Comparison Research of Hotspots and Trends of Learning Analytics from 2011-2021: A Visualization Analysis Based on CiteSpace

With the development of big data and social informatization, learning analytics became a popular topic in education and received more and more attention from scholars. In order to explore the similarities and differences of learning analytics between home and abroad, CiteSpace was used to compare and analyze 178 articles from the CNKI database and 1056 articles from the WOS database from 2011 to 2021. Hotspots, trends, frontiers of domestic and foreign research in learning analytics were visually analyzed. The findings show that: 1) The number of international publications was higher than that of Chinese publications from 2011 to 2021; 2) While more teacher education universities focus on the field of learning analytics in China, comprehensive universities pay more attention to the topic from the result of international publications. 3) Online learning is a key research area for learning analytics research, both in China and in other countries. 4) The research frontier of learning analytics in China mainly focused on learning prediction, while the research frontier in the international articles mainly focused on “educational data mining”, “big data”, and “the design of learning environments and tools”. The analysis captures the hotspots, trends, frontiers in the field of learning analytics and provides a reference for further research by scholars at home and abroad.

analytics. It was early defined as the use of data and models to predict student gains and behaviors that had the ability to process this information (Siemens & Long, 2011). A team led by Gu defined learning analytics as "a tool for extracting implicit, unknown and potentially application-worthy information or patterns from the vast amount of data in the field of education, as well as a decision-making tool" (Hu et al., 2014).
With the application of the Internet in various fields, online learning began to develop as a new supplement to learning methods, and the data left behind by learners using online learning platforms to learn made it easier to analyze their learning, having an in-depth exploration of the learning process from the behavioral data left by the learners (O'Halloran, 2011). Building analytical models and displaying and interpreting data helped teachers and educational administrators to do their jobs better (Gu et al., 2012). On one hand, big data mindsets and technological innovations also presented opportunities and challenges for learning analytics. Learning analytics can be said to be a product of further development and integration of web analytics, academic analytics, educational data mining, behavioral analytics, etc. (Elias, 2011), acquiring data and forming an educational database were the foundation of educational data decision research (Gu, 2010). Learning analytics subsequently became a new wave in education informatization (Wu et al., 2013). On the other hand, some scholars also sorted out the leading edge and trends in learning analytics-related fields in China. For example, Wang and Yu (2015) reviewed learning analytics from the perspective of big data. He (2016) published The New Development of "Learning Analytics Technology" in China. Mei et al. (2021) studied the research path of international learning analytics and its inspiration. All of them provided some useful insights for the further development of learning analytics research. However, the general trends, similarities and differences in the development of learning analytics at home and abroad from 2011 to 2021 have not been explored. Therefore, this study focuses on comparing the similarities and differences between Chinese and international high-impact research scholars and institutions, hotspots, trends, and leading edge related to learning analytics.
Based on this, this paper aims to figure out the current status, research hotspots and trends of learning analytics through CiteSpase for future research. Through a comparative analysis of Chinese and international learning analytics from 2011 to 2021, the following questions are explored: 1) What are the similarities and differences in the authors and institutions of Chinese and international authors and institutions in learning analytics; 2) What are the similarities and differences in Chinese and international research hotspots and trends in learning analytics research; 3) What are the similarities and differences in Chinese and international research frontiers in learning analytics research.

Source of the Sample
The data samples for this study were obtained from CNKI (China National

Data Processing
The search results on CNKI were exported in Refworks format and converted to identifiable data suitable for CiteSpace analysis, and the search results on WOS were exported in text format and imported into the CiteSpace software developed by Dr. Chen.
In this study, the visualization analysis related to the learning analytics knowledge graph of CNKI and WOS from 2011 to 2021 was performed by CiteSpace 5.8, using visualizations present the structure, patterns, and distribution of scientific knowledge. In this process, the files were restricted to the period from 2011 to 2021; the time slice was set to 1 year; the selected literature sources were "title", "abstract", "author keyword (DE)" and "Keyword+ (ID)"; the threshold was set to "Top N % = 50"; the visualization options were "Cluster View-Static" and "Show Merged Network". The knowledge maps were drawn from three aspects, including authors, research institutions and keywords, and the research themes were sorted out by combining with literature analysis.

