# Predicting the Impact of Academic Key Factors and Spatial Behaviors on Students' Performance
###### Abstract  
Quality education is necessary as it provides the basis for equality in society. It is also significantly important that educational institutes be focused on tracking and improving the academic performance of each student. Thus, it is important to identify the key factors (i.e., diverse backgrounds, behaviors, etc.) that help students perform well. However, the increasing number of students makes it challenging and leaves a negative impact on credibility and resources due to the high dropout rates. Researchers tend to work on a variety of statistical and machine learning techniques for predicting student performance without giving much importance to their spatial and behavioral factors. Therefore, there is a need to develop a method that considers weighted key factors which have an impact on their performance. To achieve this, we first surveyed by considering experts' opinions in selecting weighted key factors using the Fuzzy Delphi Method (FDM). Secondly, a geospatial-based machine learning technique was developed which integrated the relationship between students' location-based features, semester-wise behavioral features, and academic features. Three different experiments were conducted to prove the superiority and predict student performance. The experimental results reveal that Long Short-Term Memory (LSTM) achieved higher accuracy of 90.9% as compared to other machine learning methods, for instance, Support Vector Machine (SVM), Random Forest (RF), Naive Bayes (NB), Multilayer Perceptron (MLP), and Decision Tree (DT). Scientific analysis techniques (i.e., Fuzzy Delphi Method (FDM)) and machine learning feature engineering techniques (i.e., Variance Threshold (VT)) were used in two different experiments for selecting features where scientific analysis techniques had achieved better accuracy. The finding of this research is that, along with the past performance and social status key factors, the semester behavior factors have a lot of impact on students' performance. We performed spatial statistical analysis on our dataset in the context of Pakistan, which provided us with the spatial areas of students' performance; furthermore, their results are described in the data analysis section. s 2022  
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Predicting the Impact of Academic Key Factors and Spatial Behaviors on Students' Performance [PERSON], [PERSON], [PERSON], [PERSON], [PERSON]  
performance of other activities in the course [6]. Researchers are working on a variety of statistical and machine learning models for predicting the impact of academic factors on different students [7]. Educational Data Mining (EDM) is a field that has been used to analyze academic data [8; 9]. EDM has various applications during the development of a system as it uses different computational methods to detect patterns to analyze large-scale data. The prediction of student results in terms of academic performance, ratings, or grades is a well-known application of Educational Data Mining [10; 11; 12; 13]. Predictive modeling techniques have been considered for students' academic performance [14; 15], in this regard, classification techniques come out to be the most effective for this problem. Data mining can be used in different fields to improve overall efficiency by using pattern analysis [16]. It is possible only by extraction of valuable information [17] from a stored dataset that is undiscovered until now. This extracted useful information will be used later, as it will help in resolving the issues that were previously faced during the development of the structural model. Today, data mining and its applications in the education sector have gained importance more than before. Thus, we can define Educational Data Mining as 'the process to transform raw data from the educational system into useful information that can be used later by the stakeholders for further applications' [18]. In the end, it will assist the educational institutes to review, improve, and strengthen students' learning process.