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ChatGPT/AIGC and Educational Innovation: Opportunities, Challenges, and the Future Yongxin Zhu, Fan Yang Journal of East China Normal University(Educational Sciences)    2023, 41 (7): 1-14.   DOI: 10.16382/j.cnki.1000-5560.2023.07.001 Abstract （2678）   HTML （449）    PDF （805KB）（3859）       Knowledge map    A text-based artificial intelligence application called ChatGPT has sparked attention from all walks of life upon its launch, and its demonstrated value of educational innovation has been hotly debated in particular. Thanks to its advanced algorithms, powerful computing capabilities and massive data base, ChatGPT can learn on its own when interacting with users, thus intelligently providing Q&A, translation, writing and other services. In addition, the newly-released upgraded version of ChatGPT—GPT-4 can also better solve real-time problems, reason logically and write creatively. In the field of education, ChatGPT can be used as a teaching tool, answer professional academic questions, build autonomous learning platforms, save human resources costs, and reconstruct school education structures, providing many development opportunities for educational innovation. However, it also brings some risks and challenges, impacting the role of teachers, the goals of talent cultivation, the traditional school order, and exposes typical issues such as technological dependence, academic misconduct, and intelligent discrimination. Currently, technological renovation is pressing educational innovation step by step, which requires us to approach new technologies positively and cautiously, achieving self-reliance and strength in intelligent technology by strengthening organized scientific research, building a national digital security barrier through revising relevant laws, enhancing application service transparency by clarifying digital education standards, and transforming talent cultivation concepts to form flexible thinking in education evaluation. Based on all this, we must make every effort to create a new form of human civilization in the process of moving towards human-machine co-teaching. Reference | Related Articles | Metrics

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Exploring the Impact of ChatGPT/AIGC on Education and Strategies for Response Zongkai Yang, Jun Wang, Di Wu, Xu Chen Journal of East China Normal University(Educational Sciences)    2023, 41 (7): 26-35.   DOI: 10.16382/j.cnki.1000-5560.2023.07.003 Abstract （3530）   HTML （465）    PDF （699KB）（3525）       Knowledge map    ChatGPT, as a representative of AIGC, has a significant impact on education. It empowers teaching by shifting the teaching model from “teacher-student” to “teacher-computer-student”, promoting the shift of teaching content from manual production to intelligent production, and catalyzing the assessment model of “knowledge + literacy”. ChatGPT also empowers learning by promoting the ubiquitization of learning space, meeting the personalized needs for full coverage of the learning process, and forming a human-computer collaborative learning mode. Additionally, ChatGPT empowers education by promoting higher-order ability cultivation and comprehensive literacy cultivation, and innovating the education model of discipline integration. To cope with the impact caused by ChatGPT, we must pay full attention, think calmly, and respond positively. This requires us to accelerate the development of high-level competing products with localized characteristics and to thoroughly study the laws of intelligent pedagogy with human-computer synergy. It also involves handling the important relationships between change and invariance, equity and efficiency in education, and the specialization and universality of technology. Besides, it's important to correctly grasp the direction and development of integration of AI technology and education, and lead the transformation of education system structure and operation mechanism. Reference | Related Articles | Metrics

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Principles, Procedures and Programs of Latent Class Models Zhonglin Wen, Jinyan Xie, Huihui Wang Journal of East China Normal University(Educational Sciences)    2023, 41 (1): 1-15.   DOI: 10.16382/j.cnki.1000-5560.2023.01.001 Abstract （1987）   HTML （216）    PDF （837KB）（2272）       Knowledge map    The models used in Latent Class Analysis and Latent Profile Analysis are collectively referred to as latent class models, a kind of statistical methods of classifying individuals according to their different response patterns in observation indicators, so as to identify population heterogeneity. It has attracted increasing attention from applied researchers in the fields of pedagogy, psychology, and other social science disciplines. However, it is not easy for most education researchers to understand the existing Chinese literature on the statistical principles and analytical procedures of such models. This paper systematically introduces the basic knowledge, statistical principles, analytical procedures and Mplus programs of latent class models, and clarifies various methods and selection strategies involved in the subsequent analysis of these models. It would help applied researchers enhance their understanding of the principles and methods of the latent class models, and promote the application of these models to educational research. Table and Figures | Reference | Related Articles | Metrics

