One Case of THOUGHT: Industry-University Converged Education Practice on Open Source

Abstract

Nowadays, more and more data are produced in our life. The rapid development of big data and intelligent computing industry has put forward new demands for talents, while colleges and universities with limited industry experiences are striving to promote the talent cultivation. Therefore, the "Innovative Community of Industry-Education Convergence", named THOUGHT (innovaTive Hub for the cO-development of indUstry and hiGHer educaTion) [1], was founded in 2019. THOUGHT aims to improve education cooperation between teaching staff and industry professionals in the related field. The industry-university converged courses are different from technical trainings in enterprises or the speeches of the leaders. In order to cultivate talents, both sides of industry and education cooperate closely in nearly every aspects in course building, e.g., targets, contents, methods, and assessments. And it is necessary to make the courses systematic for learners in terms of knowledge transmission, ability training and value shaping, so as to make them more competent for challenging work in the future of big data and AI. The industry-university converged courses focus on breaking the barrier of the traditional campus. Compared with the emphasis on theories and ideal experiments of traditional campus, it focuses more on practices and applications. Therefore, it needs to rely on a bridge which is convenient for both sides to participate in. Compared with traditional tools of business platforms, open source platforms are better choices. And open source makes it easier for more learners to stand on the giants’ shoulders. This paper shares the experience of a case of THOUGHT: the industry-university converged course "Foundamentals and Application of Distributed Data System" jointly built by Tsinghua University and Greenplum open source community. The target, team, organization, structure and contents, practice project, and future work of this course are all introduced in this paper, which may be a vivid case for professors and professionals in this field.

Appendix

Part of the results regarding the course project are shown in the appendix below.

As Fig. 6 shows, Guangzhou and Shenzhen both have high year-on-year growth in POI density. In terms of mixing degree, Guangzhou increased year-on-year and Shenzhen decreased year-on-year. However, due to the small number of increase and decrease, we assume that the mixing degrees of the two cities have not changed a lot year-on-year. Both cities showed a trend of “increase in density and no change in mixing degree”, which means they recover well. Comparing Guangzhou and Shenzhen side by side, Shenzhen has higher density than Guangzhou in both 2019 and 2020, but Shenzhen is far inferior to Guangzhou in terms of mixing degree.

Fig. 6.

Urban Vitality Index of Guangzhou and Shenzhen

Figure 7 is a scatter bubble chart, reflecting the year-on-year change in the number of business people and workers in the workforce. As the scatter plot shows, the relative changes of these two indicators are positively correlated. And most cities with large business population are concentrated in the third quadrant. In other words, the business population and the labor force reduced in the same ratio. The distribution of these two indicators is not a normal distribution, but it has a certain degree of discrimination and representativeness.

Fig. 7.

Scatter Bubble Chart Regarding the Population Change

Figure 8 shows the number of business people in 50 cities during the day and its year-on-year change. It can be seen that the business population in most cities has declined year-on-year. While in districts with large business population, the business population has basically shown a year-on-year decline. In a horizontal comparison, the business population in Jiangsu, Zhejiang and Shanghai has the best year-on-year growth trend. Although Chongqing had a large business population before, it has experienced a significant year-on-year decline. The number of business people in the southern and north-eastern regions, as well as the coastal regions of South China, basically showed a downward trend.

Fig. 8.

Business Population and its Year-on-Year Change During Daytime

Figure 9  shows the number of labor force and its year-on-year change. The figure shows that the labor force in most cities has decreased year-on-year, but the overall situation is better than that of the business population during the daytime. In a horizontal comparison, the labor force in Jiangsu, Zhejiang, Shanghai and the coastal areas of South China show the best year-on-year growth trend. And the labor force in Shanghai showed an upward trend particularly. The South, Central China, and North-east regions all show a downward trend in terms of business population and labor force.

Fig. 9.

Labor Force Population and its Year-on-Year Change

Figure 10 shows the ratio of commercial and residential population in urban agglomerations. The Yangtze River Delta has the highest increase among the five urban agglomerations, followed by Sichuan-Chongqing Urban Agglomeration. While the other three urban agglomerations have declined in different degrees. For the migration of business people from labor-intensive cities to neighboring second-tier cities, different urban agglomerations show different changes regarding the commercial and residential population.

Fig. 10.

Ratio of Commercial and Residential Population in Agglomeration

The comprehensive evaluation of urban vitality after the epidemic cannot only rely on one indicator. Population and POI are used as different aspects to measure urban vitality, and the conclusions in the analysis have similarities and also differences. In terms of the overall trend, the POI shows a positive trend in the cities studied, but the population shows the overall trends of the 50 cities are still not as good as those before the epidemic. What’s more, it requires the participation of human beings. The indicators of business activities during the daytime show that people's willingness to take part in business activities has not recovered to the level before the epidemic. But the indicators of labor force are better than those of business activities, proving that the recovery potential is still great. On the whole, the epidemic still has a certain impact on the vitality of the city, which is reflected through the decline of the business population and the POI mixing degree. But the recovery potential remains optimistic, which is reflected through the labor force and POI density.