Singularity in Higher Education: Methods for Detection and Classification

Abstract

In a complex world, the education field needs to advance learning challenges that generate new dynamics of innovation and promote their detection in order to disseminate them as benchmarks for differentiated teaching. This work presents a tool that identifies and categorizes singular elements in the university system to build new disruptive educational proposals in the face of future challenges. In this sense, this article develops a methodology for the collection and analysis of information from a sample of a group of 55 schools recognized for their innovative practices. A model is defined, based on an evaluation of 16 variables that relate to the characteristics of the schools and how they operate. Using statistical techniques and artificial intelligence to process the data obtained, we conclude that there are four prototypical models into which the singularity of the schools can be classified. In addition, we analyzed which of the variables initially considered in the model are actually significant, suggesting an improvement to the initial model based on the experimental data. The result aims to provide a useful tool to analyze the schools’ models and levels of innovation, and allow them to focus on which direction they want to direct their updating strategies.

Keywords: educational singularity, disruptive education, higher education, Artificial Intelligence, innovation learning