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Artificial Intelligence Curriculum Guidelines for Primary and Secondary Schools Jiang Bo Penner:, Dai Juan Core Members:, Zhou Aimin , Dong Xiaoyong , Liu Xiaoyu , Hong Daocheng Participants:, Jiang Fei , Zheng Longwei , Zhao Jiabao , Zhang Hengyuan , Liu Yalin , Yuan Zhenguo Consultant: Journal of East China Normal University(Educational Sciences)    2023, 41 (3): 121-134.   DOI: 10.16382/j.cnki.1000-5560.2023.03.013 Abstract （2007）   HTML （131）    PDF （728KB）（1948）       Knowledge map    Artificial intelligence (AI) education in primary and secondary schools has just started in China. Lack of unified curriculum standards, we still face many difficulties in the curriculum nature and objectives, textbooks development, and academic evaluation. To address this issue, East China Normal University and Shanghai Artificial Intelligence Laboratory jointly developed the Artificial Intelligence Curriculum Guidelines for Primary and Secondary Schools. The proposed guidelines has six parts including course nature and basic concept, core competency and curriculum objectives, course structure, course content and requirements, academic evaluation standards and implementation suggestions. We aim to construct a scientific and open curriculum guidelines for AI education in primary and secondary schools and simultaneously provide a reference for the construction of an AI education system in China. Table and Figures | Reference | Related Articles | Metrics

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ChatGPT/AIGC and the Future Vocational Education Guoqing Xu, Jinfang Cai, Beijia Jiang, Zheng Li, Hui Yang, Jie Zheng Journal of East China Normal University(Educational Sciences)    2023, 41 (7): 64-77.   DOI: 10.16382/j.cnki.1000-5560.2023.07.007 Abstract （1108）   HTML （70）    PDF （668KB）（1507）       Knowledge map    Generative artificial intelligence such as ChatGPT has attracted strong attention since its inception. This paper reveals the future picture of vocational education under the influence of ChatGPT from four main aspects: vocation, vocational ability, personnel training in vocational education and scientific research in vocational colleges, which affect the development of vocational education and reflect the important functions of vocational education. Firstly, in terms of vocation, this paper puts forward corresponding coping strategies after clarifying the technical principles, mechanisms and paths of ChatGPT’s impact on vocation. Secondly, in terms of vocational ability, after combing the ability of ChatGPT, this paper analyzes the influence of ChatGPT on vocational ability from the explanation of the characteristics and structure of vocational ability, and suggests how to deal with the above influence. Thirdly, in terms of personnel training in vocational education, this paper first discusses the personnel training of vocational education school system, combs the application of artificial intelligence technology in the field of higher vocational education personnel training, and then analyzes the challenge of ChatGPT to higher vocational education personnel training and puts forward countermeasures. Then this paper discusses the personnel training of vocational skills training system, expounds the influence of iterative update of ChatGPT on future skills training and the influence of ChatGPT on the future skills training mode of vocational education. Finally, in terms of scientific research in vocational colleges, after clarifying the basic connotation of scientific research in vocational colleges, this paper depicts the iterative process of scientific research in vocational colleges in the change of skill formation, expounds the influence of ‘de-skill’ technology development represented by ChatGPT on scientific research in vocational colleges, and answers how scientific research in vocational colleges should deal with ‘de-skill’ technology development represented by ChatGPT. Reference | Related Articles | Metrics

