Predicting the Impact of Academic Key Factors and Spatial Behaviors on Students’ Performance
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,...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/19/10112 |
| _version_ | 1827655103026298880 |
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| author | Muhammad Hammad Musaddiq Muhammad Shahzad Sarfraz Numan Shafi Rabia Maqsood Awais Azam Muhammad Ahmad |
| author_facet | Muhammad Hammad Musaddiq Muhammad Shahzad Sarfraz Numan Shafi Rabia Maqsood Awais Azam Muhammad Ahmad |
| author_sort | Muhammad Hammad Musaddiq |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T22:00:02Z |
| format | Article |
| id | doaj.art-e5788636de1747edb66b83ae30ff0fb3 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T22:00:02Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-e5788636de1747edb66b83ae30ff0fb32023-11-23T19:51:15ZengMDPI AGApplied Sciences2076-34172022-10-0112191011210.3390/app121910112Predicting the Impact of Academic Key Factors and Spatial Behaviors on Students’ PerformanceMuhammad Hammad Musaddiq0Muhammad Shahzad Sarfraz1Numan Shafi2Rabia Maqsood3Awais Azam4Muhammad Ahmad5Department of Computer Science, National University of Computer and Emerging Sciences (NUCES), CFD Campus, Chiniot 35400, Punjab, PakistanDepartment of Computer Science, National University of Computer and Emerging Sciences (NUCES), CFD Campus, Chiniot 35400, Punjab, PakistanDepartment of Computer Science, National University of Computer and Emerging Sciences (NUCES), CFD Campus, Chiniot 35400, Punjab, PakistanDepartment of Computer Science, National University of Computer and Emerging Sciences (NUCES), CFD Campus, Chiniot 35400, Punjab, PakistanDepartment of Computer Science, National University of Computer and Emerging Sciences (NUCES), CFD Campus, Chiniot 35400, Punjab, PakistanDepartment of Computer Science, National University of Computer and Emerging Sciences (NUCES), CFD Campus, Chiniot 35400, Punjab, PakistanQuality 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.https://www.mdpi.com/2076-3417/12/19/10112student performanceEducational Data Mininghigher educationspatial analysispredictive modelingFuzzy Delphi Method |
| spellingShingle | Muhammad Hammad Musaddiq Muhammad Shahzad Sarfraz Numan Shafi Rabia Maqsood Awais Azam Muhammad Ahmad Predicting the Impact of Academic Key Factors and Spatial Behaviors on Students’ Performance Applied Sciences student performance Educational Data Mining higher education spatial analysis predictive modeling Fuzzy Delphi Method |
| title | Predicting the Impact of Academic Key Factors and Spatial Behaviors on Students’ Performance |
| title_full | Predicting the Impact of Academic Key Factors and Spatial Behaviors on Students’ Performance |
| title_fullStr | Predicting the Impact of Academic Key Factors and Spatial Behaviors on Students’ Performance |
| title_full_unstemmed | Predicting the Impact of Academic Key Factors and Spatial Behaviors on Students’ Performance |
| title_short | Predicting the Impact of Academic Key Factors and Spatial Behaviors on Students’ Performance |
| title_sort | predicting the impact of academic key factors and spatial behaviors on students performance |
| topic | student performance Educational Data Mining higher education spatial analysis predictive modeling Fuzzy Delphi Method |
| url | https://www.mdpi.com/2076-3417/12/19/10112 |
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