Effective sampling for large-scale automated writing evaluation systems
(c) K-means
['Set', '[ITALIC] m 10', '[ITALIC] m 20', '[ITALIC] m 30', '[ITALIC] m 40', '[ITALIC] m 50', '[ITALIC] m 60', '[ITALIC] m 70', '[ITALIC] m 80', '[ITALIC] m 90', '[ITALIC] m 100']
’s models over the baseline. To test for significance, we transform the correlations using the Fisher Z-transform before applying the two-sample t-test at the 0.001 level. The test is an approximation, as the normality assumption of the t-test does not hold, but the correlation coefficients are unimodal and the sample sizes are relatively large, so we assume that the approximation is quite good. For interpretability, the percent change is calculated using the Pearson correlation. The means of Fedorov and Kennard-Stone results are significantly different from the baseline for most ASAP AES essay sets and training set sizes Fedorov and Kennard-Stone, with the exception of the anomalous essay set 4. The k-means results are more often indistinguishable from the baseline.