation, Re-trieved Januray 29, 2018 from http://www.acbsp.org/?page=baccalaureate
    4. KH Coder, (2017). Open source software, Higuchi, Koichi, Ritsumeikan University, Japan. Available at http://khc.sourceforge.net/.
    5. Hunston, S. (2010). Corpora in Applied Linguistics, Cambridge University Press.
    6. Stoykova, V. (2017). Extracting Academic Subjects Semantic Rela-tions Using Collocations, EAI Endorsed Transactions on Energy Web and Information Technologies 17(14). DOI: 10.4108/eai.4-10-2017.153161
    7. O’Connell, B., De Lange, P., Freeman, M., Hancock, P., Abraham, A., Howieson, B, & Watty, K. (2015). Does calibration reduce vari-ability in theassessment of accounting learning outcomes? Assess-ment & Evaluation in Higher Education. DOI: 10.1080/02602938.2015.1008398
    8. Andartari A., Susanti, s., & Andriani, V. (2013). Effect of Intellectu-al Capabilities (IQ) and Learning Motivation at the Results of Ac-counting Subject on SMA Labschool Rawamangun. DOI10.21009/JPEB.001.1.1
    9. Xiong, Y., Zhou, H. & Ogilby, S. M. (2014). Investigation of the Effects of Cognitive Elaboration on Accounting Learning Outcomes, Journal of Education and Learning; 3(4); DOI: 10.5539/jel.v3n4p1
    10. Miller, A. H. (2016). Computer-Aided Content Analysis of the Cor-pus of Business Discourse: A Comparison of Accounting and HR Learners, NETs 2016 Osaka Japan July 25, 2015.
    11. Miller, A. H. (2017). Preparing Students for Career Success in Ac-counting: The SCIL-based Model with a Focus on Content Analysis, Transnational Journal of Business, Retrieved from: http://www.acbsp.org/members/group.aspx?id=143359
    12. Anzai, S., & Matsuzawa, C. (2013). Missions of the Japanese Na-tional University Corporations in the 21st Century: Content analysis of mission statements - Academic Journal of Interdisciplinary Stud-ies, 2013 - mcser.org
    13. Minami, T., & Ohura, Y. (2015). How Student’s Attitude Influences on Learning Achievement? An Analysis of Attitude Representing Words Appearing in Looking Back Evaluation Texts, International Journal of Database Theory
    14. In addition, Application, 8(2), 129-144. Retrieved from http://dx.doi.org/10.14257/ijdta.2015.8.2.13
    15. AlShammari, I. A., Aldhafiri, M. D., & Al-Shammari, Z. (2013). A meta-analysis of educational data mining on improvements in learn-ing outcomes. College Student Journal, 47(2), 326-333.
    16. DePape, J., Lockard, N. & Laramy, R. (2007). Using Accreditation Self-Study Results to Better Understand Student from Recruit through Alumnus. The Center for Teaching and Learning, Preparing Facilitators of Learning for a Diverse World. Take the Credit. Re-trieved from http://www.cair.org/wp-content/uploads/sites/474/2015/07/
    17. Trochim, W. M., (2016). Hindsight is 20/20: Reflections on the Evo-lution of Concept Mapping. Evaluation and Program Planning. DOI: 10.1016/j.evalprogplan.2016.08.009
    18. Gross, S., Kim, M., Schlosser, J., Mohtadi, C. Lluch, D., & Schnei-der, D. (2014). Fostering computational thinking in engineering edu-cation: Challenges, examples, and best practices. 2014 IEEE Global Engineering Education Conference (EDUCON), pp 450 - 459. DOI: 10.1109/EDUCON.2014.6826132
    19. Vesanto, J. & Alhoniemi, E. (2000). Clustering of the self-organizing map. IEEE Transactions on Neural Networks, 11(3). Retrieved from http://ftp.it.murdoch.edu.au/units/ICT219/Papers%20for%20transfer/papers%20on%20Clustering/Clustering%20SOM.
