Machine learning paradigms and lecturers’ academic productivity in Universities in Rivers State, Nigeria

Despite the growing adoption of machine learning in education, there is limited empirical evidence on its relationship with lecturers’ academic productivity in Nigerian universities This study examined the relationship between machine learning paradigms such as supervised learning, unsupervised learning, and reinforcement learning and lecturers’ academic productivity in universities in Rivers State, Nigeria. The study adopted a correlational research design. The population consisted of 2,385 academic staff across three public universities, from which a sample of 343 lecturers was selected using stratified random sampling, while 318 valid responses were analyzed. Data were collected using two researcher-developed instruments titled Machine Learning Paradigms Questionnaire and  lecturers’ Academic Productivity Questionnaire, with reliability coefficients of 0.84 and 0.91 respectively. Data were analyzed using simple regression analysis at the 0.05 level of significance. The study demonstrates that integrating machine learning paradigms can significantly enhance lecturers’ teaching efficiency, research output, and overall academic productivity in universities. Reinforcement learning demonstrated the strongest relationship with productivity. The study concludes that the effective integration of machine learning paradigms can enhance lecturers’ teaching efficiency, research productivity, and institutional performance. It therefore recommends that universities invest in digital infrastructure and capacity-building programmes to support the adoption of machinelearning technologies in academic work.