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The Transformation of Teachers’ Work in the Era of ChatGPT/AIGC: Opportunities, Challenges, and Responses Huan Song, Min Lin Journal of East China Normal University(Educational Sciences)    2023, 41 (7): 78-90.   DOI: 10.16382/j.cnki.1000-5560.2023.07.008 Abstract （981）   HTML （91）    PDF （1388KB）（1419）       Knowledge map    As the latest breakthrough in artificial intelligence, ChatGPT has attracted widespread attention and discussion in the field of education since its release, requiring an analysis and exploration of its impact on teachers’ work from both theoretical and practical perspectives. Drawing upon the theoretical ideas of Biesta’s “weakness of education” and Dewey’s “philosophy of communicative action”, this paper deeply considers the essence of the integration between ChatGPT and education. According to different ways of thinking about “strong education” and “weak education”, teachers and schools have different degrees of replaceability. Teachers should pay attention to the value orientation of education, grasp the essence of education, seize the opportunities brought by ChatGPT in personalized learning, teacher workload, and teacher self-growth, and actively respond to the challenges brought by ChatGPT in learning objectives, teaching processes and design, and evaluation methods. Starting from the fundamental task of education and “competency-based suyang” education, and focusing on the digital information literacy of educators, this paper proposes new requirements for the structure of teachers’ competency in the new era: the ability to lide shuren (cultivate moral character and educate students), professional ethics in the digital age, the ability to integrate artificial intelligence into teaching, and the ability to update the curriculum, learning, teaching, and evaluation. Table and Figures | Reference | Related Articles | Metrics

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Research on Learning Affective Computing in Online Education: From the Perspective of Multi-source Data Fusion Xuesong Zhai, Jiaqi Xu, Yonggu Wang Journal of East China Normal University(Educational Sciences)    2022, 40 (9): 32-44.   DOI: 10.16382/j.cnki.1000-5560.2022.09.004 Abstract （893）   HTML （127）    PDF （2774KB）（1412）       Knowledge map    Learning affection is an essential factor affecting learning performance, perception, and higher-order thinking ability. Existing research on learning affective computing is mainly based on a small sample analysis of heavyweight physiological feedback technology. There is a lack of learning affective computing research in online courses. In the online course environment, the data sources for learning affective computing are relatively limited, mainly based on a single facial expression data. On the other hand, learners are often under insufficient supervision in online learning scenarios. The body posture is more random, so it is very likely to affect the extraction of facial features. However, this study believes that the pose of online learners also has affective characteristics and is also a key source of affective information. Therefore, we try to fuse the learner’s posture data into the facial expression data, build a multi-source data fusion deep learning affective calculation model, and make up for the facial recognition defects caused by the learner’s posture change. Also, we perform collaborative analysis multi-source affective data to realize data cross verification and mutual compensation. The research concludes that it is an effective method for online learning affective calculation to build a dataset containing 7,878 facial expressions and posture images of online learners constructed through training, and use the convolutional neural networks and decision fusion methods to integrate learner posture data into facial expression data.The accuracy of affections recognition increases 3%, compared with a single facial expression recognition. In theory, this research provides a model basis for the effectiveness of multi-source data fusion in learners’ affective computing. In practice, it provides an effective technical path to learning affective computing in an online education environment. Table and Figures | Reference | Related Articles | Metrics

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How Natural Language Processing Technology Empowers the AIED: The Perspective of AI Scientist Bo Zhang, Ruihai Dong Journal of East China Normal University(Educational Sciences)    2022, 40 (9): 19-31.   DOI: 10.16382/j.cnki.1000-5560.2022.09.003 Abstract （870）   HTML （302）    PDF （1287KB）（1411）       Knowledge map    Natural language processing (NLP) is one of the most important research branches of artificial intelligence (AI). With the boosting of computer performance and the construction of large-scale corpora in the last decade, NLP technology has made great progress and has been widely applied in various areas, especially in the field of education. Specifically, in this paper, we investigate the present research and the trends of NLP technology, and how NLP promotes the development of artificial intelligence in education (AIED) through studying and analyzing publications, reports, and speeches, etc. from eminent domestic and international AI specialists. We are aiming to explore the direction and trend of AIED in the future. Table and Figures | Reference | Related Articles | Metrics

