Application of artificial intelligence for feature engineering in education sector and learning science
This study investigates the utilization of artificial intelligence (AI) for feature engineering in the education sector, highlighting its potential to enhance individualized learning and improve academic outcomes. The correlation analysis, performed using a correlation matrix of the feature set, ind...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016824011244 |
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| author | Chao Wang Tao Li Zhicui Lu Zhenqiang Wang Tmader Alballa Somayah Abdualziz Alhabeeb Maryam Sulaiman Albely Hamiden Abd El-Wahed Khalifa |
| author_facet | Chao Wang Tao Li Zhicui Lu Zhenqiang Wang Tmader Alballa Somayah Abdualziz Alhabeeb Maryam Sulaiman Albely Hamiden Abd El-Wahed Khalifa |
| author_sort | Chao Wang |
| collection | DOAJ |
| description | This study investigates the utilization of artificial intelligence (AI) for feature engineering in the education sector, highlighting its potential to enhance individualized learning and improve academic outcomes. The correlation analysis, performed using a correlation matrix of the feature set, indicated that specific pairings of characteristics exhibit a strong association, resulting in the ineffectiveness of conventional models. In order to tackle this issue, we utilized three sophisticated machine learning methodologies: Adaptive Lasso (ALasso), Artificial Neural Networks (ANN), and Support Vector Regression (SVR). The ALasso model discovered several influential characteristics, namely Gender (X5), Education (X1), Hours of Work (X4), and Marital Status (X6), that significantly affect salaries. Subsequently, a comparative evaluation of these methods was conducted using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The results demonstrated that SVR outperformed the other techniques, with the most optimal RMSE of 0.595 and MAE of 0.423. These findings emphasize the significance of using data-driven strategies in policymaking and propose further investigation into the use of AI methods in various educational contexts to improve the identification of features and the performance of models. |
| format | Article |
| id | doaj-art-9b75fb06b6ea44a2a0d2a8a58ac00e10 |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-9b75fb06b6ea44a2a0d2a8a58ac00e102025-01-09T06:13:18ZengElsevierAlexandria Engineering Journal1110-01682025-01-01110108115Application of artificial intelligence for feature engineering in education sector and learning scienceChao Wang0Tao Li1Zhicui Lu2Zhenqiang Wang3Tmader Alballa4Somayah Abdualziz Alhabeeb5Maryam Sulaiman Albely6Hamiden Abd El-Wahed Khalifa7School of Software, Handan University, Handan, Hebei 056000, China; Corresponding author.School of Software, Handan University, Handan, Hebei 056000, ChinaSchool of Software, Handan University, Handan, Hebei 056000, ChinaInformation Section, Handan First Hospital, Handan, Hebei 056000, ChinaDepartment of Mathematics, College of Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaDepartment of Mathematics, College of Science, Qassim University, Buraydah 51452, Saudi ArabiaDepartment of Mathematics, College of Science, Qassim University, Buraydah 51452, Saudi ArabiaDepartment of Mathematics, College of Science, Qassim University, Buraydah 51452, Saudi ArabiaThis study investigates the utilization of artificial intelligence (AI) for feature engineering in the education sector, highlighting its potential to enhance individualized learning and improve academic outcomes. The correlation analysis, performed using a correlation matrix of the feature set, indicated that specific pairings of characteristics exhibit a strong association, resulting in the ineffectiveness of conventional models. In order to tackle this issue, we utilized three sophisticated machine learning methodologies: Adaptive Lasso (ALasso), Artificial Neural Networks (ANN), and Support Vector Regression (SVR). The ALasso model discovered several influential characteristics, namely Gender (X5), Education (X1), Hours of Work (X4), and Marital Status (X6), that significantly affect salaries. Subsequently, a comparative evaluation of these methods was conducted using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The results demonstrated that SVR outperformed the other techniques, with the most optimal RMSE of 0.595 and MAE of 0.423. These findings emphasize the significance of using data-driven strategies in policymaking and propose further investigation into the use of AI methods in various educational contexts to improve the identification of features and the performance of models.http://www.sciencedirect.com/science/article/pii/S1110016824011244Artificial IntelligenceFeature EngineeringEducation SectorMachine LearningForecastingData set |
| spellingShingle | Chao Wang Tao Li Zhicui Lu Zhenqiang Wang Tmader Alballa Somayah Abdualziz Alhabeeb Maryam Sulaiman Albely Hamiden Abd El-Wahed Khalifa Application of artificial intelligence for feature engineering in education sector and learning science Alexandria Engineering Journal Artificial Intelligence Feature Engineering Education Sector Machine Learning Forecasting Data set |
| title | Application of artificial intelligence for feature engineering in education sector and learning science |
| title_full | Application of artificial intelligence for feature engineering in education sector and learning science |
| title_fullStr | Application of artificial intelligence for feature engineering in education sector and learning science |
| title_full_unstemmed | Application of artificial intelligence for feature engineering in education sector and learning science |
| title_short | Application of artificial intelligence for feature engineering in education sector and learning science |
| title_sort | application of artificial intelligence for feature engineering in education sector and learning science |
| topic | Artificial Intelligence Feature Engineering Education Sector Machine Learning Forecasting Data set |
| url | http://www.sciencedirect.com/science/article/pii/S1110016824011244 |
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