Manuscript

Article Deep integration of information technology and higher education: an international comparison analysis in CiteSpace Xie Shupei 1, Li Yanhong 2 Han Shuwei 3 and YuJiaguo 4 1.Textbook Construction Center of University of International Business and Economics, xieshupei1987@163.com 2. Academic Division, University of International Business and Economics, liyanhong@uibe.edu.cn 3. Institute of Higher Education, University of International Business and Economics, hanshuwei2195@126.com; 4. Textbook Construction Center of University of International Business and Economics, yujiaguo@uibe.edu.cn Abstract： With the rapid advancements of information technology in recent years, the results of information technology integrate with higher education are fruitful. This study aims to identify the research status quo and development trends of the integration of information technology and higher education using visualization analysis with CiteSpace from international comparison vision. We retrieved published papers (2004-2022) from WOS and CKNI with a topic search related to integration of information technology and higher education. The findings are as follows: the overall trends of research on the integration of information technology and higher education is on the rise and focus on converging hot topics, actively seeking transformation and innovation. Researches in China exhibits stage jumps and hotspot transitions, while researches outside China showing dispersed and sustained hotspots. The long-term evolution characteristics of research hotspots are convergent among scholars inside and outside China. Keywords: Deep integration; Information technology; Higher education; Research hotspots; Visualization 1. Introduction The digital revolution is leading companies, institutions and professionals to a profound transformation and a radical change in their ways of doing, acting and training, and the technological revolution has affected all sectors of our society, including education. For example, simulations are increasingly often used in higher education settings, especially in STEM (science, technology, engineering, and mathematics) education [1], the in-depth integration of artificial intelligence (AI) with higher education [2,3,4], mobile applications propose flipped classroom model [5]. The essence of the informatization, digitization, and intelligence of higher education lies in the continuous integration of information technology and higher education [6]. The technological empowerment of higher education has become a red thread running through the entire process of high-quality development in higher education. Particularly since the "post-COVID-19" era, the separation of time and space in teaching needs and the widespread concern for learner individuality have drawn significant attention in the field of educational technology. The digitization transformation has become a crucial trend in the international higher education sector over the years. Many countries have successively introduced supportive policies. The European Commission [7] considers that “the digital revolution has opened up great opportunities to improve the quality, accessibility and equity of education” by making it possible to learn anytime, anywhere and reduce social barriers. The integration and development of digital technology and higher education, represented by big data, cloud computing, artificial intelligence, and industry-education integration, are driving talent cultivation, teacher development, and the construction of teaching environments in universities. Therefore, examining the hot issues and development trends in the integration of information technology and higher education domestically and internationally, revealing the close connection and mutual promotion between higher education and information technology, holds important theoretical value and practical significance. In this study, we use CiteSpace as an analysis software to make analysis of the research status quo and development trends of the integration of information technology and higher education from international comparison vision. The objectives of this study are as follows: to identify the distribution of core journals related to the integration of information technology and higher education; to recognize the main research workforces in the field of the integration of information technology and higher education from international comparison vision; to detect the research hotspots and emerging trends in the field of the integration of information technology and higher education. This study is significant in three ways as follows. First, the integration of information technology and higher education is a field with critical pedagogical values that is worth exploring. Second, themes, knowledge evolution, and emerging trends are identified through computational analysis using CiteSpace, reducing subjectivity and enhancing credibility and comprehensiveness, as opposed to relying on researchers' interpretation and categorization. Third, the findings provide an overview and insights into the integration of information technology and higher education, particularly since relevant papers in the last decade are reviewed. 2. Data collection and research methods 2.1. Data collection According to [8] an essential approach for assessing the progress of research in a specific field is to graphically represent the distribution of publications across time. Precise and comprehensive retrieval of high-quality literature related to a research topic is considered the foundation and key aspect of bibliometric studies according to the Bradford Law [9]. For this study, the literature is indexed by the “core collection” of WoS and CNKI. Web of Science（WoS）has been long recognized as the most authoritative scientific and technical literature indexing tool that can provide the most crucial areas of science and technology research [10,11] and is often considered to be an ideal data source for bibliometric investigations [12]. Chinese literature mainly comes from China National Knowledge Infrastructure (CNKI). Our data sources were mainly from Citation Indexes known in print as Science Citation Index Expand (SCIE) and Social Science Citation Index (SSCI), of WoS Core Collection （WoSCC） databases, Chinese