Vocational Education in the Era of Big Data: Course Design and Optimization Strategy Based on Educational Technology

被引:1
作者
Wang, Yunan [1 ]
Feng, Liang [2 ]
机构
[1] Xi’an Vocational and Technical College, Xi’an
[2] Scegc Architecture Labor University, Xi’an
关键词
big data; course design; educational technology; learning behavior; resource recommendation; vocational education;
D O I
10.3991/ijim.v18i22.52447
中图分类号
学科分类号
摘要
In the era of big data, vocational education is confronted with the challenge of effectively utilizing students’ learning behavior data. With the advancement of information technology, the accumulation of students’ learning trajectories and behavior data presents new opportunities for the optimization of education and teaching. Currently, many studies focus on the analysis of short-term learning behaviors, while comprehensive consideration of both long-and short-term behaviors remains insufficient, limiting the precision of course design and resource recommendations. Therefore, the exploration of an optimization strategy that integrates students’ long-and short-term learning behaviors is urgently needed to enhance the effectiveness of vocational education. This study aims to propose a course design and optimization strategy based on educational technology, with a focus on integrating students’ long-and short-term learning behaviors, thereby presenting corresponding resource recommendation methods and course design plans. The study will provide more personalized and precise teaching solutions for vocational education, promoting the enhancement of educational quality. © 2024 by the authors of this article.
引用
收藏
页码:143 / 158
页数:15
相关论文
共 23 条
[1]  
Muchlas B., Budiastuti P., Khairudin M., Santosa B., Rahmatullah B., The use of personal learning environment to support an online collaborative strategy in vocational education pedagogy course, International Journal of Interactive Mobile Technologies (iJIM), 17, 2, pp. 24-41, (2023)
[2]  
Misbah Z., Gulikers J., Widhiarso W., Mulder M., Exploring connections between teacher interpersonal behaviour, student motivation and competency level in competence-based learning environments, Learning Environments Research, 25, pp. 641-661, (2022)
[3]  
Nidhom A. M., Putra A. B. N. R., Smaragdina A. A., Ningrum G. D. K., Yunos J. M., The integration of augmented reality into MOOCs in vocational education to support education 3.0, International Journal of Interactive Mobile Technologies (IJIM), 16, 3, pp. 20-31, (2022)
[4]  
Song X., Environmental sustainability as a determinant in career decisions: An exploration among recent university graduates, International Journal of Sustainable Development and Planning, 18, 8, pp. 2623-2627, (2023)
[5]  
Lou X. J., Et al., Research status and emerging trends of ideological and political education in nursing in China: A bibliometric analysis, Education Science and Management, 1, 3, pp. 169-178, (2023)
[6]  
Tan L. Y., Du F., Integrating entrepreneurship and innovation education into higher vocational education teaching methods based on big data analysis, Wireless Communications and Mobile Computing, 2022, 1
[7]  
Wu M., Hao X. X., Lv Y., Hu Z. H., Design of intelligent management platform for industry–education cooperation of vocational education by data mining, Applied Sciences, 12, 14, (2022)
[8]  
Meng W., Sumettikoon P., The use of artificial intelligence to enhance teaching effectiveness in vocational education, Eurasian Journal of Educational Research, 98, 98, pp. 266-283, (2022)
[9]  
Zhukova O., Mandragelia V., Alieksieienko T., Semenenko A., Skibina E., Digital technologies for introducing gamification into the education system in the context of the development of industry 4.0, Ingénierie des Systèmes d’Information, 28, 1, pp. 141-147, (2023)
[10]  
Vaganova O. I., Tsarapkina J. M., Zheltukhina M. R., Knyazeva E. G., Krasilnikova J. S., Cross-cutting technologies in education, Amazonia Investiga, 10, 47, pp. 27-34, (2021)