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ChatGPT in Education: A Diagnostic Study of Teaching Ability Liang He, Zhenyu Ying, Yingying Wang, Wenqi Sun Journal of East China Normal University(Educational Sciences)    2023, 41 (7): 162-176.   DOI: 10.16382/j.cnki.1000-5560.2023.07.015 Abstract （831）   HTML （70）    PDF （964KB）（1333）       Knowledge map    The development of artificial intelligence technology is triggering profound changes in the field of education. As a new generation of natural language processing tools driven by artificial intelligence technology, ChatGPT has attracted widespread attention and use due to its powerful language understanding and text generation capabilities. However, due to the special nature of education, it is especially important to pay attention to whether it will have a negative impact on students while accepting it. In order to investigate the current teaching ability of ChatGPT, Shanghai Institute of AI Education, East China Normal University organized a diagnostic study of ChatGPT in teaching ability. Through 118 questions and 800 rounds of questioning, six teachers and nine students found that ChatGPT does not yet have the ability to tutor students independently, but it can be used as a good assistant for teachers to improve their daily work efficiency. Teachers should approach, learn, and use general AI tools as soon as possible, understand their potential risks, and teach students how to properly face and use general artificial intelligence tools. Table and Figures | Reference | Related Articles | Metrics

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The Crisis of Educational Psychology: Its Challenge and Positioning Yun Dai David Journal of East China Normal University(Educational Sciences)    2022, 40 (11): 4-24.   DOI: 10.16382/j.cnki.1000-5560.2022.11.002 Abstract （607）   HTML （117）    PDF （915KB）（1243）       Knowledge map    Educational psychology as a branch of psychology has a history of more than one hundred years. However, it is still very young as an independent discipline. Internally, it lacks central concerns of its own, a coherent conceptual system, and a distinct methodology. Externally, it faces new challenges brought up by the increasing division of disciplines. In the context of the 21st century, it is increasingly difficult to view educational psychology as a sub-discipline of psychology and ignore its nature as “design science” and “human science”. Repositioning educational psychology entails distinguishing itself from other psychological sciences by establishing distinct central concerns, especially by re-examining its epistemology and methodology. The traditional logic of mechanical reductionism has to be replaced by an organismic logic of emergence that is based on the plasticity, openness, choice, and growth of human beings. At the same time, educational psychology has to draw inspiration from “design science” in its exploration of possibilities and optimality of learning and growth, while psychological and behavioral evidence provides the viability and constraints for such practices. Table and Figures | Reference | Related Articles | Metrics

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ChatGPT/AIGC and the Value and Mission of Higher Education Yuan Xun Journal of East China Normal University(Educational Sciences)    2023, 41 (7): 56-63.   DOI: 10.16382/j.cnki.1000-5560.2023.07.006 Abstract （882）   HTML （75）    PDF （640KB）（1235）       Knowledge map    Even though large multimodal models have broad application prospects at all stages of education, especially higher education, we should first focus on how to more effectively train students’ ability to cope with AI, or develop their wisdom to cooperate with AI in the process of applying this model. Today, with the rapid development of artificial intelligence, what artificial intelligence cannot achieve in education, especially in the field of higher education, is exactly the real value of human education, especially higher education, including the development of human unique rationality, collectivity and richness of human intelligence, adaptability of human culture, human moral and ethical choices, and human creativity. Higher education in the era of artificial intelligence must adhere to the intrinsic value of higher education, focus on the development of students’ morality, ethics, spirit and creativity, and provide necessary guidelines for rational application of artificial intelligence, so as to ensure that the young generation can continuously develop human collective intelligence under the premise of correct use of artificial intelligence, so as to create a future of common happiness for mankind. Reference | Related Articles | Metrics

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Investigation into the Transformation of Knowledge-Centered Pedagogy with ChatGPT/Generative AI Jingyuan Chen, Liya Hu, Fei Wu Journal of East China Normal University(Educational Sciences)    2023, 41 (7): 177-186.   DOI: 10.16382/j.cnki.1000-5560.2023.07.016 Abstract （925）   HTML （86）    PDF （2035KB）（1214）       Knowledge map    This paper explores the transformative role of ChatGPT in the teaching mode centered on knowledge concepts. As a language generation model, ChatGPT is capable of in-depth language comprehension and innovative combinations by mining the symbiotic relationships between words through massive language data learning. However, in the field of education, ChatGPT faces limitations such as over-reliance on training data, weak logical reasoning ability, and limited ability to handle new scenarios. To overcome these limitations and enhance the accuracy and relevance of ChatGPT’s generated content, this paper proposes an organic combination of ChatGPT with the organization of teaching resources centered on knowledge concepts, and improve ChatGPT by creating structure diagrams of knowledge concepts. Additionally, several specific and feasible ways to assist teachers and students using ChatGPT are also proposed. Finally, this paper discusses how to combine the prompt research paradigm with the teaching mode centered on knowledge concepts to help ChatGPT establish a “knowledge system”. This will enable ChatGPT to become a language generation model driven by both data and knowledge, providing more intelligent and personalized services in the education field, and promoting its development and transformation. Table and Figures | Reference | Related Articles | Metrics

