A data mining approach to forecast students’ career placement probabilities and recommendations in the programming field
The career opportunities in computer programming are vast and rapidly increasing. Skilled software engineers, programmers, and developers are vigorously in demand worldwide. The capability to forecast a student's future career can be helpful in a wide variety of pedagogical practices. Data mini...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Mehran University of Engineering and Technology
2023-04-01
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| Series: | Mehran University Research Journal of Engineering and Technology |
| Online Access: | https://publications.muet.edu.pk/index.php/muetrj/article/view/2736 |
| _version_ | 1797849812332183552 |
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| author | Khalid Mahboob Raheela Asif Najmi Ghani Haider |
| author_facet | Khalid Mahboob Raheela Asif Najmi Ghani Haider |
| author_sort | Khalid Mahboob |
| collection | DOAJ |
| description | The career opportunities in computer programming are vast and rapidly increasing. Skilled software engineers, programmers, and developers are vigorously in demand worldwide. The capability to forecast a student's future career can be helpful in a wide variety of pedagogical practices. Data mining is becoming a more robust tool for analysis and forecasting. Therefore, to forecast career placement probabilities in the programming field, data mining classification and forecast techniques are used in this study to facilitate prospective students to make sensible career decisions. To achieve this objective, passed-out graduates' data is utilized, which comprises features like graduates' educational attainments in pre-university grades, i.e. grades of matriculation and intermediate, programming courses taught in early semesters along with the Cumulative Grade Point Average (CGPA) with the internship experience, gender, and family demographic information. Various multi-way Classification Trees are generated, which could help students to choose a branch with high career placement probabilities. From historical data, the Classification Trees have determined whether the branch is 'Good', 'Satisfactory', or 'Poor' based on the given information. The experimental findings indicate that all the features significantly influence the career placement probabilities in the programming field. |
| first_indexed | 2024-04-09T18:50:13Z |
| format | Article |
| id | doaj.art-110499efac3849709138496e6f0f34fa |
| institution | Directory Open Access Journal |
| issn | 0254-7821 2413-7219 |
| language | English |
| last_indexed | 2024-04-09T18:50:13Z |
| publishDate | 2023-04-01 |
| publisher | Mehran University of Engineering and Technology |
| record_format | Article |
| series | Mehran University Research Journal of Engineering and Technology |
| spelling | doaj.art-110499efac3849709138496e6f0f34fa2023-04-09T11:42:23ZengMehran University of Engineering and TechnologyMehran University Research Journal of Engineering and Technology0254-78212413-72192023-04-0142216918710.22581/muet1982.2302.182736A data mining approach to forecast students’ career placement probabilities and recommendations in the programming fieldKhalid Mahboob0Raheela Asif1Najmi Ghani Haider2Department of Computer Science and Information Technology, N.E.D University of Engineering and Technology, Karachi PakistanDepartment of Software Engineering, N.E.D University of Engineering and Technology, Karachi PakistanDepartment of Computer Science and Information Technology, N.E.D University of Engineering and Technology, Karachi PakistanThe career opportunities in computer programming are vast and rapidly increasing. Skilled software engineers, programmers, and developers are vigorously in demand worldwide. The capability to forecast a student's future career can be helpful in a wide variety of pedagogical practices. Data mining is becoming a more robust tool for analysis and forecasting. Therefore, to forecast career placement probabilities in the programming field, data mining classification and forecast techniques are used in this study to facilitate prospective students to make sensible career decisions. To achieve this objective, passed-out graduates' data is utilized, which comprises features like graduates' educational attainments in pre-university grades, i.e. grades of matriculation and intermediate, programming courses taught in early semesters along with the Cumulative Grade Point Average (CGPA) with the internship experience, gender, and family demographic information. Various multi-way Classification Trees are generated, which could help students to choose a branch with high career placement probabilities. From historical data, the Classification Trees have determined whether the branch is 'Good', 'Satisfactory', or 'Poor' based on the given information. The experimental findings indicate that all the features significantly influence the career placement probabilities in the programming field.https://publications.muet.edu.pk/index.php/muetrj/article/view/2736 |
| spellingShingle | Khalid Mahboob Raheela Asif Najmi Ghani Haider A data mining approach to forecast students’ career placement probabilities and recommendations in the programming field Mehran University Research Journal of Engineering and Technology |
| title | A data mining approach to forecast students’ career placement probabilities and recommendations in the programming field |
| title_full | A data mining approach to forecast students’ career placement probabilities and recommendations in the programming field |
| title_fullStr | A data mining approach to forecast students’ career placement probabilities and recommendations in the programming field |
| title_full_unstemmed | A data mining approach to forecast students’ career placement probabilities and recommendations in the programming field |
| title_short | A data mining approach to forecast students’ career placement probabilities and recommendations in the programming field |
| title_sort | data mining approach to forecast students career placement probabilities and recommendations in the programming field |
| url | https://publications.muet.edu.pk/index.php/muetrj/article/view/2736 |
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