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Abstract: Deep learning has received considerable attention as an educational ideal. This study analyzed 381 Chinese core journal papers on deep learning, collected from the CNKI database, using Bicomb 2.0 and Citespace 5.4 software to examine the quantity of literature, research institutions, keywords, and other content. The findings reveal that current research results related to deep learning in China are limited, with weak collaboration among institutions. Research themes mainly focus on the ontology theory of deep learning, teaching models and strategies related to deep learning, the conditions and environments required for implementing deep learning, empirical studies on the practical application of deep learning, evaluation of deep learning, and research on learners. The study of deep learning is gradually shifting from teaching models and learning environments to resource construction, integration with artificial intelligence, and evaluation of teaching effectiveness.
Keywords: Deep Learning; Knowledge Graph; Visualization Analysis; Research Themes; Research Trends
Deep learning (Deep Learning) is also translated as deep learning, first proposed by American scholars Ference Marton and Roger Saljc in their 1976 publication “The Distinction of Learning: Outcomes and Processes.” Chinese scholar Li Jiahou believes that deep learning refers to the ability of learners to critically learn new ideas and facts based on understanding learning, integrating them into existing cognitive structures, making connections among various ideas, and transferring existing knowledge to new contexts to make decisions and solve problems. The New Media Alliance’s “Horizon Report” (2015 K-12 Edition) pointed out that the impact of implementing deep learning strategies on classroom teaching is increasingly profound, which is an important trend driving schools to apply educational technology. Currently, the rapid development of information technology, changes in learning methods, and advancements in artificial intelligence and learning sciences provide strong support for the research and practice of deep learning. In recent years, China’s educational development has aimed at “developing equitable and quality education,” and deep learning is an effective way to improve educational quality. Understanding the research history and current status of deep learning and forecasting its development trends is of significant research importance. Although there have been relevant studies in this area, the research data is from before 2016 and is not specifically focused on domestic deep learning research. The main achievements of domestic deep learning research emerged in 2017 and 2018, so visually displaying recent research situations is conducive to more precise analysis of the current status and dynamics.
This study queried the journal database of CNKI using “Deep Learning” as the keyword, without setting a time limit, and categorized the sources as “Peking University Chinese Core Journals” and “CSSCI” source journals, with the literature classification directory set to “Social Sciences II”. A total of 390 papers were obtained. A secondary manual search was conducted to exclude irrelevant articles such as book reviews, conference summaries, and journal solicitation articles, ultimately yielding 381 papers as the research subjects, with the retrieval date being April 19, 2019.
(2) Research Methods and Process
This study combined Bicomb 2.0 software and Citespace 5.4 software for data analysis. Bicomb is a bibliometric analysis software that conveniently counts information such as authors, institutions, units, and journals. Citespace is a knowledge graph software that can easily study the knowledge graph of keywords.
First, the selected literature information was exported from CNKI, mainly including titles, authors, units, keywords, abstracts, and journals. The information was exported in two formats: Note First and Refworks. The Note First format used the default title, while the Refworks format required changing the file name’s prefix to Download to meet the Citespace software requirements. Then, the two files were imported into Bicomb software and Citespace software for data processing, standardizing keywords using Bicomb software and conducting information slicing and other preprocessing using Citespace software. Finally, according to needs, the results were analyzed using the software.
2. Research Results and Analysis
(1) Temporal and Spatial Distribution Maps and Their Analysis
To explore the output situation of relevant literature, this study statistically analyzed the number of papers published annually, with results shown in Figure 1. It can be seen that research on deep learning in China began in 2005, but until 2015, it had not attracted widespread attention, and the output was limited. Since 2016, the number of published papers has sharply increased, with the output in one year nearly equaling the total number of papers published in previous years. The number of papers published in 2017 and 2018 exceeded 100. As of April 19, 2019, the number of papers published this year has exceeded 50. Overall, deep learning has attracted widespread attention in China for a relatively short time, and the output is limited. From the growth trend, the number of papers published in the past two years has remained around 110, indicating that research on deep learning is gradually gaining attention from researchers, and domestic scholars maintain a relatively stable growth trend in deep learning research.