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ChatGPT/AIGC Reshape Education: Underlying Logic and Possible Paths Zhi Zhang Journal of East China Normal University(Educational Sciences)    2023, 41 (7): 131-142.   DOI: 10.16382/j.cnki.1000-5560.2023.07.012 Abstract （887）   HTML （78）    PDF （1879KB）（1184）       Knowledge map    As a large model based on general artificial intelligence, ChatGPT can provide generative information acquisition services, and its functions far exceed any previous retrieval and search tools. Its appearance will also have a profound impact on education. This paper analyzes the influence of ChatGPT on education through literature research and other methods, analyzes and demonstrates its underlying logic of reshaping education from the perspective of brain science, knowledge view, and educational metacognition, and proposes its possible path to reshape education. The article believes that the impact of ChatGPT on education will mainly change education profoundly in the fields of the effectiveness of traditional educational tools, knowledge value theory, resource form, learning model, evaluation methods, and human-machine collaboration IQ. Its change in education conforms to the essence of brain science, echoes with the concept of knowledge, and also conforms to educational values. The birth of ChatGPT gave birth to a new form of education. In the future, it is necessary to accelerate the construction of human-computer collaborative IQ, educational evaluation systems, and computing-based teaching strategy models, and accelerate the revolutionary reshaping of human education. Table and Figures | Reference | Related Articles | Metrics

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A Conceptual Framework of Higher-order Thinking Shufeng Ma, Xiangdong Yang Journal of East China Normal University(Educational Sciences)    2022, 40 (11): 58-68.   DOI: 10.16382/j.cnki.1000-5560.2022.11.005 Abstract （2181）   HTML （137）    PDF （755KB）（1180）       Knowledge map    Higher-order thinking is a key competence for individuals to adapt to external environments and cope with future challenges. The theory of constructivism argues that people do not passively receive information from the environment, but actively construct knowledge to update their mental models. In authentic learning contexts, higher-order thinking manifests as the ability to identify the connection between prior knowledge and external information, transfer background knowledge to a new situation, and solve complex problems that do not have definite answers. Higher-order thinking is not a single thought process, but a complex cognitive process in which multiple mental operations coordinately work together. The conceptual framework of higher-order thinking incorporates the analysis of problem situations, the identification and formation of the relationship between old and new knowledge, the synthesis of information from different dimensions, the creation of new knowledge, and the monitoring, management and adjustment of the thinking process. The conceptual framework explains how the five cognitive components influence each other and synergistically regulate the process of cognitive development. The framework provides a new theoretical perspective for interpreting higher-order thinking, and lays a theoretical foundation for in-depth research on the developmental mechanism of higher-order thinking. Table and Figures | Reference | Related Articles | Metrics

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The Difficulties and Corresponding Strategies of Educational Digital Transformation Junjie Shang, Xiuhan Li Journal of East China Normal University(Educational Sciences)    2023, 41 (3): 72-81.   DOI: 10.16382/j.cnki.1000-5560.2023.03.008 Abstract （2283）   HTML （108）    PDF （1016KB）（1170）       Knowledge map    The digital transformation in education is an inevitable result of the developing affordance of digital technology for education. This paper discusses the barriers, difficulties, and coping strategies in the educational digital transformation. It first describes three stages of future panoramic transformation of education: infrastructure upgrading, learning mode reforming, and educational process reengineering. The digital transformation of education, as a technology-involved innovative social revolution, is not bound to be a smooth journey. The interaction between technologies and education subjects is affected by multiple factors, thus leading to various corresponding difficulties and obstacles. The surface difficulties mainly come from the barriers of technological availability in building the new education infrastructure, referring to limited investments (e.g., human & financial) and technical bottlenecks. The deep difficulties mean educational subjects’ (e.g., teachers, students, & parents) action obstacles. The core difficulty is how to improve the consensus on digital educational innovation and how to deal with the relationship between humans and technology. To solve the core difficulty of educational digital transformation, it is necessary to carry out structural reform in education and strengthen the basic research of learning science to have a deep understanding of the process and mechanism of education with digital transformation. Finally, this paper puts forward several practical strategies to solve these difficulties from three levels: foundation-building strategies, empowering strategies, and exploring strategies. Table and Figures | Reference | Related Articles | Metrics

