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Marketing and Smart Technologies pp 288–296Cite as

Words, Frequency, and Understanding: Ranking Marketing Discipline Terms Using Machine Learning

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Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 167))

Abstract

This paper reports on research currently underway that aims to refine a list of terms that represent the marketing discipline in order for them to be used in the teaching of marketing at the tertiary level. The research also forms part of a project for an intelligent tutoring system. It draws terms from existing textbooks and prior research to produce a list of 593 terms. The terms are evaluated for presence using frequency and TFIDF across a corpus of 227 first-year marketing assignments. The results reveal a high proportion of term usage, although this was not across all terms. TFIDF provided additional insights into term usage among the selected terms evaluated across the corpus. It is believed such lists can be used to inform shortfalls in areas of assignment (e.g., concepts not being addressed) and in the development of intelligent tutor systems, which can provide feedback to students on topic relevance in assignments. Being able to measure discipline knowledge is an important step in being able to assure employers of a student’s preparedness for work and is a key challenge for most Universities today.

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References

  1. Armstrong, G., Adam, S., Denize, S., Volkov, M., Kotler, P.: Principles of Marketing. 7th edn. Pearson Education Australia (2017)

    Google Scholar 

  2. Boufardea, E., Garofalakis, J.: A predictive system for distance learning based on ontologies and data mining. In: The Fourth International Conference on Advanced Cognitive Technologies and Applications, pp. 151–158 (2012)

    Google Scholar 

  3. Clarke, P.: Discipline-specific knowledge: time for clarity. Nurs. Sci. Q. 25(2), 149–150 (2012)

    Article  Google Scholar 

  4. Cliff, A., Woodward, R.: How do academics come to know? the structure and contestation of discipline-specific knowledge in a design school. High. Educ. Int. J. High. Educ. Educ. Plan. 48(3), 269–290 (2004)

    Google Scholar 

  5. Coxhead, A.: A new academic list. TESOL Q. 34(2), 213–238 (2000)

    Article  Google Scholar 

  6. Gutierrez, F., Dou, D., Fickas, S. Griffiths, G.: Providing grades and feedback for student summaries by ontology-based information extraction. In: Chen, X., Lebanon, G., Wang, H., Zaki, M. (eds.) Proceedings of the 21st ACM International Conference on Information and Knowledge Management. ACM, Maui, HI, USA, October 29–November 2, 1722–1726 (2012)

    Google Scholar 

  7. Hager, P., Holland, S., Beckett, D.: Enhancing the learning and employability of graduates: the role of generic skills. Business/Higher Education Round Table Position Paper No. 9. Melbourne, Australia (2002)

    Google Scholar 

  8. Horn, F., Arras, L., Montavon, G., Muller, K., Samek, W.: Exploring text datasets by visualizing relevant words. arXiv:1707.05261v1 [cs.CL] 17 Jul. Accessed 05 July 2019

  9. Manning, C., Raghavan, P., Schutze, H.: Introduction to Information retrieval. Cambridge University Press, New York, NY (2008)

    Book  Google Scholar 

  10. Noy, N., McGuinness, D.: Ontology development 101: a guide to creating your first ontology, stanford knowledge systems laboratory technical report KSL-01-05 and stanford medical informatics technical report SMI-2001-0880, March (2001)

    Google Scholar 

  11. Pinto, F., Gago, P., Santos, M.: Marketing database knowledge extraction—toward a domain ontology. In: International Conference on Intelligent Engineering Systems. Barbados, 16–18 April. https://doi.org/10.1109/INES.2009.4924761 (2009)

  12. Reed, P.: Strategic Marketing: Decision Making and Planning, 4th Ed. Cengage Learning Australia (2014)

    Google Scholar 

  13. Rehurek, R. Sojka, P.: Software Framework for Topic Modelling with Large Corpora. In: Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks, pp. 45–50, Valletta, Malta, May. ELRA (2010)

    Google Scholar 

  14. Salloum, S., Al-Emran, M., Abdel Monem, A., Shaalan, K.: A survey of text mining in social media: facebook and twitter perspectives. Adv. Sci. Technol. Eng. Syst. J. 2(1), 127–133 (2017)

    Article  Google Scholar 

  15. Shermis, M. D., Burstein, J.: Handbook of Automated Essay Evaluation: Current Applications and New Directions. Routledge (2013)

    Google Scholar 

  16. Shu, K., Sliva, A., Wang, S., Tang, J., Liu, H.: Fake news detection on social media: a data mining perspective. ACM SIGKDD Explor. Newsl. 19(1), 22–36 (2017)

    Article  Google Scholar 

  17. Siemens, G.: LAK’11, 1st International Conference on Learning Analytics and Knowledge 2011. https://tekri.athabascau.ca/analytics. Accessed 5 July 2019

  18. Slater, S., Joksimovic, S., Kovanovic, V., Baker, R., Gasevic, D.: Tools for educational data mining: a review. J. Educ. Behav. Stat. 42(1), 85–106 (2017)

    Article  Google Scholar 

  19. Treleaven, L., Voola, R.: Integrating the development of graduate attributes through constructive alignment. J. Mark. Educ. 30, 160–173 (2008)

    Article  Google Scholar 

  20. Vitartas, P., Ahmed, T., Alahakoon, D., Midford, S., Nathawitharana, N., Ong, K.L., Sullivan-Mort, G.: Using learning analytics to guide learning: an analysis of marketing assignments. In: Fortin, D., Ozanne, L. (eds.) Marketing in a Post-Disciplinary Era. Proceedings of the Australian and New Zealand Marketing Academy Conference (ANZMAC), Christchurch, 5–7 December, pp. 623–629 (2016)

    Google Scholar 

  21. Yang, Y., Pedersen, J.: A comparative study on feature selection in text categorization. In: Proceedings of the Fourteenth International Conference on Machine Learning, ICML ’97, San Francisco, CA, pp. 412–420 (1997)

    Google Scholar 

  22. Yilmaz, N., Alptekin, G.: An ontology-based data mining approach for strategic marketing. In: Proceedings of the World Multi-Conference on Systemics, Cybernetics and Informatics: WMSCI 2013, Orlando, Florida July 9–12 (2013)

    Google Scholar 

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Authors and Affiliations

  1. La Trobe University, Melbourne, Australia

    Peter Vitartas

Authors
  1. Peter Vitartas
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Correspondence to Peter Vitartas .

Editor information

Editors and Affiliations

  1. DEI/FCTUC, University of Coimbra, Coimbra, Portugal

    Álvaro Rocha

  2. University Institute of Maia—ISMAI, Maia, Portugal

    José Luís Reis

  3. FHNW School of Business, FHNW University of Applied Sciences and Arts, Olten, Switzerland

    Marc K. Peter

  4. Faculty of Organizational Sciences, University of Belgrade, Belgrade, Serbia

    Zorica Bogdanović

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Vitartas, P. (2020). Words, Frequency, and Understanding: Ranking Marketing Discipline Terms Using Machine Learning. In: Rocha, Á., Reis, J., Peter, M., Bogdanović, Z. (eds) Marketing and Smart Technologies. Smart Innovation, Systems and Technologies, vol 167. Springer, Singapore. https://doi.org/10.1007/978-981-15-1564-4_27

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