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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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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