## 4 Experimental Results and Discussion
### Results and Discussion
#### 4.2.1 Evaluation of Representation  
In order to evaluate the performance of the representation of the online learning activity sequence proposed in this study, we transformed the logs of students' interaction with VLE in each course as a three-dimensional tensor \(\mathbf{R}^{m\times n\times c}\) by day. Then, we divided the tensor \(\mathbf{R}\) of each course into 10 sub-tensors according to its second dimension, with the proportion of 10-100%; that is, each sub-tensor increases the length of the learning activities sequence by 10%. Sub-tensors with different proportions can be used as learning activity sequences of students in different learning phases. Clearly, the sequence of learning \begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameter** & **Value** & **Description** \\ \hline countEpoch & 500 & Epoch for training autoencoder and prediction model \\ batch\_size & 200 & Size of each batch of training data \\ optimizer & Adam & Adaptive Moment Estimation, which is the most popular optimizer at present \\ learning\_rate & 0.001 & Rate of updating artificial neuron parameters \\ test\_percent & 0.25 & Proportion of dataset split for each course: 75\% for training and 25\% for evaluation \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Training parameters selected in this study. activities is short at the beginning, but it will be longer and longer in the subsequent learning phases. We used the FNN-based classifier in Figure 3 to evaluate the performance of prediction caused by the different proportions of sub-tensors. The experimental results are shown in Figure 5.  
In Figure 5, there are 22 groups of data points, each of which corresponds to a course. Data points with different colors represent the prediction accuracy achieved by using online activity sequences with different proportions of this course. As can be seen from Figure 5, for most courses, a longer learning activity sequence can lead to a higher prediction accuracy, which shows that there is a strong correlation between the online learning activity sequence and the final performance of students. Moreover, 20% of sequences can make the classifier achieve an accuracy of 0.5, and 30% of sequences can achieve an accuracy of more than 0.6, which means that this representation can be used to predict learners' performance as soon as possible. In order to compare the features representing the online learning activity sequence with other types of features, we took the online learning activity sequence, assessment scores and the demographic features of students as the inputs for the FNN-based classifier in Figure 3, used the parameters in Table 3 to train the classifier, and used 25% of the records of each course for evaluation. In the experimental dataset, the demographic features of students include gender, region, highest_education, IMD, age, previous_attempts, studied_credits, and disability. The assessment scores are the percentile scores of students' periodicity assessments in the learning process. The experimental results are shown in Table 4.  
It can be seen from Table 4 that, among the three features, the students' assessment scores can achieve the best prediction results on the classifier. The prediction accuracy obtained using the learning activity sequence is 10% lower than that using assessment scores, while the prediction accuracy obtained using the students' demographic features is the lowest. Although the assessment scores achieve the best prediction results, they can only be obtained after students have completed all learning activities and cannot be used for the early and real-time prediction of performance. The prediction result of the online learning activity sequence reflects its a strong correlation with students' final academic performance, and it can be used to predict students' performance as soon as possible and  
Figure 5: Accuracy of prediction obtained from online learning activity sequences with different proportions. in real time.