Using Bicomb software to count the frequency of authors, there are a total of 9 authors with more than 4 published papers. Duan Jinjun and Dong Yuqi each appeared 6 times, Hu Hang and Liu Zheyu each appeared 5 times, Zhang Baohui, Wu Xiujuan, Zhang Ha, Yu Shengquan, and Ren Huhu each appeared 5 times, accounting for a total of 11.02%. If counting the frequency of first authors, there are a total of 12 authors with more than 3 published papers, Liu Zheyu and Hu Hang each appeared 5 times, Duan Jinjun and Ren Huhu each appeared 4 times, Cao Peijie, Ren Ye, Yang Yuqin, Yu Liping, Zhu Kaiqun, Zeng Mingxing, Zhang Qi, and Bai Xiaozhong each appeared 3 times, accounting for a total of 11.05%. From the author map generated by Citespace, the connections among 34 authors only have 9 links, with a density of only 0.016, indicating that the connections among authors are not close. Among them, collaboration among authors is mainly within institutions, with some inter-institutional collaborative research. In fact, more researchers are conducting independent research. Overall, collaboration among authors is relatively scattered, mainly concentrated in internal institutional collaboration, and stable research teams have not formed, with many individual researchers, making it difficult to form a series of impactful results.
Using Bicomb software to export institutional information, based on the frequency of first-level units (see Table 1), there are 11 units with a frequency of more than 6, with a total frequency of 155 times, accounting for nearly 30% of the total frequency, and most of these 11 units are normal universities. The top five institutions are East China Normal University, Northeast Normal University, Beijing Normal University, Central China Normal University, and Shaanxi Normal University. It is worth mentioning that counting by first-level units does not necessarily indicate that there is collaboration within the first-level institutions. In particular, the frequencies of Yangzhou University, Tianjin Normal University, and Tsinghua University include frequencies related to affiliated schools, especially affiliated high schools. This data only indicates the distribution of researchers.
In Citespace software, the threshold is set to TOP50, generating a cooperation map of research institutions in deep learning to explore the cooperation among institutions. It can be seen that if counted by second-level units, the Education Department of Northeast Normal University, the Education College of Shanxi Normal University, the Research Institute of Curriculum and Teaching of East China Normal University, and the School of Journalism and Communication of Yangzhou University have a relatively high number of published papers. There are few connections among the 28 institutions, with only 4 links, and a density of only 0.0106, indicating that there is little cooperation among institutions, and a large-scale research community has not formed. This indirectly reflects the lack of in-depth and systematic research in this field, which is consistent with the results of the cluster analysis later. It is evident that to achieve breakthroughs in research, the current isolated situation needs to change, and the formation of cooperative networks should be accelerated.
Journals are the carriers of articles, and the number of published papers on relevant themes represents the editorial style and preferences of the journals. The statistical results are shown in Table 2. From Table 2, it can be seen that the journals primarily carrying deep teaching themes are mainly electronic education journals, with the top five journals including “Journal of Distance Education”, “Modern Educational Technology”, “China Educational Technology”, and “China Distance Education”, with a total publication volume of 82 papers, accounting for over 20%. Comprehensive educational journals also account for a certain proportion, such as “Teaching and Management”, “Curriculum · Textbooks · Teaching Methods”, “Basic Education Curriculum”, “Management of Primary and Secondary Schools”, “People’s Education”, “Shanghai Educational Research”, and “Educational Theory and Practice”, etc. In addition, there are some subject education journals, such as “Biology Teaching”, “Secondary Political Teaching Reference”, “Ideological and Political Teaching”, “Physics Teacher”, and “Chemistry Teaching”, etc. This to some extent reflects that the focus of deep research is on the application and breakthrough of information technology, mainly concentrated in the field of basic education, with research results also accounting for a certain proportion in specific subjects such as biology, politics, physics, and chemistry.
(2) Content Co-occurrence Maps and Their Analysis
1. Keyword Co-occurrence Network Map
Using Citespace software to generate a keyword co-occurrence network map, clustering names can be automatically generated from keywords, revealing that the research field of deep learning can be divided into six clusters: deep teaching, teaching models, artificial intelligence, empirical research, teaching strategies, and MOOC.
