AUTOMATIC ASSESSMENT OF SPOKEN LANGUAGE PROFICIENCY OF NON-NATIVE CHILDREN
Table 6: Average classification results on 2018 data, spoken, reported both on training and test data, given in terms of Correct Classification (CC), Weighted Kappa (WK), Correlation (Corr).
['Dataset', 'English  [ITALIC] CC', 'English  [ITALIC] WK', 'English  [ITALIC] Corr', 'German  [ITALIC] CC', 'German  [ITALIC] WK', 'German  [ITALIC] Corr']
For measuring the performance, we consider three metrics: Correct Classification (CC), linear Weighted Kappa (WK), Correlation (Corr) between the expert’s scores and the predicted ones. For all the three metrics, the value of 1.0 corresponds to perfect classification; completely wrong classification is 0.0 for CC and WK, −1.0 for Corr. For each indicator, we used the training data to train a feedforward NN by grouping sentences sharing language, proficiency level and session. The low difference between the performance on training and corresponding test sets indicate that the models do not overfit the data.