Abstract
The aim of this study is to explore thepossibilities of neural networks to support theanalysis and representation of the complex qualitativedata in behavioral sciences. In this study for testingthe methodological possibilities we analysed data ofdesigners', teachers' and students' interpretations ofthe same educational software. The intentions of threedesigners concerning the interaction with their ownsoftware were compared with the interpretations ofthree teachers' anticipations of the interaction, andwith the actual learning situations of three pairs ofstudents. The particular kind of neural network usedfor the data analysis was TS-SOM (Koikkalainen, 1994),which is a variant of a self-organizing map SOMalgorithm (Kohonen, 1984). On the basis of the resultsit can be concluded that the method seems to bepromising to handle and visualize the data reductionin a systemic manner without oversimplifying thecomplex data. Furthermore, the method supports theresearcher in finding the most essential places whereto focus more detailed qualitative analyses. Thevisualization tools also allow us to verify theinterpretations between independent raters, whichincreases the reliability of qualitative dataanalysis.
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Häkkinen, P.M.H. Neural Network Used to Analyze Multiple Perspectives Concerning Computer-Based Learning Environments. Quality & Quantity 34, 237–258 (2000). https://doi.org/10.1023/A:1004763519135
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DOI: https://doi.org/10.1023/A:1004763519135