Sistem Rekomendasi Personalisasi Pembelajaran Mahasiswa untuk Prediksi Karir dan Sertifikasi Kompetensi yang Tepat
DOI:
https://doi.org/10.55606/jitek.v5i2.5514Keywords:
Personalized learning, recommendation system, career prediction, competency certificationAbstract
In the era of digital and globalization, the need for graduates who have competencies in accordance with industry demands is becoming increasingly important. Students often face difficulties in determining the right direction of learning, both for career development and achieving competency certification. This study aims to develop a personalized recommendation system for student learning that is able to predict appropriate career paths and recommend relevant certifications. This system utilizes a data-driven approach using data mining and machine learning techniques, by processing academic data, interests, expertise, and current industry trends. The recommendation system algorithm used includes a content-based and collaborative approach, which are combined to produce more accurate and adaptive results. This system is designed to provide learning suggestions in the form of courses, additional training, and external certifications that support students' career goals. Initial test results show that the system is able to improve students' understanding of their potential and career prospects. Thus, this system is expected to be an innovative solution in supporting the personalization of future-oriented higher education.
References
[1] A. Mavidou and D. Kakana, “Teachers’ Experiences of a Professional Development Program for Dif-ferentiated Instruction,” Creat Educ, vol. 10, no. 03, pp. 555–569, 2019, doi: 10.4236/ce.2019.103040.
[2] R. M. Ryan and E. L. Deci, “Self-Determination Theory and the Facilitation of Intrinsic Motivation, So-cial Development, and Well-Being Self-Determination Theory,” Ryan, 1985.
[3] “Pakar pendidikanabdurahman”. https://sumbar.antaranews.com/berita/644509/pakar-pendidikan-model-kurikulum-harus-fleksibel-untuk-capai-visi-indonesia-emas
[4] T. Martorella and A. Bucchiarone, “Adaptive and Gamified Learning Paths with Polyglot and .NET In-teractive,” Oct. 2023, [Online]. Available: http://arxiv.org/abs/2310.07314
[5] M. Gilang Akbar, “Mengintegrasikan AI dan Pembelajaran Adaptif untuk Hasil Belajar yang Optimal.” [Online]. Available: https://www.researchgate.net/publication/381472423
[6] R. S. ; Bondie, C. ; Dahnke, and A. Zusho, “How Does Changing ‘One-Size-Fits-All’ to Differentiated In-struction Affect Teaching?”
[7] Y. B. Widodo, S. Sibuea, and M. Narji, “Kecerdasan Buatan dalam Pendidikan: Meningkatkan Pembela-jaran Personalisasi,” Jurnal Teknologi Informatika dan Komputer, vol. 10, no. 2, pp. 602–615, Oct. 2024, doi: 10.37012/jtik.v10i2.2324.
[8] M. L. Bernacki, M. J. Greene, and N. G. Lobczowski, “A Systematic Review of Research on Personal-ized Learning: Personalized by Whom, to What, How, and for What Purpose(s)?,” Dec. 01, 2021, Springer. doi: 10.1007/s10648-021-09615-8.
[9] T. B. Lalitha and P. S. Sreeja, “Personalised Self-Directed Learning Recommendation System,” in Proce-dia Computer Science, Elsevier B.V., 2020, pp. 583–592. doi: 10.1016/j.procs.2020.04.063.
[10] H. Peng, S. Ma, and J. M. Spector, “Personalized adaptive learning: an emerging pedagogical approach enabled by a smart learning environment,” Smart Learning Environments, vol. 6, no. 1, Dec. 2019, doi: 10.1186/s40561-019-0089-y.
[11] V. A. Nguyen, H. H. Nguyen, D. L. Nguyen, and M. D. Le, “A course recommendation model for stu-dents based on learning outcome,” Educ Inf Technol (Dordr), vol. 26, no. 5, pp. 5389–5415, Sep. 2021, doi: 10.1007/s10639-021-10524-0.
[12] S. S. Khanal, P. W. C. Prasad, A. Alsadoon, and A. Maag, “A systematic review: machine learning based recommendation systems for e-learning,” Educ Inf Technol (Dordr), vol. 25, no. 4, pp. 2635–2664, Jul. 2020, doi: 10.1007/s10639-019-10063-9.
[13] W. Intayoad, C. Kamyod, and P. Temdee, “Reinforcement Learning Based on Contextual Bandits for Personalized Online Learning Recommendation Systems,” Wirel Pers Commun, vol. 115, no. 4, pp. 2917–2932, Dec. 2020, doi: 10.1007/s11277-020-07199-0.
[14] H. Li, H. Li, S. Zhang, Z. Zhong, and J. Cheng, “Intelligent learning system based on personalized rec-ommendation technology,” Neural Comput Appl, vol. 31, no. 9, pp. 4455–4462, Sep. 2019, doi: 10.1007/s00521-018-3510-5.
[15] M. C. Urdaneta-Ponte, A. Mendez-Zorrilla, and I. Oleagordia-Ruiz, “Recommendation systems for edu-cation: Systematic review,” Jul. 02, 2021, MDPI AG. doi: 10.3390/electronics10141611.
[16] H. Wang and W. Fu, “Personalized Learning Resource Recommendation Method Based on Dynamic Collaborative Filtering,” Mobile Networks and Applications, vol. 26, no. 1, pp. 473–487, Feb. 2021, doi: 10.1007/s11036-020-01673-6.
[17] A. H. Nabizadeh, J. P. Leal, H. N. Rafsanjani, and R. R. Shah, “Learning path personalization and rec-ommendation methods: A survey of the state-of-the-art,” Nov. 30, 2020, Elsevier Ltd. doi: 10.1016/j.eswa.2020.113596.
[18] N. S. Raj and V. G. Renumol, “A systematic literature review on adaptive content recommenders in per-sonalized learning environments from 2015 to 2020,” Journal of Computers in Education, vol. 9, no. 1, pp. 113–148, Mar. 2022, doi: 10.1007/s40692-021-00199-4.
[19] X. Tang, Y. Chen, X. Li, J. Liu, and Z. Ying, “A reinforcement learning approach to personalized learn-ing recommendation systems,” British Journal of Mathematical and Statistical Psychology, vol. 72, no. 1, pp. 108–135, Feb. 2019, doi: 10.1111/bmsp.12144.
[20] F. G. Karaoglan Yilmaz and R. Yilmaz, “Student Opinions About Personalized Recommendation and Feedback Based on Learning Analytics,” Technology, Knowledge and Learning, vol. 25, no. 4, pp. 753–768, Dec. 2020, doi: 10.1007/s10758-020-09460-8.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Jurnal Informatika Dan Tekonologi Komputer (JITEK)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
