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Figure 4: Decision tree for visual perception Michaela Drozdová et al. Figure 5: Dendrogram of agglomerative clustering analysis Figure 6 presents detail description of the five clusters created. Each cluster is represented by one color in the boxplot chart. The most interesting results are at first four properties. Red cluster is typical by average level at all properties except kinesthetic, auditive and verbal property. Yellow cluster is described by very high values of auditive property. Green cluster is characterized by low value of kinesthetic property, high value of auditive and verbal properties. Blue cluster is described by higher value of kinesthetic, low value of auditive and above average value of verbal property. Black cluster is typical by its high value of visual and auditive property and low value of kinesthetic, social and verbal property. The main finding is that the clusters of students depend mainly on type of perception of students. 192

Figure 6: Box plots of five clusters 3.4 Principal component analysis Michaela Drozdová et al. Principal component analysis found 13 components. The last component has much lower standard deviation compared to the other components (see table 2), so this component can be removed from the analysis. Most of the principal components have high coefficient in the covariance matrix (see table 3) at only one property, so this property is independent. Significant components with more than one high coefficient have high values mostly at sensual properties. This means that sensual properties depend on each other, but that was already known. All other properties are almost entirely independent. Table 2: Standard deviation table of principal components PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11 PC12 PC13 3,46 2,45 2,26 2,09 1,51 1,25 1,08 0,75 0,34 0,31 0,29 0,26 0,00 Table 3: Covariance matrix of principal components PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11 PC12 PC13 visual 0,0 0,1 -0,1 0,0 -0,8 0,0 0,1 0,0 0,0 0,0 0,0 0,0 0,5 kinesthetic 0,0 0,7 0,2 -0,1 0,3 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,5 auditive 0,0 -0,6 0,6 -0,1 0,1 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,5 verbal 0,0 -0,2 -0,7 0,3 0,3 0,0 -0,1 0,0 0,0 0,0 0,0 0,0 0,5 social -1,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 motivation 0,0 -0,1 -0,3 -0,9 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 order 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 -0,7 -0,7 0,2 0,0 0,0 theor/exper 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,1 -0,4 -0,9 0,2 0,0 detail/holistic 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 -0,7 0,6 -0,2 0,4 0,0 surface 0,0 0,0 0,0 0,0 -0,1 1,0 -0,1 0,1 0,0 0,0 0,0 0,1 0,0 strategic 0,0 0,0 0,0 0,0 0,1 0,1 0,9 -0,3 0,0 0,0 0,0 0,0 0,0 deep 0,0 0,0 0,0 0,0 0,1 -0,1 0,3 0,9 0,0 0,0 0,0 0,0 0,0 auto-regulation 0,0 0,0 0,0 0,0 0,0 0,1 0,0 0,0 -0,3 0,2 -0,3 -0,9 0,0 4. Conclusion The newly created LPQ questionnaire has many advantages compared to the group of questionnaires used in our previous research. Its main benefit is lower filling time that led to more honest filling. Analysis of filled questionnaires found only minor dependencies between chosen learning styles, so there is no need to change the set of measured student characteristic. One potential problem is that 193

Figure 4: Decision tree for visual perception<br />

Michaela Drozdová et al.<br />

Figure 5: Dendrogram of agglomerative clustering analysis<br />

Figure 6 presents detail description of the five clusters created. Each cluster is represented by one<br />

color in the boxplot chart. The most interesting results are at first four properties. Red cluster is typical<br />

by average level at all properties except kinesthetic, auditive and verbal property. Yellow cluster is<br />

described by very high values of auditive property. Green cluster is characterized by low value of<br />

kinesthetic property, high value of auditive and verbal properties. Blue cluster is described by higher<br />

value of kinesthetic, low value of auditive and above average value of verbal property. Black cluster is<br />

typical by its high value of visual and auditive property and low value of kinesthetic, social and verbal<br />

property. The main finding is that the clusters of students depend mainly on type of perception of<br />

students.<br />

192

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