Social Sciences Citation Index (CSSCI), of CNKI Core Collection databases. The data used in this study have been retrieved on December 31,2022, form the online version of National Library of China. The search strategy adopted combined multiple search queries to ensure the inclusion of more comprehensive literature, and the retrieval strategies are as follows: The retrieval methods primarily relied on "subject terms" for searching. For Chinese literature, topic= "information technology" or "educational technology" or "informatization" or "digitalization”, with additional searches for "higher education" in the results. Keywords like "talent cultivation," "teaching models," are used for further expansion. This process retrieves relevant journal articles on the integration of information technology and higher education from 2004 to 2022. Base on the retrieval strategy described above, we extract 3387 articles. For English literature, topic= "information technology" or "higher education" or "information technology" or "digital* teaching" or "higher education." The language is limited to "English," and the document types are restricted to "articles" and "review articles." The initial search focuses on literature related to "information technology" with the search query: TS= (information technology OR digitalized teaching OR virtual technology OR artificial intelligence). Subsequently, an advanced search is conducted, combining it with terms like "higher education" using "AND," resulting in a retrieval of relevant journal articles on the integration of information technology and higher education from 2004 to 2022. Base on the retrieval strategy described above, we extract 1278 articles. We exclude news reports, book reviews, conference abstracts, solicitations, and other irrelevant documents. 2.2. Research method The visualization of scientific knowledge through social network analyses and graph theory represents a growing field within bibliometric methods. Nine representative software tools specifically developed to analyze the scientific domains through the utilization of science mapping [13]. CiteSpace, a freely available Java-based scientific visualization software package created by Dr. Chen Chaomei [14,15] at Drexel University (USA) [16], has garnered widespread application and global attention due to its advanced and robust functionalities [17] It is a software tool developed to detect, analyze, and visualize patterns and trends in scientific literature, and has three visualization modes: cluster view, time line, and time zone [15]. Its primary goal is to facilitate the analysis of emerging trends in a knowledge domain. In this paper, we use the latest version of CiteSpace (version 6.1) to analysis the knowledge maps in the literature related to integration of information technology and higher education research, and the primary analysis procedures are outlined as follows. First, Concurrent analyses of country, keywords, and burst terms are conducted separately for both Chinese and English literature, along with timeline. The data processing conditions include a time range from 2004 to 2022, divided into 19 time slices with a year per slice setting of 1. Keywords are defined as abstracts, author-provided keywords, and keywords plus. Then, we use the Log-Likelihood Ratio test method (LLR) to present the current status, knowledge foundation, research hotspots, and trends in the field of the integration of information technology and higher education. 3. Results and discussion In this section, the results of the analysis in terms of research status, hot research topics and emerging trends of the integration of information technology and higher education are presented and discussed. 3.1. Research status 3.1.1. Annual publication volume analysis The annual publication volume is a crucial indicator for assessing the development trends of a particular field within a specific time frame. It holds significant importance in analyzing the development patterns of a field and predicting its future trends. In general, the greater the number of publications related to a specific topic, the more it reflects the academic community's attention to that field. In this study, we utilize Excel to statistically analyze the annual publication volume of sample literature (Figure 1). Figure 1. The distribution of the 4665 records from 2004 -2022 As seen in Fig.1, the dashed line represents the trend of publications in WoS, the solid line represents the trend of publications in CNKI, and the dotted line presents the total publication number linear trend from 2004 to 2022. The data indicates a significant disparity in the total number of core literature between Chinese and English research. China has a much higher volume of research literature in this field (3387 articles) compared to the other countries (1278 articles), with a publication output nearly three times higher. As mentioned earlier, the integration of information technology and higher education has been ongoing since its inception, starting with the information technology revolution in the 1960s. With the widespread application of electronic computers and their organic integration with modern communication technology, it has profoundly influenced and promoted the development of higher education. Due to the inclusion of literature, it was not until 2004 that documents were indexed in the WOS (Web of Science) database. The first literature indexed in the WoS core database is "The effects of mandatory and optional use on students' ratings of a computer-based learning package." [18]. Due to the research interval setting, the earliest Chinese literature retrieved is "Strategic Thinking on Higher Education Reform—A Brief Discussion on the Development of High-tech Issues in Higher Education."