全球留学生留学网络时空演化及其影响因素

doi:10.11821/dlxb202004002

地理学报 ›› 2020, Vol. 75 ›› Issue (4): 681-694.doi: 10.11821/dlxb202004002

全球留学生留学网络时空演化及其影响因素

侯纯光^1,^2,³, 杜德斌^1,^2,³(), 刘承良^1,², 桂钦昌^1,², 刘树峰^1,², 覃雄合^1,^2,³

1. 华东师范大学全球创新与发展研究院,上海 200062
2. 华东师范大学城市与区域科学学院,上海 200062
3. 华东师范大学国家教育宏观政策研究院,上海 200062

收稿日期:2018-05-25 修回日期:2019-09-24 出版日期:2020-04-25 发布日期:2020-06-25
作者简介:侯纯光(1990-), 男, 安徽利辛人, 博士, 主要从事世界经济地理与区域创新研究。E-mail: 1570846532@qq.com
基金资助:
国家社科基金重大项目(19ZDA087);华东师范大学优秀博士生学术创新能力提升计划(YBNLTS2019-033)

Spatio-temporal evolution and factors influencing international student mobility networks in the world

HOU Chunguang^1,^2,³, DU Debin^1,^2,³(), LIU Chengliang^1,², GUI Qinchang^1,², LIU Shufeng^1,², QIN Xionghe^1,^2,³

1. Institute for Global Innovation and Development, East China Normal University, Shanghai 200062, China
2. School of Urban and Regional Science, East China Normal University, Shanghai 200062, China
3. National Institutes of Educational Policy Research, East China Normal University, Shanghai 200062, China

Received:2018-05-25 Revised:2019-09-24 Published:2020-04-25 Online:2020-06-25
Supported by:
Major Program of National Social Science Foundation of China(19ZDA087);ECNU Academic Innovation Promotion Program for Excellent Doctoral Students(YBNLTS2019-033)

摘要/Abstract

摘要：

基于联合国教科文组织公布的全球高等学校留学生流动数据,借鉴网络科学,利用社会网络分析方法和负二项回归模型,对2001—2015年全球留学网络时空演化及其影响因素进行研究。主要结论为：① 时序演化特征上,全球留学网络关系流从4921个增加到9137个,平均加权度从8004迅速增加到20834,表明全球留学网络不仅学生数量在增加,且留学路径数量也在不断增加。② 拓扑结构上,从以美国为单一核心,演变成以美国、中国为双核心的拓扑结构,亚洲国家逐渐成为全球留学网络的主体,欧洲国家的中心性逐渐萎缩。③ 空间结构上,全球留学网络具有明显的等级层次性,美国是全球性主导型节点,德国、捷克是区域性主导型节点,次级主导型节点从25个增加到32个,全球留学网络“东向西、南到北”的地理空间格局正在发生变化,中国、澳大利亚等国成为新的区域性中心,区域化成为全球留学网络一个重要趋势。④ 回归分析发现,留学目的地国高等教育质量、高校在校生数量、人均GDP、留学目的地国与留学生来源国间的历史联系及全球化水平与留学生流量呈显著正相关关系,地理距离、语言临近性与留学生流量呈显著负相关关系,表明语言差异的阻抗作用在迅速减弱,地理距离仍然是阻碍学生流动的一个重要因素。

关键词: 留学生, 国际人才流动, 网络结构, 地理空间

Abstract:

