# Progressive Teaching Improvement For Small Scale Learning: A Case Study in China
## 4 Experiments and Discussions
### Learning Performance Prediction
#### 4.2.1 Comparison with State-of-the-Art Machine Learning Methods  
In this part, the proposed ANN model is compared with other state-of-the-art machine learning methods, including Logistic Regression, Random Forest, Decision Trees, and SVM. These models have demonstrated excellent performance in predicting students' outcomes on large scale dataset and are computationally cheap. However, these traditional machine learning methods have to fine-tune their hyperparameters to get the best results, which could be time-consuming. Moreover, for different input data, researchers usually have to repeat experiments to fine-tune the hyperparameters and find the best one. In this experiment, four metrics, i.e., Accuracy, Precision, Recall and F1 score are adopted to evaluate different algorithms. Table 4 demonstrates that the proposed ANN model obtains better results than other machine learning methods. Figure 7: Training metrics of the proposed ANN model across 2500 epochs with early stopping. (**a**) Training accuracy; (**b**) Training loss value. Accuracy is used to evaluate the proportion of samples that have been classified correctly. From Table 4, it is shown that ANN performed best among all classifiers of 73.69% with 16 lessons, followed by Random Forest with 65.27%. Precision and recall metrics are widely used in data science to evaluate the performance of models. Precision indicates the ability of classifiers that predict labels correctly, and recall shows how accurately the model to assign true labels has been used. Since the task is to classify students' final scores into 3 classes, the precision and recall values are calculated separately for each category. The macro-average strategy is used when the number of these three classes is almost equal. The precision and recall values of all proposed models are displayed in Table 4. It can be observed that ANN outperforms other methods significantly after data from four lessons are used. At the same time, Logistic Regression shows relatively good results only when very few lessons data are available. F1 score is defined as the harmonic mean between precision and recall, which can provide a more realistic measure of a model's performance. The F1 score of all models is listed in Table 4. ANN again shows relatively good results along with the course's progress and performs best when 4, 8, 12 and 16 lessons data are used. Logistic Regression outperforms other methods only with less than 4 lessons. The F1 score of all compared models running with different amounts of data is illustrated in Figure 8a. It shows that traditional methods like Random Forest and SVM outperform the proposed ANN model only before Week 4 because too few data are involved. After Lesson 4, the proposed model outperforms other methods and gets better performance when more data introduced. At the end of the course, the F1 score of the proposed method reaches 0.7372 in Lesson 16. Figure 8b shows the F1 score of the proposed ANN model and the baseline when comment features were not used. All models performed poorly in this task. Machine learning methods present similar results with their performance in Figure 8a, while the proposed ANN model shows plausible results when both comment features and more lessons' data are used. This indicates that comment information is crucial to improve the ANN's performance. The reason that ANN and machine learning models have different performance on predicting accuracy is that machine learning models often lack