[19]. From Figure 1 we can see that research on the integration of information technology and higher education has shown an upward trend in the past 20 years, and Chinese and foreign literature research show different trajectories. Chinese literature exhibits three developmental phases. Phase one (2004-2007) is the initial phase, with publications of less than 100 (67-86 papers). In Phase two (2008 – 2015), publications show an explosive growth, with an average publication of 240 per year (216-268 papers), reaching its peak in 2015. This explosive growth can be attributed to the implementation of the "Eleventh Five-Year Plan for the Development of National Education" in May 2007. The plan explicitly outlined the construction goals of higher education informatization, proposed the implementation of relevant policies such as building an educational informatization public service system and service support system. Phase three (2016 -2022) enters a declining trend, with publications from 199 to 106, indicating a rational return after the peak of research interest. In general, it can be concluded that China is in a leading position in the research on the integration of information technology and higher education. English research literature exhibits two developmental phases. Phase one (2004 to 2011) is in stable-growing period, with publications ranging from 13 to 70. Phase two (2012-2022) enters a fast-growing period, with publications ranging from 28 to more than 250, indicating further room for development. Based on the above analysis, we conclude that in the field of the integration of information technology and higher education, Chinese publication reached its peak in 2015 but exhibited overall instability with a declining trend. On the other hand, English research has been relatively stable and shows an upward trend. 3.1.2. Country distribution analysis Table 1 presents the top ten countries in terms of the number of publications in the field of the integration of information technology and higher education from 2004 to 2022. From Table 1 we can find that apart from China, research in this field internationally is generally dominated by developed countries, with significant variations in the number of publications among different countries. The United States, being the most developed country in higher education and information technology globally, has published 226 articles, accounting for 28.54% of the total literature, nearly twice as much as the second-ranked Spain. This clearly demonstrates the research strength of the United States and underscores its prominent academic position globally. Australia, ranked third, contributed 105 articles, constituting 13.26% of the total. The fourth and fifth positions are held by the United Kingdom and Turkey, with 88 and 58 articles, accounting for 11.11% and 7.32%, respectively. Table 1. Top 10 productive countries of integration of information technology and higher education related articles (except China). Ranking Country Counts Percentage (%) 1 USA 226 28.54 2 SPAIN 127 16.04 3 AUSTRALIA 105 13.26 4 ENGLAND 88 11.11 5 TURKEY 58 7.32 6 CANADA 50 6.31 7 MALAYSIA 41 5.18 8 GERMANY 39 4.92 9 SAUDI ARABIA 30 3.79 10 SOUTH AFRICA 28 3.54 3.2. Hot research topics Keywords can provide information about the core content of papers. A knowledge map of keyword co-occurrence could reflect hot topics over time, and burst keywords could indicate frontier topics [20]. Using by running CiteSpace and the "Export-Network summary table" function, we plot Table 2, Figure 2, and Figure 3, respectively. Table 2. Burst keywords in research on the deep integration of information technology and higher education Chinese literature English literature NO keywords N Intermediary Centrality keywords N Intermediary Centrality 1 higher education 401 0.4 user acceptance 20 0.22 2 Vocational education 270 0.27 distance education 50 0.12 3 Talent cultivation 181 0.19 Skill 43 0.12 4 Career Education 112 0.18 online education 89 0.11 5 Artificial Intelligence (AI) 109 0.15 blended learning 199 0.1 6 Teaching Reform 110 0.12 information technology 181 0.08 7 Educational Technology 85 0.12 technology system 128 0.08 8 School-enterprise cooperation 117 0.11 teacher 33 0.08 9 Information Technology 112 0.11 higher education 253 0.07 10 Teaching Mode 74 0.06 college student 133 0.07 11 Integration of Industry and Education 54 0.06 model 52 0.07 12 Major Development 52 0.06 teaching/learning strategy 11 0.06 13 Academic Majors 33 0.05 performance 86 0.05 14 Curriculum System 47 0.04 impact 50 0.05 15 Flipped Classroom 61 0.03 framework 24 0.05 Table 2 lists 15 high-frequency keywords in Chinese and foreign literature. Through the analysis of these high-frequency keywords, academic research hotspots can be identified. Figure 2. Burst detection of the deep integration of information technology and higher education in China: 2004-2022 (TOP20) Note: Since the literature data in this figure is from CNKI, the software exports the keywords in Chinese. The corresponding English for each Chinese keywords are as follows: 高职( higher vocational);工学结合(integration of engineering and education);顶岗实习(on-the-job internship);高职教育(Vocational education);启示(inspiration);课程体系(Curriculum System);职业能力(occupation skills);内涵建设(internal construction);专业设置(Major Development);校企合作(school-enterprise cooperation);高职院校(higher vocational colleges);人才培养(Talent cultivation);职业院校(vocational colleges);翻转课堂(Flipped Classroom);慕课(MOOC);在线教育(online education);互联网+(Internet plus);新形态(New form);人工智能(Artificial Intelligence);产教融合( School-enterprise cooperation). Figure 2 present the top 20 burst terms in Chinese literature. From figure 2 we can see that the citation bursts strength of keywords ranges from 5.17 to 30.8, that is, the citation bursts strength of “Vocational education “is 30.8, and the citation bursts strength of “Major Development “is 5.17. Figure 3 present the top 20 burst terms in foreign literature. From figure 3 we can see that the citation bursts strength of keywords ranges from 4.24 to 22.52, that is, the citation bursts strength of “higher education “is 30.8, and the citation bursts strength of “management “is 5.17. Figure 3. Burst detection of the deep integration of information technology and higher education outside China 2004-2022 (TOP20) The analysis of burst terms reveals instances where the citation frequency of keywords has sharply increased within a short period. This indicates that the research topic in this field has attracted close attention from relevant scholars, reflecting changes and dynamics in the subject area. With the information provided in Table 2, Figure 2 and Figure 3, along with secondary literature analysis, the three main hot research topics similarities and differences faced by Chinese and foreign researchers in the field of