engleski

Neural networks for student performance in higher education: Preliminary bibliometric analysis

Neural networks or artificial neural networks are a branch of machine learning, a technology based on brain and nervous system studies. Thanks to the fast-technological changes, growing computing power, advanced algorithms, and widely available digital data, the application of neural networks has spread tremendously in many fields of study, from health and medicine, accounting, finance, engineering, manufacturing, and marketing to natural language processing and robotics, wherever the analysis of big sets of data is needed. This paper focuses on using neural networks in higher educational institutions for student performance analysis and prediction, intending to investigate these researches' bibliometric and topical characteristics published in scientific papers. We have searched the indexing service Scopus to track the papers that present the applications of neural networks in higher education in the last five years. The research has been investigated based on the bibliometric characteristics (authors, publications, institutions, funding) as well as the topics of the research. The presented analysis is preliminary and can be relevant for future in- depth research on the applications of neural networks for the analysis of student performance in higher education.

neural network, higher education, educational data mining, student performance

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