CONSTRAINTS TO EFFICIENT USE OF MACHINE LEARNING IN TEACHING BUSINESS EDUCATION COURSE CONTENT IN NIGERIAN UNIVERSITIES
Abstract
The study investigated the constraints to the efficient use of machine learning in teaching business education course content in Nigerian universities. Three research questions guided the study, along with three corresponding null hypotheses tested at the 0.05 level of significance. A survey research design was adopted, and the study area comprised all the universities in Cross River State.The population consisted of 52 Business Education lecturers across the three universities in the state. Data were collected using a structured questionnaire titled Constraints to Efficiently Using Machine Learning in Teaching Business Education Course Content (CEUMLTBECC), which contained 15 items validated by three experts. The reliability of the instrument was established using the Cronbach Alpha method, which yielded a coefficient of 0.93, indicating that the instrument was reliable for the study. The mean and standard deviation were used to answer the research questions, while an independent t-test was employed to test the null hypotheses at the 0.05 level of significance. The findings revealed that financial constraints, human resource and skills constraints, and institutional and administrative constraints significantly influence the efficient use of machine learning in teaching business education course content in Nigerian universities. Furthermore, there was no significant difference between the mean ratings of male and female Business Education lecturers regarding the identified constraints. Based on these findings, it was recommended, among other things, that institutions should explore partnerships with private technology firms and non-governmental organizations to secure financial resources and technical support. Such collaborations can help reduce the costs associated with adopting machine learning technologies through grants, donations, or subsidized access to technology.
Key words: Constraints, Machine Learning, Teaching, Business Education, Course Content
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