A Machine Learning Approach to Career Path Choice for Information Technology Graduates

Al-Dossari, Hmood; Nughaymish, F. A.; Alqahtani, Zuhair; Alkahlifah, M.; Alqahtani, Abdullah

doi:10.48084/etasr.3821

Cited by 12 publications

(6 citation statements)

References 18 publications

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“…The studies also emphasize the importance of incorporating additional data and utilizing different algorithms to enhance the performance of recommender systems. Ensemble techniques and machine learning algorithms, such as XGBoost, have been utilized to develop hybrid models that recommend suitable career paths and occupations (Idakwo John et al, 2022;Al-Dossari et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

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Review of literature on Recommender Models for career pathway selection in Competency-Based Education

Kainyu,

Mwadulo,

Munialo

2024

AJSTSS

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In Competency-based Education (CBE), learners acquire skills and knowledge through a personalized and flexible learning path, based on their prior knowledge and skills. To support learners in selecting career pathways that match their interests and competencies, recommender models are widely used. Recommender models (RM) analyze learners' competencies, interests and goals to suggest career pathways suited for them. This paper reviews literature by analyzing recommender models used for career pathway selection in CBE through desk study. First, it explains the concepts of CBE. Secondly, it discusses the types of recommender models used for career pathway selection in CBE. Lastly, it discusses various recommender models developed for career pathway selection highlighting their strengths and weaknesses. Findings from the review reveals that hybrid recommender model is popularly employed for career pathway selection compared to content-based, knowledge-based, and collaborative filtering recommender models as it gives a higher level of accuracy in the recommendation. Overall, this review paper contributes to the growing body of literature on recommender models for career pathway selection in CBE. In addition, it provides valuable insights for practitioners, researchers and emphasizes the need for future research to address the challenges associated with recommender models for career pathway selection in CBE.

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Section: Discussionmentioning

confidence: 99%

“…A recommendation model by (Al-Dossari et al, 2020) called CareerRec was proposed, which uses machine learning algorithms to help IT graduates select a career path based on their skills. Career-Rec was developed and tested using a dataset of 2255 employees in the IT sector in Saudi Arabia.…”

Section: Hybrid Filtering Recommender Model (Hfrm)mentioning

confidence: 99%

Review of literature on Recommender Models for career pathway selection in Competency-Based Education

Kainyu,

Mwadulo,

Munialo

2024

AJSTSS

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“…Dentre os diversos cursos de graduação, conferimos destaque na presente pesquisa aos estudantes da área de informática por reconhecer que a literatura científica sobre esses alunos é escassa (Mahadeo, Hazari, & Potvin, 2020). Isso é preocupante dada a importância de garantirmos uma adequada capacitação desses alunos, afinal é premente a necessidade por profissionais de tecnologia, para a solução de problemas sociais e, ainda, para o impulsionamento do desenvolvimento científico, tecnológico e econômico de uma nação (Al-Dossari et al, 2020).…”

Section: Introductionunclassified

Computer science students and graduates profile: research on professional interests and work context

Bem,

Garbácio,

Henklain

et al. 2024

Br. J. Ed., Tech. Soc.

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“…proposed a job recommendation system based on machine learning algorithms [20]. A career prediction model was proposed by Roy et al This article focused on computer science domain applicants' career area projection [21].…”

Section: Introductionmentioning

confidence: 99%

Appropriate Job Selection Using Machine Learning Techniques

Khan

Paul

Rahman

et al. 2023

Preprint

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Job recruitment is the process of picking a qualified candidate for a vacant position in an organization. Suitable job selection is a challenging task, especially for freshers. Every year, millions of students complete their graduation and come up with many options for choosing their jobs. This job selection procedure depends on various factors. Considering these issues, this research aims to build a model to predict whether a job is suitable or not suitable for a candidate according to their skills, experiences, and desires for the job. In this situation, Machine Learning approaches can be useful. Data were collected from 120 people currently appointed to a job in various fields. Distinct machine learning techniques are used to predict whether they are satisfied with their current job or not. We also find the other performance matrices and compare them with other algorithms to evaluate the performance of the best model. The results show that the random forest technique is the most effective method for forecasting appropriate job selection, with 92% accuracy and 8% error. Based on the findings, this research will become an effective tool for selecting a suitable job based on people's desires.

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A Machine Learning Approach to Career Path Choice for Information Technology Graduates

Cited by 12 publications

References 18 publications

Review of literature on Recommender Models for career pathway selection in Competency-Based Education

Review of literature on Recommender Models for career pathway selection in Competency-Based Education

Computer science students and graduates profile: research on professional interests and work context

Appropriate Job Selection Using Machine Learning Techniques

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