# Predicting Student Achievement Based on Temporal Learning Behavior in MOOCs
## 1 Introduction  
Recent developments in data mining have led to renewed interest in educational data mining (EDM). Studies on massive open online courses (MOOCs) are an important part [1; 2; 3; 4] of EDM. Compared to traditional classes, students can obtain optimal learning resources in MOOCs using the internet, without considering the location and cost of obtainment. Researchers are committed to improving the effectiveness of learning in many ways. Previous work has tried to predict and improve student achievement in MOOCs by considering courses [5; 6], forums [7], watching behaviors on video [8; 9], quizzes [10], plagiarism [11; 12], and so on. Researchers have found that students' behaviors [13] can reflect their learning situation and achievement, and the overall behaviors (such as the number of assignments done, the number of quizzes passed, and total amount of time spent completing assignments [10]) can be used to predict student achievement. Since students' same overall behaviors may have different meanings with different learning process, the use of overall behaviors, which cannot fully reflect students' learning processes, affects the accuracy of predictions. Table 1 shows four students with similar overall behaviorsthat have different achievements, and the data used in Table 1 are from the data set mentioned in Section 5.1. All students have completed 69 assignments, and their average submission times of all assignments is 2.42, and their average submission order of all assignments is about 770. However, everyone's score varies greatly from 21 to 80, and this means that the overall behaviors cannot fully reflect students' learning processes. The students' learning processes reveal their temporal learning behaviors. Current temporal algorithms that can analyze students' temporal learning behaviors mainly include traditional algorithms [14; 15; 16; 17] and deep learning-based algorithms [18; 19; 20]. Deep learning-based methods such as long short-term memory (LSTM) have achieved outstanding performance in processing temporal issues. However, as the length of a time-series neural network increases, some critical information will be lost and affect the accuracy of the prediction. In addition, behavior patterns contain critical behavior information shared by students, and they are also helpful in revealing students' learning processes. Researchers have mined students' behavior patterns [21; 22]. [PERSON] [21] analyzed log data and revealed four patterns (non-procrastinators, socially focused, individually focused, procrastinators) of online behaviors that predict student achievement. They found that students with procrastination patterns gain low final marks. [PERSON] [22] identified seven types of participant behaviors. In these behavior patterns, the same behavior pattern may exist in different student groups. For example, the behavior pattern of completing assignments with fewer attempts several times may exist in both the student group with excellent achievements and that with continuous plagiarism. This pattern is not useful for us to predict students' achievements. We need to find discriminative behavior patterns, and these patterns help effectively to predict student achievement. In order to solve the problems above, in the present paper, we extract temporal assignment-related behaviors (e.g., submission order sequence, completion time sequence) from logs in a course on MOOCs, describe students' learning processes, and predict students' achievement by considering students' learning processes and discriminative behavior patterns. Our contributions include the following:  
1. We construct an achievement prediction framework with an attention mechanism that fully leverages student temporal behaviors and behavior patterns. 2. We propose a discriminative sequential pattern (DSP) mining algorithm to mine student behavior patterns (such as those defined by Quitter, Faineant, and Cheater), which enhances the importance of critical information and improves the performance of student achievement prediction. 3.