Table 2 Comparison of attention prediction accuracies of tested machine learning methods
From: Predicting students’ attention in the classroom from Kinect facial and body features
Dataset | |||||
|---|---|---|---|---|---|
Method m | A | A’ | B | B’ | C |
1 (Simple tree) | 0.694 | 0.740 | 0.756 | 0.771 | 0.708 |
2 (Medium tree) | 0.638 | 0.712 | 0.636 | 0.674 | 0.717 |
3 (Coarse KNN) | 0.654 | 0,701 | 0.717 | 0.731 | 0.734 |
4 (Medium KNN) | 0.633 | 0.715 | 0.601 | 0.640 | 0.723 |
5 (Weight. KNN) | 0.638 | 0.721 | 0.613 | 0.641 | 0.725 |
6 (Bagged trees) | 0.655 | 0.734 | 0.706 | 0.747 | 0.753 |
7 (Subspace KNN) | 0.615 | 0.703 | 0.680 | 0.712 | 0.738 |