The map includes 67 nodes and 73 links, with a modularity of Modularity Q=0.6968, indicating that the connections within the clusters are relatively tight; the clustering silhouette indicator Mean Silhouette=0.3097, which is less than 0.5, indicates that each cluster does not have sufficient similarity, and the research themes within the clusters are relatively dispersed (see Table 3). This is related to the short time of the rise of deep learning and the limited related research results, as well as the different research angles of various scholars. In addition to studying the mechanisms of deep learning, many scholars have also conducted research on the integration of information technology and the implementation of deep learning in specific subject teaching.
Analyzing the keywords in the clustering themes and combining literature reading, the current research status of deep learning is as follows.
Firstly, research on the ontology theory of deep learning. Deep learning has only a forty-year development history abroad, while in China, it only began to gain attention around 2005, with substantial research only taking place in the last three to four years. Research on the theory of deep learning itself mainly relies on foreign research experiences, with relatively few original theories and results. Scholars generally agree that deep learning emphasizes the learner, is based on a life perspective, highlights subjective thinking, advocates participatory experience, and focuses on the development of learners. Regarding the mechanism of mastery learning, some scholars believe that mastery learning focuses on promoting conceptual transformation, emphasizing knowledge reconstruction, and transitioning from the superficial formal transmission of symbols to the logical deduction of knowledge’s cultural intrinsic creation, deep motivation, personal experience—higher-order thinking and deep understanding—practical innovation are the practical characteristics of deep learning. The transformative perspective can open up new research horizons, and some scholars have studied typical mechanisms of deep learning under the lens of scientific perspective, including growth mechanisms, interaction mechanisms, modeling mechanisms, and expression mechanisms, which are highly innovative. Overall, China is relatively late in the research of deep learning’s own theory, and it can open up the research field of ontology theory by transforming perspectives or applying new research methods based on foreign research experiences.
Secondly, research on the models and strategies of applying deep learning in teaching. Applying deep learning in teaching practice is a concern for researchers. Chinese scholars have conducted relatively rich research in this area. Some scholars have pointed out the drawbacks of traditional learning and the need for teaching reform through empirical research, analyzing the necessity of applying deep learning. More scholars have proposed implementation strategies for deep learning, such as simulating social practice in teaching activities for knowledge discovery, applying the “perception—discussion—action” practical framework for reconstructing deep learning content, managing core relationships such as students’ daily accumulation and comprehensive breakthroughs, presetting and generating classroom teaching, and promoting diverse interaction among students. In terms of teaching design, some scholars have pointed out that implementing deep learning requires directing the teaching design and implementation process towards cultivating students’ higher-order thinking based on core subject content. In terms of teaching modes, some scholars have designed classroom teaching models based on flipped classrooms and split classrooms. The research themes in this field are relatively rich, reflecting scholars’ attention to the application of deep learning. Deep learning, as a learning mechanism, can be integrated into various forms of teaching activities, requiring more frontline educators to participate in research and practice.
Thirdly, research on the conditions and environments required for implementing deep learning. The implementation of deep learning is inseparable from the related conditions and environments, mainly reflected in the development of resources needed for implementing deep learning and the construction and application of information technology environments. In resource-related research, scholars have analyzed the representation, development principles, and application strategies of supporting resources required for deep learning. In terms of information technology environments, some scholars have constructed a teaching interaction framework that promotes students’ deep learning in smart classrooms, while more scholars focus on the application of information systems, analyzing the support of the TensorFlow system (an open-source artificial intelligence system launched by Google) and the ITtools 3.0 platform (an information technology-assisted teaching platform) for deep learning. With the development of information technology, various intelligent learning systems are emerging, providing opportunities for the practice of deep learning. Initially, researchers focused more on the feasibility and strategy analysis of applications. Subsequently, attention should be paid to resource development and application research to support the practice of deep learning. Once certain conditions are met, emphasis should be placed on empirical research validation and optimization of the role of information systems in deep learning.
