Inclusion of Neural Networks in Higher Education: A Systematic Review and Bibliometric Analysis

The goal of elevating higher education to suit shifting societal and industrial needs has led to the development of many projects, including educational data mining based on neural networks. This research article presents a systematic review of how neural networks have been applied to improve educational results, and teaching and learning processes, and addresses issues faced by higher educational institutions by analyzing a wide range of studies. We carry out a thorough bibliometric analysis of academic works on the use of artificial neural networks in higher education. Through comprehensive bibliometric analysis of 225 articles taken from the Scopus database, we identified the evolution, trend of publications, salient features of research, and most prolific contributors in terms of researchers, countries, and institutions in this rapidly developing field. Biblioshiny using R software and VOSviewer are utilized to analyze the data. We uncover intriguing areas for further study, including the use of neural networks in conjunction with other technologies, such as VR and NLP to improve teaching and learning experiences. This study contributes to the existing scholarly conversation by highlighting trends, literature gaps, and potential future directions. It also provides information for processes of decision-making involving the incorporation of artificial neural networks (ANL) in the context of higher education.