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The Influence of ChatGPT/AIGC on Education: New Frontiers of Great Power Games Nanping Yu, Yiran Zhang Journal of East China Normal University(Educational Sciences)    2023, 41 (7): 15-25.   DOI: 10.16382/j.cnki.1000-5560.2023.07.002 Abstract （1211）   HTML （308）    PDF （1057KB）（1157）       Knowledge map    The emergence and development of the new generation of artificial intelligence (AI), represented by the ChatGPT, marks the significant transformation from the weak AI to the strong AI, so as to bring great opportunities and challenges to the education system. The new generation of AI’s technological involvement in education elevates the relationship between teachers and students to a brand new third dimension. It presents the following characteristics: a)the network effects of the relationship between teachers and students, b) the dynamic creation of teaching activities, c) th implicit transmission of knowledge and information. From the perspective of application scenarios, the ChatGPT model will focus on three fields: “teachers”, “students”, and “disciplines”, and play an active role in team building, student training, , and discipline building. The application of ChatGPT to the education system not only cast game-changing influence on the current education norms, but also enlarges the gaps in regional developments, controls the industrial transformation process, reshapes the labor structure and complicates the national information security. Th above-mentioned influences not only go beyond the scope of self-definition of education, but also spread to every field of today’s society, and becomes new Frontiers of Great Power Games, directing the shaping of international power structure. In this process, education will be re-understood and redefined. Table and Figures | Reference | Related Articles | Metrics

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Artificial Intelligence Promotes the Development of Future Education: Essential Connotation and Proper Direction Xiaoqing Gu, Shijin Li Journal of East China Normal University(Educational Sciences)    2022, 40 (9): 1-9.   DOI: 10.16382/j.cnki.1000-5560.2022.09.001 Abstract （1006）   HTML （347）    PDF （724KB）（1127）       Knowledge map    The development of artificial intelligence technology brings both opportunities and challenges to the education system. Based on the major social science project “Research on Artificial Intelligence Promoting the Development of Future Education”, the study found out the key focus points of artificial intelligence to promote the development of future education: artificial intelligence highlights the challenges of innovative talent development; artificial intelligence supports the personalized realization of large-scale education; artificial intelligence reshapes the concept of knowledge and teaching innovation; artificial intelligence empowers future teacher development research; and artificial intelligence promotes the systematic update of the education ecosystem. At the same time, technology empowerment education, technology innovation education, and technology reshaping education are the advanced forms of the “trilogy” of artificial intelligence to promote the development of future education. To explore the core mechanism of artificial intelligence to promote future education development, it is necessary to reveal the interaction law of “technology-education-society” from the perspective of complex systems, explore the essential impact of artificial intelligence on educational development from the perspective of learning science, and predict the development trend of future education from the perspective of “history-culture”. On this basis, it's important to grasp the development strategy of innovative talents driven by the intelligent era, focus on the key elements of education innovation driven by artificial intelligence, reshape the blueprint of future education ecology boosted by artificial intelligence, so as to create a “high-quality and warm” artificial intelligence education new ecology in China. Table and Figures | Reference | Related Articles | Metrics