Fourthly, empirical research on the practical application of deep learning. With the growing appreciation for empirical research in the domestic academic community and researchers’ focus on the effects of deep learning, some scholars have conducted empirical research on deep learning, yielding many valuable conclusions. Theoretically, some scholars have concluded that the reconstruction of learning content and its resources based on cognitive processes has high effectiveness, that the quality of learning and technology exist in an interactive relationship, and that “technology design” rather than “technology itself” promotes learner development. Some scholars have revealed the cognitive processing mechanisms that promote understanding of scientific concepts in mixed-reality learning environments, providing important theoretical support for the practice of deep learning. Practically, some scholars have concluded that reflective deep learning models are feasible and that reflective activities can effectively promote deep learning. Some scholars have conducted teaching practice using specific courses, exploring the main factors affecting deep learning in the flipped classroom teaching model. Both theoretical validation and practical application are equally important; current empirical research mainly relies on quantitative research, and if combined with qualitative research, it could more finely showcase the practical process of deep learning and derive conclusions from subtle aspects.
Fifthly, research on evaluation in deep learning. Domestic scholars mainly derive evaluation methods based on deep learning from mature learning evaluation methods abroad, constructing an evaluation index system based on deep learning. Popular methods utilize relatively mature evaluation systems, such as Bloom’s taxonomy of cognitive objectives, Biggs’ SOLO taxonomy, Simpson’s psychomotor objectives taxonomy, and Krathwohl’s affective objectives taxonomy to construct evaluation systems based on deep learning. Some scholars have also constructed immersive learning evaluation index systems based on deep learning, focusing on learning motivation, attention, and learning adaptation as three-level indicators. In addition to teaching evaluation based on deep learning, some scholars have researched resource evaluation based on deep learning. For example, some scholars have optimized design directed towards deep learning using case studies, establishing and improving evaluation indices and proposing evaluation strategies. Research on evaluation mainly references foreign evaluation models, with relatively few original studies, focusing more on the research of learning processes and outcomes than on the evaluation of learning environments and resources. Researchers should emphasize integrating reference and originality, strengthening research on the evaluation of the learning process, environment, and configured resources of deep learning to safeguard its implementation.
Sixthly, research on learners. Learners are the fundamental starting point and ultimate destination of deep learning; studying learners helps grasp the research direction of deep learning and adjust its final focus. Some scholars believe that reflective learning is an important way for learners to engage in deep learning. Other scholars analyze learner behavior in blended learning environments, finding that deep learners significantly outperform shallow learners in critical understanding and meaning negotiation. Some scholars have also improved the learner model and emotion recognition methods in deep learning based on learning science theories. The research results in this theme are relatively few, mainly due to the following reasons: firstly, the time for extensive research on deep learning domestically is short, and this field has not yet attracted wide attention; secondly, learner behavior is difficult to detect and evaluate, making strict empirical research challenging, which has always been a difficulty in educational research. Research in this field requires researchers to shift their research perspectives and methods to conduct more in-depth studies on learners.
2. Keyword Co-occurrence Temporal Map
Using Citespace software to generate a keyword co-occurrence temporal map, one can observe the time of occurrence of various keywords and the continuity and changes in research themes. For example, research on flipped classrooms began to appear around 2014, while studies related to MOOCs and empirical research on deep learning and teaching strategies began around 2015. Research hotspots in 2017 and 2018 were relatively dense, which aligns with the previously reported publication volume statistics. Research themes mainly focus on deep teaching, core competencies, teaching models, educational data mining, artificial intelligence, and other areas. The keyword co-occurrence temporal map indicates that research on deep learning is gradually transitioning from studying teaching models and learning environments to resource construction, integration with artificial intelligence, and evaluation of teaching effectiveness.
Analysis of the knowledge graph reveals that research on deep learning in China is still at a preliminary stage, with a short research time and relatively dispersed themes. The research field is relatively concentrated, and the research scope is narrow, but it also shows that researchers continue to pay attention to deep learning, and whenever new teaching models or information technologies emerge, researchers attempt to apply them in the implementation of deep teaching. The evolution of deep learning research from integration with flipped classrooms to MOOCs and then to artificial intelligence illustrates this point. This indicates that enhancing learning effectiveness is an eternal topic. Additionally, keywords such as teaching strategies, effective teaching, educational big data, and educational data mining suggest that in 2018, a considerable portion of research focused on the practice, application, and evaluation of deep learning, indicating that scholars are more concerned about the practice of deep learning than theoretical research.