Number of Articles Issued Per Year
The WOS is more than that on CNKI, and this number gap reached its maximum in 2020 (difference of 221 articles) (see Figure 1).

Author of the Paper
To search the high-impact authors in CNKI and WOS, the node type is set as "Author" in CiteSpace (see Table 1). Among the authors with the most publications in CNKI, Zhao, W. ranked first (17)   37 articles on learning analytics, followed by Pardo, A. and Rienties, B., both with 21 articles on learning. In general, the number of articles published by scholars from other countries in the field of learning analytics was higher than the number of articles published by scholars in CNKI (see Table 1).
The study generated a co-occurrence map of authors of learning analytics research (see Figure 2 and Figure

Research Institutions
In the node type of CiteSpace, select "Institution", and treat the publishing institutions of different colleges of the same school as the same publishing school, among which the high-impact research institutions in CNKI and WOS are shown in Table 2 Table 2).
The study generated a learning analytics research institution co-occurrence map (see Figure 4 and Figure

The Network of Keywords
The keywords with higher frequency and centrality in the analysis results are the research hotspots. Keywords are the core summary of an article, and there is some relationship between keywords in a text, the more occurrences of a word pair in the same document, the stronger the association between the two topics. Ci-teSpace can measure the literature of a specified field to explore the research hotspots and development trends of a discipline or field, and its keyword co-occurrence can directly reflect the research hotspots and frontier trends of a research field (Chen, 2006). The higher the frequency of keyword co-occurrences is, the higher the point centrality is, and the more important the node is in its field. Except for the basic keyword "learning analytics", the top 10 keyword co-occurrences of learning analytics from 2011 to 2021 are shown in Table 3. The top three most frequent keywords in CNKI learning analytics research are "big data" with 20 occurrences and a centrality of 0.05, followed by "data mining" with 12 occurrences and a centrality of 0.02, and "learning behavior" with 8 occurrences and a centrality of 0.04. The top three most frequent keywords in WOS learning analytics research are: "learning process" with 93 occurrences and a centrality of 0.02, "learning outcomes" with 55 occurrences and a centrality of 0.04. "Learning outcomes" appears 55 times with a centrality of 0.05, and "learning management system" appears 50 times with a centrality of 0.02. As shown in Table 3, the research directions of learning analytics at international publications and Chinese publications are mainly "online learning" and "MOOC". In China, the main research objects are "big data" and "data mining", while in other countries, the research mainly analyzes the learning process and the related learning results. As can be seen from the table, "online learning" and "MOOC" are common keywords in Chinese and international research, which reveal the research focus in the field of learning analytics in the world (see Table  3).

Keywords Cluster
In CiteSpace, the timeline view shows the publication and peak times of articles and terms, while the clustering view provides node and linkage graphs where nodes indicate details of authors, institutions, countries, terms, keywords, cited literature, cited journals, etc (Chen et al., 2010). The keyword clustering results are shown in Figure 6  . It can be seen that "Big Data", "Data Mining" and "Learning Behavior" rank the top three among the 26 categories in CNKI (see Figure 6). The WOS keyword clustering results in a total of 176 entries, with the top three being "MOOC", "learning outcome" and "uncertainty" (see Figure 7).

