Education-to-Skill Mapping Using Hierarchical Classification and Transformer Neural Network
Skills gained from vocational or higher education form an essential component of country’s economy, determining the structure of the national labor force. Therefore, knowledge on how people’s education converts to jobs enables data-driven choices concerning human resources within an ever-changing jo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/13/5868 |
| _version_ | 1797528942108737536 |
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| author | Vilija Kuodytė Linas Petkevičius |
| author_facet | Vilija Kuodytė Linas Petkevičius |
| author_sort | Vilija Kuodytė |
| collection | DOAJ |
| description | Skills gained from vocational or higher education form an essential component of country’s economy, determining the structure of the national labor force. Therefore, knowledge on how people’s education converts to jobs enables data-driven choices concerning human resources within an ever-changing job market. Moreover, the relationship between education and occupation is also relevant in times of global crises, such as the COVID-19 pandemic. Healthcare system overload and skill shortage on one hand, and job losses related to lock-downs on the other, have exposed a necessity to identify target groups with relevant education backgrounds in order to facilitate their occupational transitions. However, the relationship between education and employment is complex and difficult to model. This study aims to propose the methodology that would allow us to model education-to-skill mapping. Multiple challenges arising from administrative datasets, namely imbalanced data, complex labeling, hierarchical structure and textual data, were addressed using six neural network-based algorithms of incremental complexity. The final proposed mathematical model incorporates the textual data from descriptions of education programs that are transformed into embeddings, utilizing transformer neural networks. The output of the final model is constructed as the hierarchical classification task. The effectiveness of the proposed model is demonstrated using experiments on national level data, which covers whole population of Lithuania. Finally, we provide the recommendations for the usage of proposed model. This model can be used for practical applications and scenario forecasting. Some possible applications for such model usage are demonstrated and described in this article. The code for this research has been made available on GitHub. |
| first_indexed | 2024-03-10T10:05:31Z |
| format | Article |
| id | doaj.art-5af7b18fbff84cc3a4319964357ccc1e |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T10:05:31Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-5af7b18fbff84cc3a4319964357ccc1e2023-11-22T01:32:36ZengMDPI AGApplied Sciences2076-34172021-06-011113586810.3390/app11135868Education-to-Skill Mapping Using Hierarchical Classification and Transformer Neural NetworkVilija Kuodytė0Linas Petkevičius1Faculty of Mathematics and Informatics, Vilnius University, LT-03225 Vilnius, LithuaniaInstitute of Computer Science, Vilnius University, LT-08303 Vilnius, LithuaniaSkills gained from vocational or higher education form an essential component of country’s economy, determining the structure of the national labor force. Therefore, knowledge on how people’s education converts to jobs enables data-driven choices concerning human resources within an ever-changing job market. Moreover, the relationship between education and occupation is also relevant in times of global crises, such as the COVID-19 pandemic. Healthcare system overload and skill shortage on one hand, and job losses related to lock-downs on the other, have exposed a necessity to identify target groups with relevant education backgrounds in order to facilitate their occupational transitions. However, the relationship between education and employment is complex and difficult to model. This study aims to propose the methodology that would allow us to model education-to-skill mapping. Multiple challenges arising from administrative datasets, namely imbalanced data, complex labeling, hierarchical structure and textual data, were addressed using six neural network-based algorithms of incremental complexity. The final proposed mathematical model incorporates the textual data from descriptions of education programs that are transformed into embeddings, utilizing transformer neural networks. The output of the final model is constructed as the hierarchical classification task. The effectiveness of the proposed model is demonstrated using experiments on national level data, which covers whole population of Lithuania. Finally, we provide the recommendations for the usage of proposed model. This model can be used for practical applications and scenario forecasting. Some possible applications for such model usage are demonstrated and described in this article. The code for this research has been made available on GitHub.https://www.mdpi.com/2076-3417/11/13/5868deep neural networkshierarchical classificationNLPoccupational modelingtransformers |
| spellingShingle | Vilija Kuodytė Linas Petkevičius Education-to-Skill Mapping Using Hierarchical Classification and Transformer Neural Network Applied Sciences deep neural networks hierarchical classification NLP occupational modeling transformers |
| title | Education-to-Skill Mapping Using Hierarchical Classification and Transformer Neural Network |
| title_full | Education-to-Skill Mapping Using Hierarchical Classification and Transformer Neural Network |
| title_fullStr | Education-to-Skill Mapping Using Hierarchical Classification and Transformer Neural Network |
| title_full_unstemmed | Education-to-Skill Mapping Using Hierarchical Classification and Transformer Neural Network |
| title_short | Education-to-Skill Mapping Using Hierarchical Classification and Transformer Neural Network |
| title_sort | education to skill mapping using hierarchical classification and transformer neural network |
| topic | deep neural networks hierarchical classification NLP occupational modeling transformers |
| url | https://www.mdpi.com/2076-3417/11/13/5868 |
| work_keys_str_mv | AT vilijakuodyte educationtoskillmappingusinghierarchicalclassificationandtransformerneuralnetwork AT linaspetkevicius educationtoskillmappingusinghierarchicalclassificationandtransformerneuralnetwork |