the deep integration of information technology and higher education over the past two decades are as follows: 3.2.1. Higher education ecosystem trans from tangible to virtual The transition from tangible to virtual represents the changing landscape of higher education and signifies the profound impact of information technology on the sector. From the perspective of keywords, foreign literature includes terms such as distance education, online education, higher education, and technology system. Chinese literature includes terms like higher education, vocational education, vocational-technical education, school-enterprise cooperation, and industry-education integration. This indicates that higher education institutions worldwide are actively embracing the challenges posed by information technology, seeking partnerships with online education and technology companies, promoting deep integration between industry and education, and exploring changes in educational models. From the perspective of burst keywords, the transformation of the higher education ecosystem is evident through different burst terms and across different time periods. From Figure 3, we can see that in foreign literature, the strength of burst keyword "higher education" is 22.52, and emerged in 2004, gained prominence in 2018, and continued through 2022. From Figure 2, we can see that Chinese literature's burst keywords include “vocational education (2004, 2008-2014)”, “online education (2014, 2014-2016)”, and “Internet+ (2015, 2015-2016)”. From the perspective of burst duration, it is evident that the higher education ecosystem has been continuously evolving under the influence of information technology over the past two decades, bearing the imprints of different stages and technological eras. For instance, some scholars at the beginning of the century emphasized the need to keep pace with modern educational technology, promoting a leap in educational development to meet new challenges. In the post-COIVD era, highlighted the shift from emergency online teaching to widespread normalization, prompting a reevaluation of the power of educational technology. 3.2.2. Learning models become diversified. With the increasing impact of information technology on higher education, there is growing concern about learners' learning methods and efficiency. The cultivation of thinking methods and the avenues for acquiring knowledge are becoming more diversified, reflecting technological characteristics. From the perspective of keywords, foreign literature includes terms such as “information technology”, “college student”, “user acceptance”, “blended learning”, “performance”, “skill”, etc., while Chinese literature includes terms like “artificial intelligence”, “curriculum system”, etc. From the perspective of burst keywords, there is a certain degree of divergence in foreign literature regarding the burst keywords that characterize learners' learning models. Foreign literature mainly includes performance (2008, 2018-2022), motivation (2011, 2018-2022), knowledge (2010, 2018-2022), online (2010, 2018-2022), etc. Chinese literature mainly includes MOOC (2014, 2014-2018), new form (2016, 2016-2018), etc. From the perspective of burst duration, foreign literature shows sustained attention from international scholars over the past four years to the integration of information technology and higher education. Chinese literature, however, reflects periodic hotspots with attention focused on the 2-4-year timeframe. 3.2.3. Continuous Innovation in Teaching Models Innovation in teaching models, supported by information technology, has broken through the traditional teacher-centered instructional structure, establishing a new teaching model that not only allows teachers to play a leading role but also fully reflects the student-centered approach. From the perspective of keywords, foreign literature includes keywords such as teacher, teaching/learning strategy, model, etc., while Chinese literature includes terms like educational technology, talent cultivation, professional development, teaching reform, flipped classroom, etc. These high-frequency keywords extracted from literature mining indicate a series of innovations brought about by the integration of information technology and teaching models, significantly enhancing teaching effectiveness. From the perspective of burst keywords related to teaching models, foreign literature's burst keywords mainly include performance (2008, 2018-2022), classroom (2011, 2019-2022), engagement (2013, 2019-2022), model (2007, 2020-2022), etc. Chinese literature's burst keywords mainly include talent cultivation (2004, 2010-2013), MOOC (2014, 2014-2018), flipped classroom (2014, 2014-2018), etc. From the perspective of burst duration, the impact of information technology on teaching models shows a similar divergence to learning methods. Foreign literature reveals sustained attention from international scholars to the integration of information technology and higher education teaching models over the past 3-4 years. Meanwhile, Chinese literature reflects periodic hotspots with attention focused on about a 4-year timeframe. Over the past 20 years, the burst keywords in foreign literature has shown a scattered pattern in terms of the time points in the literature, such as in 2004, 2005, 2008, 2010, etc. However, the majority of the burst keywords demonstrate a sustained popularity in the recent 4 years (2018-2022). This sustained interest aligns with the previously mentioned data showing a continuous rise in foreign literature, providing mutual confirmation of the continuity of hotspots and annual publication volume data. In contrast to foreign scholars, Chinese scholars have exhibited a changing pattern in tracking research hotspots, showing periodic characteristics. From 2004 to 2008, the main focus was on vocational education, the combination of engineering and vocational education. From 2009 to 2014, the emphasis shifted to vocational colleges, vocational skills, curriculum systems, and talent cultivation. From 2015 to 2018, the focus shifted to online education, Internet+, MOOCs, and flipped classrooms. Since 2018, the main focus has been on artificial intelligence and the integration of industry and education, representing the forefront of research by