Timeline of Keywords
The keyword timeline view (Figure 8 and Figure 9) shows the development of hotspots in Chinese and international learning analytics research from 2011 to 2021. From 2011 to 2021, the scope of learning analytics research in China has undergone a more expansive development, and the early hotspots of learning analytics are mainly focused on "big data", "data mining", "learning behavior", and "decision support". "Data mining" and "learning behavior" gradually declined from 2020 to 2021. "Decision support" has been declining in popularity since 2017, and it is obvious that the application of big data on learning was the most important research hotspot of learning analytics. "Optimization of learning" and "learning evaluation" were relatively recent research topics Overall, the scope of learning analytics research has gradually expanded and increased from 2011 to 2021 (see Figure 8)   Voice of the Publisher learning analytics, with research such as "achievement goal theory", "evaluation facilitation strategies", "learning analytics dashboard" and "web mining". After 2016, research on "learning outcome", "evaluation facilitation strategies", and "learning analytics dashboard" began to decrease gradually (see Figure 9).

Keywords Burst Terms
The flourishing of a research frontier inevitably leads to an increase in the number of its keywords in a short period of time. Burst terms refer to words that appear or used more frequently in a short period of time, and the frontiers and trends of research fields can be judged based on the word frequency changes of burst words. Figure 10 shows are among the topics that were highlighted with high intensity. "Learning prediction", "empirical studies", and "literature review" were the topics of more interest in the field of learning from 2019 to 2021, and "empirical studies", and "literature review" were also the main research methods in the field of learning analytics in China this year (see Figure 10). Figure 11 shows the learning analytics burst terms mapping in WOS from 2011 to 2021. It can be seen that "learning process", "learning environment", "big data", "educational data mining", and "learning environment" are the five hot topics in learning analytics research. Among them, "learning process" was a hot topic from 2019 to 2021, "learning environment" was a hot topic from 2017 to 2021, and "big data" was a hot topic from 2017 to 2021. "Big data" was a hot topic from 2017 to 2019, "educational data mining" was a hot topic from 2019 to 2021, and "instructional design" was a hot topic from 2019 to 2021 (see Figure 11).
The research frontier of learning analytics in China is mainly focused on learning prediction, and the research approach is mostly based on empirical studies and literature reviews and the international publications are mainly focused on educational data mining, big data, and the design of learning environments and tools，more focus on the learner's learning process.

Discussion and Conclusion
This study aims to explore the development trends and frontiers of learning analytics from 2011 to 2021, using CiteSpace as a research tool to visualize and analyze data from CNKI and WOS. This paper visualizes the similarities and differences of learning analytics research on Chinese publications and international publications in a graphical way, and also provides a reference for researchers in the field of learning analytics.

Similarities and Differences between Authors and Institutions
From the analysis of the volume of author publications and publication institu- In the future, Chinese scholars and institutions can form more collaborations with scholars and institutions from other countries in the field of learning analytics.

Similarities and Differences in Research Hotspots and Trends
From the network of keywords map and the timeline of keywords map, it can be found that the research hotspots of learning analytics in China focus on "big data", "data mining" and "learning behavior", while the hotspots of international learning analytics focus on the "learning process", "learning outcomes", and "learning management systems". From the research results, it can be seen that Chinese research on learning analytics is mostly focused on data analysis of the learning process, while the main object of international learning analytics research is learning-related aspects, from data back to the learning behavior itself.
This also provides a new research idea for the next stage of localization of learning analytics development in China.

Similarities and Differences in Research Frontiers
From the keyword Burst terms tables, we can conclude that the research frontiers of learning analytics in China are mainly focused on research on "learning prediction" and "smart education", and the research area is dominated by online learning. In addition, it can be inferred that the research on the construction of models based on learning analytics in smart education and learning analytics in online learning is likely to be the focus of the future study. The frontiers of international learning analytics mainly focus on "learning process", "learning environment" and "education data mining", and education technology-related majors are more concerned with the development of learning analytics. Combining learning analytics with various factors in the learning process is also of greater significant relevance.

Limitations and Future Research
This study still has some limitations that the study data only encompasses the CNKI database and the WOS database, with a small sample base of data, which may not cover all the studies in the fields. Future studies may consider obtaining more comprehensive data from more databases. Additionally, CiteSpace can be used in combination with other literature analysis softwares in the future to provide a more comprehensive and in-depth study of relevant topics.