Investigating the Teaching Reform of the "Smart Logistics and Big Data Processing" Course in the Context of Artificial Intelligence

Xinghong Qin; Hainan Li; Yue Wen

doi:10.54097/dvgkjb80

Authors

Xinghong Qin
Hainan Li
Yue Wen

DOI:

https://doi.org/10.54097/dvgkjb80

Keywords:

Artificial Intelligence, Big Data, Logistics, Teaching Reform, Evaluation System

Abstract

This study explores the enhancement of the "Smart Logistics and Big Data Processing" course in the field of artificial intelligence. The aim is to cultivate skilled professionals who can effectively address upcoming challenges in the logistics sector. This study examines the restructuring of the "Smart Logistics and Big Data Processing" course in artificial intelligence to cultivate skilled professionals prepared to tackle forthcoming challenges in the logistics sector. The paragraph discusses the importance of preparation and training for educators and students, highlighting the necessity for teachers to master new technologies and innovative teaching methods, and for students to improve their technical skills by acquiring proficiency in new tools. The study concludes by assessing the fulfillment of research objectives, which include updating course content and establishing an evaluation mechanism. Through the modernization of course content, the implementation of innovative teaching strategies, and the provision of sufficient preparation and training for both students and educators, significant progress has been observed in practical teaching. This study provides significant insights and practical knowledge for educational reform in the areas of smart logistics and big data processing, thus making a positive contribution to forthcoming educational advancements.

Downloads

Download data is not yet available.

References

Akbari M, Do T N A. A systematic review of machine learning in logistics and supply chain management: current trends and future directions[J]. Benchmarking: An International Journal, 2021, 28(10): 2977-3005.

Rahman M M. Applications of the digital technologies in textile and fashion manufacturing industry[J]. Technium: Romanian Journal of Applied Sciences and Technology, 2021, 3(1): 114-127.

Lu H P, Cheng H L, Tzou J C, et al. Technology roadmap of AI applications in the retail industry[J]. Technological Forecasting and Social Change, 2023, 195: 122778.

BAHUGUNA D, KAUR J, SINGH B. Artificial Intelligence's Integration in Supply Chain Management: A Comprehensive Review[J]. European Economics Letter, 2023, 13(3): 1512-1527.

Heizer J H, Render B. Principles of operations management[M]. Pearson Educación, 2004.

Waters D. Logistics An Introduction to supply chain management [M]. Palgrave macmillan, 2021.

Ding Y, Jin M, Li S, et al. Smart logistics based on the internet of things technology: an overview[J]. International Journal of Logistics Research and Applications, 2021, 24(4): 323-345.

Saxena Arora A. The “organization” as an interdisciplinary learning zone: Using a strategic game to integrate learning about supply chain management and advertising[J]. The Learning Organization, 2012, 19(2): 121-133.

Stockard J, Wood T W, Coughlin C, et al. The effectiveness of direct instruction curricula: A meta-analysis of a half century of research[J]. Review of educational research, 2018, 88(4): 479-507.

Klein M. Self-determination theory: Basic psychological needs in motivation, development, and wellness[J]. Sociologicky Casopis, 2019, 55(3): 412-413.

Langley G J, Moen R D, Nolan K M, et al. The improvement guide: a practical approach to enhancing organizational performance[M]. John Wiley & Sons, 2009.

Revell A J, Ayotte B J. Novel approaches to teaching aging and disability: Active learning through design and exploration[J]. The International Journal of Aging and Human Development, 2020, 91(4): 373-380.

Wang G, Gunasekaran A, Ngai E W T, et al. Big data analytics in logistics and supply chain management: Certain investigations for research and applications[J]. International journal of production economics, 2016, 176: 98-110.

Chen C L P, Zhang C Y. Data-intensive applications, challenges, techniques and technologies: A survey on Big Data[J]. Information sciences, 2014, 275: 314-347.

Bhattacharjya J, Ellison A B, Pang V, et al. Creation of unstructured big data from customer service: The case of parcel shipping companies on Twitter[J]. The International Journal of Logistics Management, 2018, 29(2): 723-738.

Wamba S F, Akter S, Edwards A, et al. How ‘big data can make big impact: Findings from a systematic review and a longitudinal case study[J]. International journal of production economics, 2015, 165: 234-246.

Hofmann E, Sternberg H, Chen H, et al. Supply chain management and Industry 4.0: conducting research in the digital age[J]. International Journal of Physical Distribution & Logistics Management, 2019, 49(10): 945-955.