The week's pick
Random Articles
Reseach Article
A Hybrid Recommender Model for Career Pathway Selection in Competency-based Education
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 2 |
| Year of Publication: 2025 |
| Authors: Fridah Kainyu, Mary Mwadulo, Samson Munialo |
10.5120/ijca2025924808
|
Fridah Kainyu, Mary Mwadulo, Samson Munialo . A Hybrid Recommender Model for Career Pathway Selection in Competency-based Education. International Journal of Computer Applications. 187, 2 ( May 2025), 82-87. DOI=10.5120/ijca2025924808
Abstract
Learners in Competency-based education (CBE) follow a personalized, flexible learning path based on their prior knowledge and skills. Still, career pathway decisions are frequently influenced by parents, teachers, and career counsellors, missing the critical elements that help learners make informed choices. While recommender models are widely used in education for course selection and career advising, they have typically failed to integrate these diverse factors comprehensively. To address this gap, the study developed a hybrid recommender model that integrates deep neural networks and random forest using the stacking ensemble method to enhance CBE career pathway selection. A mixed-method research design was used, and data were collected through an online survey from teachers teaching junior secondary schools in Meru County, focusing on factors influencing career pathway decisions. SPSS was used for analysis and revealed that academic performance, personal interests, extracurricular activities, career goals, and job market trends are important in these decisions. Hence, a CBE senior school dataset was created, and a hybrid recommender model was developed using the hybrid filtering technique with deep neural networks and random forest algorithms, combined through the stacking ensemble method. K-fold cross-validation was used to validate the model, achieving an accuracy of 90.06% and a precision of 92.07% when used for STEM career pathway tracks compared to existing approaches. These results indicate that the hybrid model is suitable for assisting learners in identifying the right STEM career pathway tracks in CBE. Future work could include the examination of more sophisticated algorithms and the extension of the model to encompass other career pathways.
References
- V. Maphosa and M. Maphosa, “Fifteen Years of Recommender Systems Research in Higher Education: Current Trends and Future Direction,” Appl. Artif. Intell., vol. 37, no. 1, 2023, doi: 10.1080/08839514.2023.2175106.
- S. Raza and C. Ding, News recommender system: a review of recent progress, challenges, and opportunities, vol. 55, no. 1. Springer Netherlands, 2022.
- H. Ko, S. Lee, Y. Park, and A. Choi, “A survey of recommendation systems,” Inf. Resour. Manag. J., vol. 11, no. 1, p. 141, 2022, doi: 10.3390/electronics11010141.
- P. Gavhane, D. Shinde, A. Lomte, N. Nattuva, and S. Mandhane, “Career Path Prediction Using Machine Learning Classification Techniques,” Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol., vol. ISSN, no. 3, pp. 2456–3307, 2021, [Online]. Available: http://ijsrcseit.com.
- N. D. Lynn and A. W. R. Emanuel, “A review on Recommender Systems for course selection in higher education,” IOP Conf. Ser. Mater. Sci. Eng., vol. 1098, no. 3, p. 032039, 2021, doi: 10.1088/1757-899x/1098/3/032039.
- M. F. Wyne, L. Zhang, and A. Farahani, “A Review of Competency Based Education (CBE),” in Proceedings of the 2021 International e-Engineering Education Services Conference, e-Engineering 2021 (pp. 127–132). Institute of Electrical and Electronics Engineers Inc., 2021, pp. 127–132.
- O. T. Kit Kilag, J. A. Dejino, M. T. Arcillo, M. L. Borong, R. G. Manligoy, and L. I. Combista, “Exploring the Determinants of Senior High School Track Preference among Grade 10 Students: A Comprehensive Study,” Univers. J. Innov. Educ., vol. 2, no. 6, p. 2023, 2023, [Online]. Available: https://univerpubl.com/index.php/semantichttps://univerpubl.com/index.php/semantic.