Chinese scholars in this field. These shifts in burst keywords are likely related to changes in policy guidance in China. For example, in April 2015, the Ministry of Education issued the "Opinions on Strengthening the Construction, Application, and Management of Open Online Courses in Higher Education Institutions." In July 2017, the State Council issued the "Notice on Printing and Distributing the Development Plan for the New Generation of Artificial Intelligence." These policies emphasized the construction of an open and collaborative artificial intelligence technology innovation system. In April 2018, the Ministry of Education issued the "Notice on Printing and Distributing the 2.0 Action Plan for Education Informatization," which continued to promote the deep integration of information technology and higher education, facilitating the advanced evolution from integrated applications to innovative development, deeply embedding throughout the entire process, and driving improvements in teaching, optimization of management, and enhancement of performance. 3.3. Analysis of research trend Cluster analysis considers the concentration of keyword variations, while the timeline views examines the distribution of clusters along the temporal axis. Clustering analysis, as a technique to identify inherent structures in data, can divide the entire dataset into several similar groups known as clusters. This paper focuses on the analysis of keyword cluster layout and timeline views to delineate the concentration level of research hotspots and topics. The basic attributes of the software include Node Types, Keyword, Layout, Clusters, Timeline views, etc. This study utilizes the LLR clustering algorithm and employs the K-means clustering method to name the keyword cluster layout, selecting the top 10 typical clusters with large time spans and complex structures, and the results are shown in Table 3, Table 4, Figure 4 and Figure 5. The cluster numbers are inversely proportional to their sizes, with the largest cluster labeled as #0, and the others following sequentially. The network's characteristic signature (some important information about the network) is displayed in the top left corner of the diagram. From Figure 4 we can see that the Chinese literature keyword co-occurrence network consists of 770 nodes and 1628 edges. From Figure 5 we can see that the foreign literature keyword co-occurrence network is composed of 1523 nodes and 6967 edges. The modularity of a network is a global measurement of its overall structure, where changes in local structures can lead to global alterations. Modularity Q values and average silhouette S values are important indicators for evaluating the overall structural performance of a network. The "average silhouette" measures the homogeneity of clusters, with higher silhouette scores indicating greater consistency among cluster members, providing comparability between clusters. In this study, the modularity Q value for Chinese literature clusters is 0.532 (>0.3), and for foreign literature clusters, the Q value is 0.684 (>0.3). This indicates that the keyword clusters in both types of literature are significant. The average silhouette values for Chinese and foreign literature clusters are 0.8378 and 0.8924, respectively. Generally, when S>0.5, the clustering is considered reasonable, and when S>0.7, the clustering is considered convincing. Therefore, the average silhouette values for both indicate that the clustering results in this study are significantly reliable. Combining the analysis of Table 3, Table 4, and secondary literature, the hot trends and similarities/differences faced by domestic and foreign researchers in the integration of information technology and higher education over the past 20 years are as follows: 3.3.1. Difference in the clustering dimensions The clustering dimensions show difference in Chinese literature and English literature. Foreign scholar researches are reflected in learning modes, teaching strategies, and information technology. From Table 3, we can see that foreign scholars' research on the integration of information technology and higher education can be divided into three groups in terms of clustering trends. The first group consists of #0, #4, #5, #9. This group of clusters reflects the dimensions of the new features of changes in learners' learning modes after the integration of information technology and higher education, namely "blended learning," "web-based learning," "mobile learning," and "inquiry-based learning." These clusters are formed by the inherent meaning clustering of typical keywords such as information technology, virtual learning environment, computational thinking, online, teaching machine, etc. In the teaching environment deeply integrated with information technology and higher education, students' learning modes have broken away from face-to-face learning between teachers and students, with "blended learning" becoming the most important learning mode, and "web-based learning," "mobile learning," and "inquiry-based learning" continuously evolving [21]. Group one reflects the diversity and richness of learners' acquisition of new knowledge in higher education under the influence of information technology, embodying one of the essential characteristics of the deep integration of information technology and higher education. The second group consists of #1, #3, #8. This cluster group comprises "distance education," "higher education," and "teaching strategies," mainly clustered by the meanings of high-frequency keywords such as distance learning, instructional technology, performance, strategy, teacher, flipped learning. The dimensions of clustering are reflected in the technological transformation of teaching in the integration of information technology and higher education, as well as the mutual influence at macro and micro levels. For example, the separation of teaching time and space, new changes in "teaching organization" and "teaching strategies," forcing teachers to enhance information literacy and engage in teaching reforms such as "flipped learning." The third group consists of #6, #7. This cluster group depicts the "technological aspects" of the integration of information technology and higher education, including artificial intelligence and the