Jinheon Baek, Hayeon Kim, Kiwon Kwon

KIPS Transactions on Software and Data Engineering, Vol. 10, No. 1, pp. 35-44, Jan. 2021
https://doi.org/10.3745/KTSDE.2021.10.1.35,   PDF Download:
Keywords: Artificial intelligence, Edutech, Career Exploration Recommendation System
Abstract

Recent advances in the 4th Industrial Revolution have accelerated the change of the working environment, such that the paradigm of education has been shifted in accordance with career education including the free semester system and the high school credit system. While the purpose of those systems is students’ self-motivated career exploration, educational limitations for teachers and students exist due to the rapid change of the information on education. Also, education technology research to tackle these limitations is relatively insufficient. To this end, this study first defines three requirements that education technologies for the career education system should consider. Then, through data-driven artificial intelligence technology, this study proposes a data system and an artificial intelligence recommendation model that incorporates the topics for career exploration, courses, and majors in one scheme. Finally, this study demonstrates that the set-based artificial intelligence model shows satisfactory performances on recommending career education contents such as courses and majors, and further confirms that the actual application of this system in the educational field is acceptable.

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