    20. Berinato (2016). Visualizations That Really Work. Harvard Business Review, Retrieved from https://hbr.org/2016/06/visualizations-that-really-work Laramy.pdf
    21. Buja, A., Swayne D. F., Littman M. L., Dean, N., Tamura, T. (2011). Application of text-mining methodology to sociological anal-ysis of internet text in Japan. Retrieved from http://www.cajs.tsukuba.ac.jp/monograph/articles/ 01_201103/cajs01_201103_077-097.pdf
    22. Matsuo, Y, & Ishizuka, M. (2004). Keyword Extraction from a Sin-gle Document using Word Co-occurrence Statistical Information. In-ternational Journal on Artificial Intelligence Tools, 13(1) pp 157–169. Retrieved from https://www.researchgate.net/profile/Mitsuru_Ishizuka/publication/2572200_Keyword_Extraction_from_a_Single_Document_using_Word_Cooccurrence_Statistical_Information/links/02e7e522976acdaa9e000000.pdf
    23. Bargiela-Chiappini, F., Nickerson, C., & Plancken, B. (2008) Busi-ness Discourse. Retrieved from http://www.palgraveconnect.com/pc/doifinder/10.1057/9780230627710
    24. Yu, C. H., Jannasch-Pennell, A., & DiGangi, S. (2011). Compatibil-ity between text mining and qualitative research in the perspectives of grounded theory, content analysis, and reliability. The Qualitative Report, 16(3), 730-744. Available from http://nsuworks.nova.edu/tqr/vol16/iss3/6/
    25. Pelet, J-E, Khan. J., Papadopoulou, P., & Bernardin, E. (2014). M Learning: Exploring the Use of Mobile Devices and Social Media, in (Ed) Baporikar, N. (2014). Handbook of Research on Higher Educa-tion in the MENA Region: Policy and Practice, 261-296. Hershey, PA: IGI Global.
    26. Posner, R., (2012) Opinion, United States of America, Plaintiff-Appellee, v. Deanna L. Costello, Defendant-Appellant, No. 11-291 U.S. Court of Appeals Seventh Circuit Court.
    27. Smith, G. (2014). Data and Intuition, The Conglomerate, Retrieved from http://www.theconglomerate.org/corpuslinguistics/
    28. Flowerdew, L. (2009). Applying corpus linguistics to pedagogy: A critical evaluation* International Journal of Corpus Linguistics, 14(3), 393-417. DOI:10.1075/ijcl.14.3.05flo
    29. O’Neil, C. (2016). ‘Rogue Algorithms’ and the dark side of big data. Knowledge@Wharton. Wharton, University of Pennsylvania. Avail-able from http://knowledge.wharton.upenn.edu/article/rogue algo-rithms-dark-side-big-data/?utm_ source=kw_newsletter&utm_medium=email&utm_campai n=2016-09-22
    30. Higuchi. K. (2016). A Two-Step Approach to Quantitative Content Analysis: KH Coder Tutorial using Anne of Green Gables (Part I) Ritsumeikan Social Sciences Review, p. 77-91. Retrieved from http://www.ritsumei.ac.jp/file.jsp?id=325881
    31. Miller, A. H. (May 2017). Assessing work readiness in accounting graduates via the SCIL-based model. Seventh QS_MAPLE Confer-ence Dubai, UAE, May 01- 04, 2017. Retrieved from: http://www.qsmaple.org/7thqsmaple/
    32. Borg, I, & Groenen, P. (2005). Modern multidimensional scaling: Theory and applications (2nd Ed.). New York, NY: Springer-Verlag.
    33. Hofman, H., & Chen, L. (2008). Data Visualization with Multidi-mensional Scaling. Journal of Computational and Graphical Statis-tics, 17(2), 444-472. Doi: 10.1198%2F106186008X318440 [24]. Austin, D. (2017).
    34. Kohonen, T. (1982). Self-Organized Formation of Topologically Correct Feature Maps. Biological Cybernetics. 43 (1): 59–69. Doi: 10.1007/bf00337288.

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