technology acceptance model. This group of clusters is mainly formed by the inherent meaning clustering of high-frequency keywords such as multimodal learning environment, computer-based assessment (CBA), adaptive learning, perceived ease. The dimension of this cluster group focuses on "artificial intelligence" technologies such as machine learning, semantic understanding, information retrieval, and the "technology acceptance model" proposed by Davis et al. in 1989, which applies rational behavior theory to study users' willingness to accept information systems. It is determined by the joint factors of behavioral intention (attitude towards using) and perceived ease of use. Chinese scholars' research clustering dimensions manifest as overall impact, technological applications, and talent cultivation. Table 3. Keyword Clustering Table for the Integration of Information Technology and Higher Education in Foreign Countries ID Label Size S year LLR, P #0 blended learning 128 0.858 2009 blended learning (69.16, 1.0E-4); higher education (28.95, 1.0E-4); educational technology (21.16, 1.0E-4); online learning (19.96, 1.0E-4); dental education (16.85, 1.0E-4) #1 distance education 94 0.831 2012 distance education (44.53, 1.0E-4); distance learning (31.97, 1.0E-4); teacher education (17.26, 1.0E-4); information literacy (14.57, 0.001); professional development (12.08, 0.001) #2 American Indians 92 0.951 2012 American Indians (18.57, 1.0E-4); American Indian (14.16, 0.001); college students (14.16, 0.001); USA (12.36, 0.001); physical education (12.36, 0.001) #3 teaching/ learning strategies 87 0.856 2010 teaching/learning strategies (47.81, 1.0E-4); improving classroom teaching (37.97, 1.0E-4); pedagogical issues (32.08, 1.0E-4); post-secondary education (21.65, 1.0E-4); media in education (21.65, 1.0E-4) #4 web-based learning 80 0.834 2012 web-based learning (9.92, 0.005); learning management system (9.86, 0.005); context awareness (9.43, 0.005); undergraduate (6.44, 0.05); health systems (5.81, 0.05) #5 mobile learning 79 0.828 2013 mobile learning (18.17, 1.0E-4); information technology (it)(11.09, 0.001); user acceptance (11.09, 0.001); health care (11.09, 0.001); program development (11.09, 0.001) #6 Artificial intelligence 76 0.854 2012 artificial intelligence (30.73,1.0E-4); big data (8.65, 0.005); academic libraries (8.65, 0.005); big data (8.65, 0.005); blended-learning (8.65, 0.005) #7 technology acceptance model 76 0.896 2011 technology acceptance model (13.11, 0.001); service delivery (11.13, 0.001); special education technology (11.13,0.001); technology skills (11.13, 0.001); technology acceptance (10.23, 0.005) #8 higher education 71 0.918 2012 higher education (31.53, 1.0E-4); blended learning (17.73, 1.0E-4); further education (13.51, 0.001); context (13.51, 0.001); virtual learning environment (9.76, 0.005) #9 enquiry-based learning 65 0.92 2011 enquiry-based learning (8.64, 0.005); cancer (8.64, 0.005); distributed cognition (8.64, 0.005); substance abuse (8.64, 0.005); client-centered therapy (8.64, 0.005) Note: Regarding Cluster #2, "American Indians", this clustering group is primarily formed by 92 key words, including "impact" and "college student." The cluster reflects the "core" of the integration of information technology and higher education. If naming based on "college student," it would be more accurate; however, the current label is likely due to the software algorithm prioritizing the appearance year. Nevertheless, the clustering dimension is appropriate. From Table 4 we can see that research on the integration of information technology and higher education in Chinese literature also can be divided into three groups. The first group consists of #0, #2, and #3, focuses on general higher education, vocational education, and common career-oriented education. These clusters, with a large number of keywords and abundant information, such as artificial intelligence and information technology, reflect the overall impact of information technology on higher education. The comprehensive promotion of educational informatization has become a strategic choice for the reform and development of China's education. The second group consists of #1, #5, and #8, emphasizes aspects like school-enterprise cooperation, educational technology, and quality development. These clusters focus on the application of educational technology, collaborative education through school-enterprise cooperation, and the construction of a high-quality development system. This reflects the new direction of information construction in building collaborative education mechanisms and supporting the development of the economy and society. The third group consists of #6, #7, #9, and #4, revolves around talent cultivation, professional development, curriculum construction, and flipped classrooms. These categories focus on reforming talent cultivation models, teaching methods, and curriculum systems, especially in areas like online education, flipped classrooms, and massive open online courses (MOOCs). This reflects how information construction optimizes the layout of subject disciplines, reshapes teaching methods, promotes education reform, advances the sharing of high-quality resources, and establishes a new ecosystem for smart education. Table 4. Keyword Clustering Table for the Integration of Information Technology and Higher Education in China ID Label Size S year LLR, P #0 Higher education 127 0.836 2014 Higher education (297.18, 1.0E-4); Artificial intelligence (153.1, 1.0E-4); Information technology (81.53, 1.0E-4); Higher vocational education (69.37, 1.0E-4); Higher vocational education (42.84, 1.0E-4) #1 school-enterprise cooperation 98 0.853 2008 school-enterprise cooperation (101.58, 1.0E-4); Higher vocational colleges (94.03, 1.0E-4); Higher education (63.29, 1.0E-4); Integration of engineering and education (52.3, 1.0E-4); Secondary vocational (36.08, 1.0E-4) #2 vocational education 57 0.849 2011 Vocational education (125.12, 1.0E-4); Curriculum system (70.94, 1.0E-4); Occupational skills (34.13, 1.0E-4); path (29.39, 1.0E-4); Higher vocational education (20.36, 1.0E-4) #3 Higher vocational 55 0.801 2006 Higher vocational education (182.84, 1.0E-4); Higher education (39.23, 1.0E-4); Educational research paper (28.36, 1.0E-4); Teaching and