(1) Focus on the Development and Application of New Technologies
The evolution of deep learning research in China shows that deep learning research is closely linked to the development of new technologies. The advancement of information technology has facilitated the development of flipped classrooms and MOOCs, which deep learning immediately integrates with. With the development of big data, deep learning leverages information technology to collect educational data for evaluation and analyze learners’ learning behaviors. As artificial intelligence develops, some scholars have begun to research the possibilities and strategies for building deep learning environments using artificial intelligence. Therefore, the development of deep learning is progressive, especially focusing on the potential for integration with new technologies, using new technologies to create conditions for the implementation of deep learning. Additionally, attention should be paid to the development of learning sciences, particularly brain sciences, which are crucial for the research of deep learning ontology.
(2) Return to the Research of Learning Ontology
Deep learning is ultimately a learning strategy; without research on learning theory, it will inevitably become a source-less water and a tree without roots. With the development of psychology, especially brain sciences, the system of learning theory is gradually improving and advancing. Moreover, with the advancement of information technology, research on learning theories supported by information technology will also play a positive role. Therefore, only by focusing on the development of learning ontology can theoretical support for deep learning’s development be provided. This requires more researchers to return to the study of learning theory itself, improve existing deep learning theories, and create new learning theories. Particularly, the focus should be on learners and knowledge itself as research objects, emphasizing students’ learning rather than teachers’ teaching, using humanistic concepts to improve and construct deep learning theories.
(3) Enrich Practical Research on Deep Learning
Currently, practical research on deep learning mainly focuses on practices under models such as flipped classrooms, concentrated in subjects like physics, biology, and chemistry, with relatively few practical studies in other teaching models and subjects. There are many studies abroad based on project learning and problem-based learning that Chinese scholars can reference to enrich deep learning research in various teaching models. At the same time, current deep learning research is concentrated in a few subjects with rich teaching experiments. Its core is also to reinforce students’ mastery of knowledge through experiments and trial and error to achieve deep learning effects. In other subjects, although experimental teaching is less common, deep learning concepts can guide improvements in teaching models, integrating deep teaching ideas, and conducting effectiveness evaluations, which has positive significance for expanding the application scope of deep learning.
(4) Emphasize Research on Related Educational Resource Construction
The implementation of deep learning cannot be separated from the support of relevant educational resources. Currently, deep learning is mainly in the theoretical research stage, and extensive practice requires the construction of related educational resources. Researchers should combine specific subjects to make top-level designs, relying on subjects to integrate deep learning concepts in constructing related teaching materials, and provide corresponding teaching resources for teaching. Only in this way can frontline teachers be guided and referenced in deep learning teaching practices. In the resource development process, the principle of combining theory with practice should be adhered to, absorbing frontline teachers with rich teaching experience into the development team, and adjusting and optimizing resources based on feedback from frontline teachers.
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Author Affiliations: Liu Hui, Liaoning Normal University, School of Education, Dalian, Liaoning, Postcode 116029; Kang Wenyan, Lvliang College, Department of Mathematics, Lvliang, Shanxi, Postcode 033000.
* This article is a phased research result of the Shanxi Provincial Educational Science “13th Five-Year Plan” project “Research on Teaching Design to Improve Students’ Core Competencies in Mathematics” (Project Number: GH-18106).
Author Profiles: 1. Liu Hui (1982-), male, from Liulin, Shanxi, doctoral student at Liaoning Normal University, School of Education, mainly engaged in research on curriculum and teaching theory; 2. Kang Wenyan (1986-), female, from Liulin, Shanxi, lecturer and master in the Department of Mathematics, Lvliang College, mainly engaged in research on mathematics education.
This article is sourced from “Educational Theory and Practice” 2020, Issue 1, Volume 40.
