Forecasting the position of science, technology, and innovation in higher education institutions of the world in the global ranking system using artificial neural networks

In recent years, evaluating the performance quality of universities and higher education institutions has become a global priority. To meet this need, numerous ranking systems have been developed, each employing different indicators to assess institutional performance. In Iran, the "Positioning System of Science, Technology, and Innovation of Iran in the World" (NAMA), implemented by the Iranian Research Institute for Science and Technology (IRANDOC), provides regular reports on the status of national universities and higher education institutions according to key global benchmarks. Beyond assessment, however, the ability to predict future performance based on past and current data is a vital component of strategic planning and decision-making. Institutions that can forecast trends with minimal error are better positioned to identify effective strategies and improve their competitiveness in global ranking systems. This study applies artificial neural networks (ANN) to predict the rankings of universities and higher education institutions within the Times Higher Education (THE) framework. The results demonstrate that the proposed ANN model is capable of predicting THE ranking indicators, overall scores, and institutional standings with satisfactory accuracy. These findings highlight the potential of ANN as a decision-support tool for improving the global visibility and strategic planning of universities.

کلیدواژه ها:

Times Higher Education Ranking System ، Time series data ، prediction ، NAMA (Positioning System of Science ، technology ، And Innovation of Iran In The World)