Introducing a framework to assess newly created questions with Natural Language Processing
Table 4: Ablation study.
['Features', 'Difficulty Estimation INF', 'Difficulty Estimation SUP', 'Difficulty Estimation RMSE', 'Difficulty Estimation MAE', 'Discrimination Estimation INF', 'Discrimination Estimation SUP', 'Discrimination Estimation RMSE', 'Discrimination Estimation MAE']
The objective of this ablation study is to i) empirically support our choice of features and ii) assess the impact of specific features on the estimation. In all cases, we use Random Forests in the regression module, since it seemed to be the most accurate and robust approach, according to the preliminary experiments; as baseline, we consider the majority prediction.