- C. Felaco, A. Zammitti, J. Marcionetti, and A. Parola, “Career Choices, Representation of Work and Future Planning: A Qualitative Investigation with Italian University Students,” Societies, vol. 13, no. 10, pp. 1–11, 2023, doi: 10.3390/soc13100225.
- A. Parola and J. Marcionetti, “Profiles of Career Indecision: A Person-Centered Approach with Italian Late Adolescents,” Eur. J. Investig. Heal. Psychol. Educ., vol. 14, no. 5, pp. 1437–1450, 2024, doi: 10.3390/ejihpe14050095.
- B. F. S. Blanco and L. L. Tingzon, “Predictors of Shs’ Choice on Tvl Track,” Eur. J. Educ. Stud., vol. 11, no. 4, pp. 245–265, 2024, doi: 10.46827/ejes.v11i4.5270.
- Claver V. D., “Career Satisfaction of Filipino Professional Self-Initiated Expatriates in Thailand,” Asian Pacific J. Relig. Cult., vol. 4, no. 1, pp. 40–54, 2020.
- M. Qamhieh, H. Sammaneh, and M. N. Demaidi, “PCRS: Personalized Career-Path Recommender System for Engineering Students,” IEEE Access, vol. 8, pp. 214039–214049, 2020, doi: 10.1109/ACCESS.2020.3040338.
- P. Verma, S. K. Sood, and S. Kalra, “Student career path recommendation in engineering stream based on three-dimensional model,” no. November 2016, pp. 578–593, 2017, doi: 10.1002/cae.21822.
- K. Bhumichitr, S. Channarukul, N. Saejiem, R. Jiamthapthaksin, and K. Nongpong, “Recommender Systems for university elective course recommendation,” Proc. 2017 14th Int. Jt. Conf. Comput. Sci. Softw. Eng. JCSSE 2017, 2017, doi: 10.1109/JCSSE.2017.8025933.
- R. Hernandez and R. Atienza, “Career Track Prediction Using Deep Learning Model Based on Discrete Series of Quantitative Classification,” Appl. Comput. Sci., vol. 17, no. 4, pp. 55–74, 2021, doi: 10.23743/acs-2021-29.
- H. Al-Dossari, F. A. Nughaymish, Z. Al-Qahtani, M. Alkahlifah, and A. Alqahtani, “A Machine Learning Approach to Career Path Choice for Information Technology Graduates,” Eng. Technol. Appl. Sci. Res., vol. 10, no. 6, pp. 6589–6596, 2020, doi: 10.48084/etasr.3821.
- B. Mondal, O. Patra, S. Mishra, and P. Patra, “A course recommendation system based on grades,” 2020 Int. Conf. Comput. Sci. Eng. Appl. ICCSEA 2020, no. March, 2020, doi: 10.1109/ICCSEA49143.2020.9132845.
- K. T. Idakwo John, Babatunde Joshua Agbogun, “Development of a hybrid students’ career path recommender system using machine learning techniques,” FUW Trends Sci. Technol. Journal, www.ftstjournal.com, vol. Vol. 7 No., pp. 236–242, 2022.
- Y. Zhang, J. Liu, and W. Shen, “applied sciences A Review of Ensemble Learning Algorithms Used in Remote Sensing Applications,” 2022.
- A. Ochirbat et al., “Hybrid occupation recommendation for adolescents on interest, profile, and behavior,” Telemat. Informatics, vol. 35, no. 3, pp. 534–550, 2018, doi: 10.1016/j.tele.2017.02.002.
- A. O. Ogunde and J. O. Idialu, “A recommender system for selecting potential industrial training organizations,” Eng. Reports, vol. 1, no. 3, pp. 1–15, 2019, doi: 10.1002/eng2.12046.
- Q. Ruan, Q. Wu, Y. Wang, X. Liu, and F. Miao, “Effective learning model of user classification based on ensemble learning algorithms,” Computing, 2018, doi: 10.1007/s00607-018-0688-4.
Index Terms
Keywords