Based on the data released by UNESCO on international student mobility in tertiary education from 2001 to 2015, this paper draws on network science to construct a multidimensional weighted directed network heterogeneity model. This model combines the GIS spatial analysis method and the negative binomial regression model to study spatiotemporal evolution and factors influencing international student mobility networks. The results are as follows. First, in terms of the evolutionary characteristics of time series, the linkages of international student mobility networks have increased from 4921 to 9137, and its average weighted degree centrality has rapidly increased from 8004 to 20,834, which indicates that both the number of international students and the choices of overseas routines are mounting. Second, in terms of the topological structure, it has evolved from a single core comprising the United States into dual cores comprising the United States and China. Gradually, Asian countries have become the main body of international student mobility networks, while the centrality of European countries has reduced. Thirdly, in terms of spatial structure, the international student mobility network has a significant core-periphery structure and hierarchical characteristics. The countries in the core, strong semi-periphery and semi-periphery alternate with countries from other tiers. The spatial patterns of the international student mobility network "from east to west, and from south to north" are changing. China and Australia have become the new regional centers of international student mobility networks, meaning that regionalization has become an important trend. Finally, regression analysis shows that the flow of international students correlates positively with the quality of higher education in destination countries, the number of students in colleges and universities, the per capita GDP, the historical links connection between the destination country and the country of origin, and the level of globalization. Geographical distance and linguistic proximity have a significant negative correlation with the flow of international students, indicating that the impediment of language is rapidly weakening, while geographical distance still hinders student mobility.

Key words: international students, international talent mobility, network structure, geospace

侯纯光, 杜德斌, 刘承良, 桂钦昌, 刘树峰, 覃雄合. 全球留学生留学网络时空演化及其影响因素[J]. 地理学报, 2020, 75(4): 681-694.

HOU Chunguang, DU Debin, LIU Chengliang, GUI Qinchang, LIU Shufeng, QIN Xionghe. Spatio-temporal evolution and factors influencing international student mobility networks in the world[J]. Acta Geographica Sinica, 2020, 75(4): 681-694.

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年份	网络规模				集聚性	度中心性	强度中心性
年份	节点数	边数	图密度	直径	平均聚类系数	平均度	平均加权度
2001	203	4921	0.120	3	0.686	24	8004
2002	202	5379	0.132	4	0.699	27	10055
2003	205	6021	0.144	4	0.665	29	11206
2004	207	5906	0.139	5	0.670	29	11386
2005	206	5679	0.134	3	0.709	28	11935
2006	207	5959	0.140	4	0.688	29	12068
2007	208	6103	0.142	4	0.708	29	13092
2008	208	6781	0.157	4	0.670	33	14004
2009	208	7026	0.163	4	0.647	34	14993
2010	207	7226	0.169	5	0.674	35	15510
2011	209	7605	0.175	4	0.677	36	16561
2012	211	7793	0.176	5	0.662	37	16631
2013	209	7712	0.177	4	0.687	37	16971
2014	211	8456	0.191	4	0.667	40	18298
2015	212	9137	0.204	3	0.655	43	20834

变量	模型1	模型2	模型3	模型4
目的地国高等教育质量		0.0431^***		0.0469^***
		(0.0027)		(0.0028)
目的地国高校在校生数量		0.0515^***		0.0524^***
		(0.0010)		(0.0010)
目的地国人均GDP		0.1498^***		0.1508^***
		(0.0027)		(0.0027)
语言临近性			-0.6509^***	-0.1589^***
			(0.0343)	(0.0340)
历史联系			0.4406^***	0.7994^***
			(0.0424)	(0.0414)
地理距离			-0.7300^***	-0.6092^**
			(0.2271)	(0.2419)
目的地国全球化水平	0.2261^***	0.0035	0.3313^***	0.0321
	(0.0246)	(0.0242)	(0.0256)	(0.0254)
来源国全球化水平	0.1717^***	0.0468^**	0.1588^***	0.0397^**
	(0.0188)	(0.0190)	(0.0179)	(0.0190)
常数项	0.9470^***	0.3582^***	0.9059^***	0.3294^***
	(0.0215)	(0.0235)	(0.0274)	(0.0290)
样本数	27510	27510	27510	27510
关系对	1834	1834	1834	1834
Wald chi2	166.65	12026.53	951.78	12409.43
Prob > chi2	0.0000	0.0000	0.0000	0.0000
Log likelihood	-137976.95	-134414.42	-137644.68	-134242.95

全球留学生留学网络时空演化及其影响因素

Spatio-temporal evolution and factors influencing international student mobility networks in the world

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