Academic Journal
Decision support system to reveal future career over students' survey using explainable AI.
| Τίτλος: | Decision support system to reveal future career over students' survey using explainable AI. |
|---|---|
| Συγγραφείς: | Faruque, Sakir Hossain, Khushbu, Sharun Akter, Akter, Sharmin |
| Πηγή: | Education & Information Technologies; Jul2025, Vol. 30 Issue 10, p14471-14509, 39p |
| Θεματικοί όροι: | Artificial intelligence, Machine learning, Computer science, Software engineering, Counseling in higher education, Vocational guidance, Natural language processing, Artificial neural networks |
| Περίληψη: | A career is crucial for anyone to fulfill their desires through hard work. During their studies, students cannot find the best career suggestions unless they receive meaningful guidance tailored to their skills. Therefore, we developed an AI-assisted model for early prediction to provide better career suggestions. Although the task is difficult, proper guidance can make it easier. Effective career guidance requires understanding a student's academic skills, interests, extracurricular activities, internships, courses or training, research background, and skill-related activities. In this research, we gathered key data from Computer Science (CS) and Software Engineering (SWE) students to train machine learning (ML) and neural network (NN) models for career path prediction based on career-related information. To adequately train the models, we applied Natural Language Processing (NLP) techniques and completed dataset pre-processing. For comparative analysis, we utilized multiple classification ML algorithms and deep learning (DL) algorithms. This study contributes valuable insights to educational advising by providing specific career suggestions based on the unique features of CS and SWE students. The research also helps individual CS and SWE students find suitable industrial roles, research fields, and higher study fields that match their skills, interests, and skill-related activities. Furthermore, we developed an AI-driven career prediction website system, transforming how students receive career information and ensuring they make educated decisions about their future. [ABSTRACT FROM AUTHOR] |
| Copyright of Education & Information Technologies is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Links: – Type: other Text: Availability: 0 CustomLinks: – Url: https://dx.doi.org/doi:10.1007/s10639-025-13361-7 Name: EDS - Springer Nature Journals (s7799221) Category: fullText Text: View record at Springer |
|---|---|
| Header | DbId: edb DbLabel: Complementary Index An: 186464339 RelevancyScore: 1007 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1007.33422851563 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Decision support system to reveal future career over students' survey using explainable AI. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Faruque%2C+Sakir+Hossain%22">Faruque, Sakir Hossain</searchLink><br /><searchLink fieldCode="AR" term="%22Khushbu%2C+Sharun+Akter%22">Khushbu, Sharun Akter</searchLink><br /><searchLink fieldCode="AR" term="%22Akter%2C+Sharmin%22">Akter, Sharmin</searchLink> – Name: TitleSource Label: Source Group: Src Data: Education & Information Technologies; Jul2025, Vol. 30 Issue 10, p14471-14509, 39p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+science%22">Computer science</searchLink><br /><searchLink fieldCode="DE" term="%22Software+engineering%22">Software engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Counseling+in+higher+education%22">Counseling in higher education</searchLink><br /><searchLink fieldCode="DE" term="%22Vocational+guidance%22">Vocational guidance</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+language+processing%22">Natural language processing</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: A career is crucial for anyone to fulfill their desires through hard work. During their studies, students cannot find the best career suggestions unless they receive meaningful guidance tailored to their skills. Therefore, we developed an AI-assisted model for early prediction to provide better career suggestions. Although the task is difficult, proper guidance can make it easier. Effective career guidance requires understanding a student's academic skills, interests, extracurricular activities, internships, courses or training, research background, and skill-related activities. In this research, we gathered key data from Computer Science (CS) and Software Engineering (SWE) students to train machine learning (ML) and neural network (NN) models for career path prediction based on career-related information. To adequately train the models, we applied Natural Language Processing (NLP) techniques and completed dataset pre-processing. For comparative analysis, we utilized multiple classification ML algorithms and deep learning (DL) algorithms. This study contributes valuable insights to educational advising by providing specific career suggestions based on the unique features of CS and SWE students. The research also helps individual CS and SWE students find suitable industrial roles, research fields, and higher study fields that match their skills, interests, and skill-related activities. Furthermore, we developed an AI-driven career prediction website system, transforming how students receive career information and ensuring they make educated decisions about their future. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Education & Information Technologies is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edb&AN=186464339 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10639-025-13361-7 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 39 StartPage: 14471 Subjects: – SubjectFull: Artificial intelligence Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Computer science Type: general – SubjectFull: Software engineering Type: general – SubjectFull: Counseling in higher education Type: general – SubjectFull: Vocational guidance Type: general – SubjectFull: Natural language processing Type: general – SubjectFull: Artificial neural networks Type: general Titles: – TitleFull: Decision support system to reveal future career over students' survey using explainable AI. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Faruque, Sakir Hossain – PersonEntity: Name: NameFull: Khushbu, Sharun Akter – PersonEntity: Name: NameFull: Akter, Sharmin IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 07 Text: Jul2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 13602357 Numbering: – Type: volume Value: 30 – Type: issue Value: 10 Titles: – TitleFull: Education & Information Technologies Type: main |
| ResultId | 1 |