research edition (20.76, 1.0E-4); Vocational and technical education (19.87, 1.0E-4) #4 Flipped classroom 54 0.856 2013 Flipped classroom (147.16, 1.0E-4); Teaching model (69.12, 1.0E-4); Teaching reform (62.09, 1.0E-4); MOOC (46.93, 1.0E-4); Online education (32.18, 1.0E-4) #5 Education technology 47 0.834 2007 Education technology (81.45, 1.0E-4); inspiration (63.01, 1.0E-4); USA (45.16, 1.0E-4); strategy (40.77, 1.0E-4); China (37.41, 1.0E-4) #6 Talent cultivate 43 0.739 2010 Talent cultivate (149.99, 1.0E-4); Higher education (25.43, 1.0E-4); model (24.31, 1.0E-4); innovation (23.41, 1.0E-4); Curriculum reform (15.06, 0.001) #7 Major development 42 0.819 2014 Major development (70.95, 1.0E-4); Integration of industrial and education (70.27, 1.0E-4); New engineering disciplines (25.19, 1.0E-4); Supply side (18.87, 1.0E-4); Innovation and entrepreneurship (18.87, 1.0E-4) #8 development 38 0.895 2005 Development (43.5, 1.0E-4); system (30.97, 1.0E-4); connotation (25.66, 1.0E-4); construct (24.75, 1.0E-4); quality (22.78, 1.0E-4) #9 curriculum 29 0.857 2010 curriculum (41.14, 1.0E-4); higher vocational (37.71, 1.0E-4); major (33.57, 1.0E-4); regional economy (28.23, 1.0E-4); industrial structure (28.23, 1.0E-4) 3.3.2. Features of research hotspots clustering dimensions The clustering of Chinese and foreign research literature reveals commonalities that reflect new changes and features in the deep integration of information technology and higher education. At the macro level, the overall and global impact of information technology on higher education is more pronounced in Chinese literature, ranking first in clustering with 127 closely related words. In contrast, foreign literature ranks ninth in clustering, composed of 71 closely related words. At the micro level, there is a profound impact on teachers, students, instructional organization, and learning modes, including aspects like distance education, virtual space, and blended learning. However, foreign scholars, compared to Chinese scholars, place a greater emphasis on researching student learning modes and instructional organization. For instance, "blended learning," "mobile learning," and "inquiry-based learning" rank first, fourth, and fifth in clustering, respectively, among foreign scholars. 3.3.3. Timeline perspective evolutionary features The timeline chart of keyword clustering, based on the historical span of nodes and cluster themes within the literature, is distributed along the same horizontal line in chronological order. It can present the historical presentation and evolution of research topics. Circles in the chart represent clustered keywords, and lines indicate the co-occurrence of two keywords in one or more articles, representing the connections between keywords. It is through these connections that the timeline factor is incorporated into the pathway chart. As shown in Figure 4 and Figure 5, the year 2010 marks a significant time boundary. The majority of keyword clusters are distributed before year 2010, while the clusters after year 2010 are generally a continuation, indicating the continuity of research hotspots. This is consistent with the previously mentioned continuous increase in annual publication volumes. The analysis of the timeframe of keyword clustering further reflects the evolving characteristics of research hotspots in the field of the integration of information technology and higher education worldwide. Figure 4. Timeline Chart of Clustering in the Integration of Information Technology and Higher Education from 2004 to 2022 (Chinese) Figure 5. Timeline Chart of Clustering in the Integration of Information Technology and Higher Education from 2004 to 2022 (Foreign) 4. Conclusion This article conducts a visualized quantitative study based on 1,278 foreign language articles from the Web of Science (core collection) and 3,387 Chinese language articles from the CNKI (core journals and CSSCI database). Utilizing the CiteSpace software, the study analyzes the research status of the integration of information technology and higher education from aspects such as publication volume, contributing countries, high-frequency keywords, burst keywords and keyword clustering. The main conclusions drawn from this analysis are as follows: First, Chinese literature of research on the integration of information technology and higher education show different productive phase trend from foreign literature. Although the productive of Chinese literature is far more than foreign literature so far, Chinese literature enters into decrease phase since 2015, while foreign literature enter into rapid increase phase since 2018. The gap in publication quantity between domestic and foreign researchers is gradually widening. Furthermore, analysis of burst keywords reveals a dispersed pattern in the appearance of keywords in foreign literature, but the majority of these keywords show sustained popularity from 2018 to 2022. In contrast, Chinese scholars demonstrate a dynamic approach to tracking research hotspots, displaying a periodic characteristic with transitions usually occurring every 3-4 years. Second, researchers from both China and abroad are converging on common hot topics. Analysis of burst keywords reveals that scholars are collectively addressing issues such as the transition from the tangible to the virtual in the higher education ecosystem, diversification of learning methods, and innovation in teaching models in response to the impact of information technology. This suggests that higher education institutions, learners, and educators worldwide are actively seeking transformation and innovation in the face of challenges posed by information technology. Third, Chinese and foreign scholars demonstrate differences in the concentration of research hotspots. Through keyword clustering, Chinese and foreign literature exhibit distinct clustering orders, indicating differences in clustering dimensions, that is, foreign scholars' clustering dimensions mainly focus on "learning modes," "teaching strategies," and "information technology.”, while Chinese scholars' clustering dimensions are primarily concentrated on "overall impact," "technological applications," and "talent cultivation." Chinese and foreign scholars exhibit convergence in the long-term evolution of research