A Big-Data-Based Experimental Platform for Green Shipping Monitoring and Its Teaching Application

Yuzhe Zhao, Jingmiao Zhou (), Zhongxiu Peng, Zongyao Wang and Zunkuo Sheng
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Yuzhe Zhao: Collaborative Innovation Center for Transport Studies, Dalian Maritime University, Dalian 116026, China
Jingmiao Zhou: Business School, Dalian University of Foreign Languages, Dalian 116044, China
Zhongxiu Peng: Collaborative Innovation Center for Transport Studies, Dalian Maritime University, Dalian 116026, China
Zongyao Wang: Collaborative Innovation Center for Transport Studies, Dalian Maritime University, Dalian 116026, China
Zunkuo Sheng: Elane Inc., Beijing 100073, China

Sustainability, 2023, vol. 15, issue 11, 1-19

Abstract: The construction of New Business Studies (NBS) in China and big data technology offer an opportunity for teaching reform. Based on the existing teaching resources, professional knowledge, data, and technology, we monitored the dynamics and checked the statistics of air pollutant emissions from ships in global waters. Various techniques of big data analysis and methods of artificial intelligence were employed, including data collection, data fusion, feature analysis, deep learning network, and system testing. Specifically, the scenario of green shipping monitoring was reproduced by virtual reality; experimental learning was carried out, involving five experimental methods, eight experimental steps, and ten interactive operations; and the results of the experimental learning were assessed. In this way, the students had a better cognition of datasets, a deeper understanding of data correlation, and an improved mastery of interactive operations. In addition, the students varied in terms of learning performance, experimental participation, and active performance inspired by individual thinking. Overall, the students were satisfied with the quality of experimental learning.

Keywords: big data technology; teaching method reform; teaching effect evaluation; green shipping; experimental learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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