CONSTRAINTS TO EFFICIENT USE OF MACHINE LEARNING IN TEACHING BUSINESS EDUCATION COURSE CONTENTS IN DELTA STATE UNIVERSITIES

Abstract

The study investigated the perceived challenges to comprehensively using machine learning in the delivery of business education courses in Delta State universities.  In order to achieve the objectives of the study, three research questions were raised and three null hypotheses were tested at 0.05 level of significance guided the study. The study adopted the descriptive survey research design. The population of the study comprised of fifty-two (52) business education lecturers in three government owned universities in Delta State.  The entire population was studied. A structured questionnaire titled “Constraints to efficiently using machine learning in teaching business education course content (CEUMLTBECC)" was used for data collection. The research instrument was validated by three experts in the Department of Business Education in Delta State University, Abraka. The study adopted the Cronbach Alpha reliability method to ascertain the consistency of the research instrument.  It yielded a co-efficient of 0.93 adjudged reliable for this study. The descriptive statistics such as mean and standard deviation were used to answer the research questions, while an independent t-test was adopted to test the null hypotheses.  The findings of the study revealed that that financial limitations, human financial constraints, human resources, and skills constraints, institutional and managerial constraints enormously impacted the comprehensive use of machine learning in the delivery of business education courses in universities in Delta State.  Based on the findings of the study, it was recommended amongst others: higher institutions should delve into partnership with private technology firms and nongovernmental organizations to procure financial resources and technical support.