# Predicting Student Achievement Based on Temporal Learning Behavior in MOOCs
## 3 Framework and Data Pre-Processing  
In this section, we propose an end-to-end achievement-prediction framework that extracts features from interaction logs in MOOCs and predicts students failing to pass the final exam. As shown in Figure 1, the framework is illustrated in four parts. Part (a) is the data pre-processing, part (b) is a multi-layer LSTM neural network, part (c) is a pattern adapter and part (d) is the attention mechanism.  
We carry out the data pre-processing in part (a) and extract temporal features that reflect the students' learning process. Part (b) uses a multi-layer LSTM neural network to predict student  
Figure 1: The student achievement-prediction framework in massive open online courses (MOOCs): (**a**) Data pre-processing; (**b**) multi-layer long short-term memory (LSTM) neural network for predicting student achievement using temporal behaviors; (**c**) pattern adapter for mining behavior patterns that could strengthen critical information; (**d**) attention mechanism to adjust the weight of parts (**b**) and (**c**).  
achievement, considering students' temporal features. The temporal algorithm-based LSTM neural network, using temporal features, can reflect the learning processes of students well. Part (c) finds the discriminative sequential patterns and constructs pattern adapters according to the sequential patterns. Sequence patterns can emphasize the critical information that may be forgotten by the LSTM neural network in part (c). Part (d) adjusts the weights of parts (b) and (c) by using the attention mechanism, obtaining the final prediction result.  
Before extracting temporal features, the data need to be pre-processed. Missing logs or absences of students' assignments will affect the effectiveness of features. Each assignment of each student should have relevant logs, and the logs of each assignment will form a time status with temporal features. Where we found that there were missing logs, these missing logs were replaced with simulated logs. We can do this because all the extracted features are numeric, and thus we can replace the missing data with an adjacent or global value.  
There were three approaches used to fill the missing logs: The first approach used was adjacent replenishment, which means to fill the logs by using the average value of adjacent logs. The usage of adjacent replenishment indicates that the learning behavior of a student is similar to that of an upcoming assignment in the process of learning, and the missing behavior data can be filled by the adjacent learning behavior data. The second technique is global replenishment, which involves filling the logs by the average value of all logs of the given student. Global replenishment means that behaviors in the learning processes are similar for the same student, and thus the actual behavior data can be replaced by the average behavior of all assignments. The third method is 0 replenishment, which implies the status value is replaced with 0. This means that the value of all the features reflecting the students' completion of the assignment are set to 0, regardless of whether the students failed to submit their assignment, or if the assignment they submitted was lost because of unexpected circumstances. We provide the experimental outcomes in Section 5.