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The Essence of Education Digital Transformation: From Technology Integration to Human-machine Fusion Lianyu Cai, Mingfei Jin, Yueliang Zhou Journal of East China Normal University(Educational Sciences)    2023, 41 (3): 36-44.   DOI: 10.16382/j.cnki.1000-5560.2023.03.004 Abstract （894）   HTML （47）    PDF （766KB）（1120）       Knowledge map    Exploring the qualitative prescriptive nature of education digital transformation is the cognitive basis for effectively promoting transformation practices. Information technology enhances the efficiency of information sharing, but objectively brings about the separation of technology from educational subjects (teachers, students, and administrators) and fails to achieve the convergence and fusion of machine intelligence and human intelligence. As a theoretical paradigm, “education technology integration” believes that the application of information technology in education is a process of improving the efficiency of industrialized “educational production” through the “integration of things and things” between technology and curriculum, innovating the presentation of teaching content, focusing on knowledge push, and facilitating students’ knowledge acquisition. The “technology integration” paradigm has limitations in the four dimensions of ontology, methodology, epistemology and axiology, and it is difficult to meet the value creation needs of education digital transformation. The essence of education digital transformation points to “human-machine fusion”. This new paradigm believes that the application of information technology in education is a process of “human-machine fusion” between educational subjects and machine, using human-machine collaboration to bring together machine intelligence and human intelligence to promote students’ mental growth, thus realizing “teaching students in accordance with their aptitudes on a large scale”. This paradigm follows the modern educational philosophy and is the theoretical basis for education digital transformation. Pushing education digital transformation needs to be based on the paradigm of “human-machine fusion”, and: (a) realize the digital-intelligence drive of the education system by the overall process of reengineering; (b) realize the human-machine fusion of education through new capacity building; (c) realize value system reconstruction by system optimization & innovating; (d) realize the educational function improvement of technology by organizing special researches and developments. Table and Figures | Reference | Related Articles | Metrics

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A Maturity Model for Digital Transformation in Education Yonghe Wu, Qiuxuan Xu, Zhuzhu Wang Journal of East China Normal University(Educational Sciences)    2023, 41 (3): 25-35.   DOI: 10.16382/j.cnki.1000-5560.2023.03.003 Abstract （968）   HTML （62）    PDF （854KB）（1098）       Knowledge map    The digital transformation in education is the driving force and innovation factor of the high-quality development of education. The evaluation of the ability level of the digital transformation in education has become one of the key points of the current education reform and practice. The maturity model provides a new method to solve the difficulty of evaluating the ability level of education digital transformation and helps to lead the implementation of the transformation practice. Based on the theory and basic structure of maturity model, this study analyzes the existing digital maturity model from the perspective of international comparison, and refines the key components of education digital transformation through policy analysis. By referring to the maturity levels and behavior characteristics of Capability Maturity Model and combining with the development characteristics of education digital trans- formation, an evaluation framework of education digital transformation maturity was constructed, which included five key process domains, 18 sub-key domains and five maturity levels. Based on the idea of “macro to medium to micro” to systematically promote digital transformation, this study discusses the key application scenarios of the ma-turity evaluation framework of education digital transformation from national, regional and school levels. A maturity model for digital transformation in education provides feasible evaluation paths for the implementation of education digital strategic actions and high-quality development of education. Table and Figures | Reference | Related Articles | Metrics

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Exploration of the Path of Digital Education Transformation and Practice in Shanghai Haiwei Li, Gong Wang, Meichen Lu Journal of East China Normal University(Educational Sciences)    2023, 41 (3): 110-120.   DOI: 10.16382/j.cnki.1000-5560.2023.03.012 Abstract （604）   HTML （61）    PDF （1190KB）（1077）       Knowledge map    In the background of the “Digital China” strategy, accelerating digital transformation and development and building a digital China have become the torrent of the times. In the field of education, with the integration of a new generation of information technology and education, such as artificial intelligence, Internet of Things and cloud computing, education informatization has reached a critical point of “quantitative change” to “qualitative change”. At present, China’s education digital transformation practice still faces challenges in terms of top-level design and implementation paths. Around Shanghai Educational Digital Transformation policy implementation process, this paper introduced the procedure Shanghai constructed its plan from top to bottom and extract its experiences, including forming new vision through new infrastructure, breaking barriers through digital base, exploring educational new normal through top-down and bottom-up paths, enhancing deep reform through digital competences, assessment and resources construction. These measures can help solve the key problem in educational digital transformation. Table and Figures | Reference | Related Articles | Metrics