Neural network used to analyze multiple perspectives concerning computer-based learning environments

The aim of this study is to explore the possibilities of neural networks to support the analysis and representation of the complex qualitative data in behavioral sciences. In this study for testing the methodological possibilities we analysed data of designers', teachers' and students' interpretations of the same educational software. The intentions of three designers concerning the interaction with their own software were compared with the interpretations of three teachers' anticipations of the interaction, and with the actual learning situations of three pairs of students. The particular kind of neural network used for the data analysis was TS-SOM (Koikkalainen, 1994), which is a variant of a self-organizing map SOM algorithm (Kohonen, 1984). On the basis of the results it can be concluded that the method seems to be promising to handle and visualize the data reduction in a systemic manner without oversimplifying the complex data. Furthermore, the method supports the researcher in finding the most essential places where to focus more detailed qualitative analyses. The visualization tools also allow us to verify the interpretations between independent raters, which increases the reliability of qualitative data analysis.