Table 7 Evaluation metrics
From: Unlocking teachers’ potential: MOOCLS, a visualization tool for enhancing MOOC teaching
Metric | Formula | Description |
|---|---|---|
Accuracy | \(ACC = \frac{{\sum\nolimits_{i = 1}^{n} {TP_{i} } }}{{\sum\nolimits_{i = 1}^{n} {TP_{i} } + FN_{i} }}\) | The percentage of predictions that are correct |
Precision | \(\Pr ecision_{i} = \frac{{TP_{i} }}{{TP_{i} + FP_{i} }}\) | The percentage of positive predictions that are correct |
Recall | \({\text{Re}} call_{i} = \frac{{TP_{i} }}{{TP_{i} + FN_{i} }}\) | The percentage of positive cases that were predicted as positive |
F1-score | \(F1{ - }score_{i} = \frac{2 \times P \times R}{{P + R}}\) | The Harmonic mean of precision and recall. P represents precision and R represents recall |
Micro-precision | \(Micro_{precision} = \frac{{\sum\nolimits_{i = 1}^{n} {TP_{i} } }}{{\sum\nolimits_{i = 1}^{n} {TP_{i} } + FP_{i} }}\) | The average precision of all classes |
Macro-precision | \(Macro_{precision} = \frac{{\sum\nolimits_{i = 1}^{n} {precision_{i} } }}{n}\) | The sum of each class precision divided by the number of classes |