hotspots. The policy recommendations of this paper are as follows: first, Pay attention to deep integration and empowerment for transformation. The deep integration of information technology and educational teaching has become an overarching trend, digitally empowering higher education modernization. This calls for proactive adaptation to the ecological changes brought about by technological innovation, promoting changes in learning methods based on learners' needs, innovating teaching models based on educators' practices, enhancing digital governance systems based on managerial performance, transforming the construction of an educational powerhouse into a conscious action for higher education. Second, the integration of higher education and technology calls for global perspective and future orientation. The rapid development of information technology has connected the world into a single entity. Considering the comprehensive adjustments and intelligent transformations in higher education concerning educational concepts, institutional structures, operational methods, teaching models, and learning methods, the question arises of how to transform research advantages into competitive and developmental advantages, becoming the historical mission entrusted by the era to higher education. Third, we need to shift from quantitative development to qualitative development, both China and the other countries, to better utilization of technology to cultivate top-talents in higher education. Author Contributions: Conceptualization, Shuwei Han; methodology, Shuwei Han and Xiaojun Wang; software, Shuwei Han; validation, Shuwei Han and Yanhong Li;; formal analysis: Shuwei Han and Xiaojun Wang; data curation, Shuwei Han; writing-original draft preparation, Shuwei Han and Yanhong Li; writing-review and editing, Shuwei Han and Yanhong Li; visualization, Shuwei Han; supervision, Xiaojun Wang. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Conflicts of interest: The authors declare no conflicts of interest. Reference 1. Chernikova, O.,  Heitzmann, N. ,  Stadler, M. ,  Holzberger, D. , &  Fischer, F. Simulation-based learning in higher education: a meta-analysis. Review of Educational Research,2020, 90(4). 2. Li Y and Li X, The Artificial Intelligence System for the Generation of Sports Education Guidance Model and Physical Fitness Evaluation Under Deep Learning. Front. Public Health,2022, 10:917053. doi: 10.3389/fpubh.2022.917053 3. Alyahyan, E., & Düştegör, D. Predicting academic success in higher education: literature review and best practices. In International Journal of Educational Technology in Higher Education ,2020, (Vol. 17, Issue 1). Springer. https://doi.org/10.1186/s41239-020-0177-7 4. Baneres, D., Rodriguez-Gonzalez, E., M., & Serra, M. An early feedback prediction system for learners at-risk within a first-year higher education course. IEEE Transactions on Learning Technologies, 2019,12(2), 249–263. https://doi.org/10.1109/TLT.2019.2912167 5. Hao Y and Lan Y . Research and practice of flipped classroom based on mobile applications in local universities from the perspective of self-determination theory. Front. Psychol. 2023, 13:963226. doi: 10.3389/fpsyg.2022.963226 6. Fernández-Batanero, J. M., Cabero-Almenara, J., Román-Graván, P., & Palacios-Rodríguez, A. Knowledge of university teachers on the use of digital resources to assist people with disabilities. the case of Spain. Education and Information Technologies, 2022,27(7), 9015-9029. 7. Comisión Europea. Un nuevo concepto de educación: invertir en las competencias para lograr mejores resultados socioeconómicos. Publications Office of the European Union,2012 8. Li, X. Y., & Chen, Y. H. A visual analysis of the entropy application in the field of domestic social science using CiteSpace. Science and Technology Management Research,2018, 38(13), 259–266. 9. Chen, Y, Chen, C. Liu Z. Hu. Z, Wang. X. The methodology function of CiteSpace mapping knowledge domains, Studies in science of sience,2006, 33(2),242-253. 10. Boyack, K.W., Klavans, R., Bo€rner, K. Mapping the backbone of science. Scientometrics,2005, 64 (3), 351–374. https://doi.org/10.1007/s11192-005-0255-6. 11. Adriaanse, L.S.; Rensleigh, C. Comparing Web of Science, Scopus and Google Scholar from an Environmental Sciences perspective. South Afr. J. Libr. Inf. Sci. 2011, 77, 169–178. [CrossRef] 12. Leeuwen, T.V. The application of bibliometric analyses in the evaluation of social science research. Who benefits from it, and why it is still feasible. Scientometrics,2006, 66 (1), 133–154. https://doi.org/10.1007/s11192-006-0010-7. 13. Cobo, M.J., Lopez-Herrera, A.G., Herrera-Viedma, E., Herrera, F.Science mapping software tools: review, analysis, and cooperative study among tools. J. Am. Soc. Inf. Sci. Technol.2011,62 (7), 1382–1402. https://doi.org/10.1002/asi.21525. 14. Chen, C. Searching for intellectual turning points: Progressive knowledge domain visualization. Proceedings of the National Academy of Science of the United States of America (PNAS), 2004, 101(suppl. 1), 5303–5310. 15. Chen, C. CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 2006, 57(3), 359–377. 16. Liu, Z., Yin, Y., Liu, W., Dunford, M. Visualizing the intellectual structure and evolution of innovation systems research: a bibliometric analysis. Scientometrics,2015, 103 (1), 135–158. https://doi.org/10.1007/s11192-014-1517-y. 17. Hou, J., Hu, Z. Review on the application of CiteSpace at home and abroad. J. Mod. Inf. 2013, 33 (4), 99–103. 18. Garland, K., Noyes, J. The effects of mandatory and optional use on students' ratings of a computer-based learning package. British Journal of Educational Technology, 2004, 35(3), 263–273. 19. Feng Keyi, Feng Qingguo. Strategic thinking on higher education reform—a brief discussion on the development of high-tech issues in higher education. Heilongjiang researches on higher education,1992, (2), 3. 20. Yu, D., Xu, Z., Pedrycz, W., Wang, W. Information Sciences 1968-2016: a retrospective analysis with text mining and bibliometric. Inf. Sci. 2017,48, 619–634. https://doi.org/10.1016/j.ins.2017.08.031. 21. So, H J, Brush T A. Student perceptions of collaborative learning, social presence and satisfaction in a blended learning environment: Relationships and critical factors[J]. Computers & Education, 2008, 51(1):318-336.