# Predicting Student Achievement Based on Temporal Learning Behavior in MOOCs
## 5 Experiments
### Results
#### 5.5.2 DSP-Based Pattern Adapter  
We constructed feature sequences of 14 lengths per week for multiple features. In order to find behavioral patterns, we need to discretize the values of features. Take the submission order as an example: Since there are 1528 students, the order of submitting an assignment is 1-1528. The first submission is 1, and the last submission is 1528. We discretized the value of the submission order, and the eigenvalue of the first 500 submitting students was set to be value '1'. The eigenvalues of the 501-1000 th students who submitted assignments were discretized to value '2'. The eigenvalues of the 1001-1528 th students who submitted assignments were discretized to value '3'. Similarly, we discretized other features. Then, we used the DSP algorithm to find behavior patterns. The experimental results are as follows.  
The sequential patterns selected in the experiment are all behavioral patterns where students fail the final exam. For students who pass the exam, it is difficult to find sequential patterns, as losers have the same reasons, while winners have their own strategies for success. Several sequential patterns are selected in Table 4. In Table 4, (*) means any number of values, ranging from 0 to infinity.  
The first two sequential patterns, called Quitter 1 and Quitter 2, respectively, reflect the learning behavior of students with learning difficulties. In these two patterns, a value of '2' means that the number of submissions is much larger than the average number of submissions of all people in the group, and a value of '\(-2\)' denotes that the number of submissions is much smaller than the average  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Pattern Name** & **Feature** & **Pattern** & **Accuracy** & **Recall** & **Pattern Type** \\ \hline Quitter 1 & Submission times & (\(\cdot\)) 1 (\(\cdot\)) 2 (\(\cdot\)) 2 (\(\cdot\)) 2 & 72.7\% & 72.1\% & Failed exam \\ Quitter 2 & Submission times & (\(\cdot\)) 2 (\(\cdot\)) 2 (\(\cdot\)) 2 (\(\cdot\)) 2 & 73.6\% & 73.9\% & Failed exam \\ Faineant & Submission order & (\(\cdot\)) 3 (\(\cdot\)) 3 (\(\cdot\)) 3 (\(\cdot\)) & 73.8\% & 70.2\% & Failed exam \\ Cheater & Plagiarism & (\(\cdot\)) 2 (\(\cdot\)) & 73.49\% & 85.00\% & Failed exam \\ \hline \hline \end{tabular}
\end{table}
Table 4: Behavior patterns.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Accuracy** & **Recall** \\ \hline Adjacent-replenished & 89\% & 94\% \\
0-replenished & 90\% & 94\% \\ Global-replenished & 87\% & 89\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: The comparison of three methods that fill missing logs.  
number of submissions. These students have trouble learning in the early stages and need to spend more time completing their assignments. However, in the late phases of learning, they give up doing assignments independently and complete them through plagiarism, so their submission times are far less than the average submission times.  
The third sequential pattern, called Faineant, means that students do not study actively and always submit their assignments at the end. In the submission order, value '3' means that the student submitted his assignment after 1000 students. The experimental outcomes demonstrate that if three groups of assignments submission order are after 1000, the probability of failure of these students is 73.8%. This indicates that the order of submission can reflect students' learning initiative and has an important impact on the final exam.  
The fourth sequential pattern, called Cheater, means that students have serious cheating in their assignments. This means the student has committed serious fraud for a group of assignments. The experimental findings show that 73.49% of the students who cheat seriously for a group of assignments fail the final exam.