# A Predictive Model for Student Achievement Using Spiking Neural Networks Based on Educational Data
## 4 Experimental Studies
### Benchmark Datasets and Evaluation Indicators  
#### 4.1.1 Benchmark Datasets  
Two benchmark datasets are used here to verify the performance of the proposed algorithm. Details of these datasets including xAPI-Edu-Data and UCI datasets are discussed below.  
The first classification dataset is xAPI-Edu-Data from Alibaba Cloud Tianchi, which contains 17 variables related to student grades. More specifically, the dataset contains 480 student records from two semesters of different countries and genders. There are 17 information attribute including current education level, class, courses selected, students raising their hands in class, attendance characteristics, and parents of students, etc. Student grades are divided into three categories ['L', 'M', 'H'], which will serve as the criteria for judging student. Among them, \"L\" (0-59) means failing, \"M\" (60-89) means medium, and \"H\" (90-100) means high score. We analyzed the proportion of students in the three categories of grades, as shown in Figure 4. As can be seen in Figure 4, we found that most of the students were in the middle grades, and the high-scoring students accounted for 29.58% of the total number.  
The dataset contains 480 student records and is divided into training dataset and test dataset. Among them, the training dataset consists of 384 items, accounting for 80% of the total number of samples. The test dataset accounts for 20% of the total number of samples with a total of 96 entries.  
Another categorical dataset is the student performance dataset from the UCI Machine Learning Repository, which consists of 395 student records in mathematics subjects and contains 33 variables related to student achievement. The dataset was collected from two Portuguese schools in the 2005-2006 school year, using school reports and questionnaires. These factors affect final student performance in the dataset, including demographic metrics(e.g., mother's education, household income), social/emotional associations(e.g., alcohol consumption), and school-related expectations (e.g., number of exam failures). The dataset is used under the binary/five-level classification and regression tasks in Table 1.  
The raw values in Table 1 are the true values in the dataset. The classifications in Table 1 refer to the 5 values of the five-level classification system on a 20-point scale. The codes in Table 1 represent 5 types of values, which is convenient for programming.  
We analyzed the proportion of students with different scores in the student performance dataset. As can be seen from Figure 5, the overall pass rate of the students is not bad. The students with excellent grades account for 6.09% of the total number, but 46.95% of them also fail.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Category** & **Excellent/Very Good** & **Good** & **Satisfactory** & **Sufficient** & **Fail** \\ \hline Original value & 16–20 & 14–15 & 12–13 & 10–11 & 0–9 \\ Classification & A & B & C & D & E \\ Encoding & 4 & 3 & 2 & 1 & 0 \\ \hline \end{tabular}
\end{table}
Table 1: The five-level classification system.  
Figure 4: Student performance analysis on xAPI-Edu-Data.  
The dataset is composed of 395 student records, which are divided into training dataset and test dataset. Among them, the training dataset consists of 316 entries, accounting for 80% of the total number of samples. The test dataset has a total of 79 items, accounting for 20% of the total samples.  
To find the best prediction model for student achievement, the training dataset is used to optimize the hyperparameters in the model. The test dataset is used to provide the best solution as the final output of the proposed model, and demonstrate the performance of the proposed model.