A CFBPN artificial neural network model for educational qualitative data analyses: Example of students attitudes based on Kellerts typologies

Abstract

In this study, artificial neural networks are suggested as a model that can be 'trained' to yield qualitative results out of a huge amount of categorical data.It can be said that this is a new approach applied in educational qualitative data analysis.In this direction, a cascade-forward back-propagation neural network (CFBPN) model was developed to analyze categorical data for determine students' attitudes.The data were collected using a conceptual understanding test which includes open-ended questions.The results of this study indicate that using CFBPN model in analyzing data from educational research examining attitudes, behaviors, or beliefs may help us obtain more detailed information about the data analyzed and hence about the characteristics of the participants involved.