The most decisive study difficulties identified by a cluster analysis examining STEM higher education

Abstract

The main goal of the research is to create a value-added model for STEM higher education. In this research we approached dropout as a loss element with a special examination. The target was to identify the background factors proved to be the most decisive study difficulties that could be converted into the value-added model. Another important research objective was to examine the educational attitudes, similarities, and differences between institutional and higher education groups of freshstart and of real dropout. We identified cluster analysis as a well-suited method for answering research questions. By using the cluster analysis R Project Rankcluster, we have made homogeneous groups of study difficulties rankings visible, by treating responses to the further higher education plans as a second dimension. Two cluster analyses were carried out to distinguish institution and higher education loss. The findings of these analyses show that "interest in other training" increases the chances of staying in the higher education. Other decisive factors such as "critical subject(s)", "lecturer was not inspiring" proved to be also important. Due to the findings the most important background factors became identifiable, so we can move forward a leaner model to the essence of value added of STEM higher education.