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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
    Abstract2549)   HTML445)    PDF (805KB)(3744)      

    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.

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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
    Abstract3252)   HTML456)    PDF (699KB)(3340)      

    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.

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    Determining Sample Size in Qualitative Research: Saturation, its Conceptualization, Operationalization and Relevant Debates
    Ailei Xie, Jiayi Chen
    Journal of East China Normal University(Educational Sciences)    2021, 39 (12): 15-27.   DOI: 10.16382/j.cnki.1000-5560.2021.12.002
    Abstract2063)   HTML468)    PDF (899KB)(2387)      

    Saturation has become an important criterion to judge the quality of qualitative research and explain the rationality of sample size. This paper systematically and critically reviews the literature and introduces three types of saturation: theoretical saturation, thematical saturation and data saturation and points out that researchers should consider their own overall research design to decide which model to adopt. The paper also introduces three ways to record the process of saturation. Among them, the structural coding book is more suitable for recording deductive thematical saturation and data saturation. There are two types of saturation tables which are more suitable for recording a prior thematical saturation. The conceptual depth scale is particularly suitable for recording theoretical saturation. This paper points out that saturation is a process rather than an event. Quality rather than quantity is the key to understand saturation. Saturation itself should be a concrete methodology practice. Researchers should pay attention to the concept of saturation and make a clear statement of the process of achieving saturation, which can help to improve the standardization, transparency, quality and recognition of qualitative research.

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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
    Abstract1849)   HTML210)    PDF (837KB)(2133)      

    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.

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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
    Abstract1912)   HTML125)    PDF (728KB)(1896)      

    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.

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    Starting Line Competition: An Analysis of the First Time for Primary and Secondary School Students to Participate in Extracurricular Tutoring—— An Empirical Study Supporting the Implementation of the “Double Reduction” Policy
    Haiping Xue, Huanhuan Shi
    Journal of East China Normal University(Educational Sciences)    2022, 40 (2): 71-89.   DOI: 10.16382/j.cnki.1000-5560.2022.02.006
    Abstract1824)   HTML209)    PDF (1060KB)(1852)      

    The effective implementation of the “Double Reducation” policy needs to accurately identify the deep-seated reasons why students choose to participate in extracurricular tutoring. Studying the time when primary and secondary school students participate in extracurricular tutoring for the first time can provide an empirical basis for relieving the extracurricular tutoring needs of parents and students. Based on the data of the 2017 China Institute for Educational Finance Research-Household Survey (CIEFR-HS 2017), this paper uses the method of survival analysis to describe primary and secondary school students’ first-time participation in extracurricular tutoring, and discusses the impact of family socio-economic background on the survival time of students’ first-time participation in extracurricular tutoring. This paper finds that, first, most of the primary and secondary school students in China have participated in extracurricular tutoring, and many of them have participated in extracurricular tutoring relatively early. Second, the time of primary and secondary school students who participate in extracurricular tutoring of subjects for the first time is significantly earlier than that of interest classes, and the difference is more prominent with the increase of grade. Third, there is a significant difference in the first time students from urban and rural and different social classes participate in extracurricular tutoring, but the difference gradually reduces with the increase of school period. Fourth, family socio-economic status has a significant positive impact on the time of students’ first participation in extracurricular tutoring. Students with better family socio-economic backgrounds are more likely to participate in extracurricular tutoring earlier, but this difference decreases with the increase of students’ learning period. In the context of “Double Reduction” policy, it is suggested that the government should continue to improve the quality of elementary education to meet the needs of parents and students for individualized education. Besides, it's important to provide academic counseling for disadvantaged students with poor academic performance, and provide subsidies for the students of vulnerable groups with poor academic performance. Also, it’s important to guide parents to choose extracurricular tutoring rationally, as well as objectively and dialectically examine the impact of extracurricular tutoring on the development of students.

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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
    Abstract1017)   HTML70)    PDF (668KB)(1429)      

    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.

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    What are the Changes in the Employment Situation of Chinese College Graduates in the Context of the 2021 Epidemic: An Empirical Study Based on National Survey Data in 2021 and 2020
    Tao Li, Nuan Sun, Zhihui Wu
    Journal of East China Normal University(Educational Sciences)    2022, 40 (2): 100-113.   DOI: 10.16382/j.cnki.1000-5560.2022.02.008
    Abstract1057)   HTML212)    PDF (924KB)(1422)      

    The “Comprehensive Investigation of Employment Situation of College Graduates in China During the Epidemic Period” conducted the second round of surveys in 34 provinces, autonomous regions and municipalities across the country for two and a half months from June to August 2021. Compared with the first round of survey in 2020, the following findings were found. (a) Nearly 50 percent of graduates signed a monthly salary of 3,001-5,000 yuan after tax. (b) The overall matching rate of job positions and majors increased significantly. (c) Employment satisfaction remained high but declined slightly, and (d) the rate of considering leaving had increased slightly. In 2021, unemployed graduates felt that the impact of epidemic factors on employment had significantly reduced. Their intentions of employment within the government system were obvious. Those who failed in civil service examinations and higher education had strong willing to retake the examinations. Different from the situation of employed graduates, the “salary package” exceeding the “place of work” became a significant employment factor for unemployed graduates. At the same time, the gap between expected salary and actual salary was widening. The ranking of employment expectations for private enterprises had dropped significantly. In the survey sample, employers’ recruitment plans were shrinking, but rates were rising. The salary levels were increasing, as well as the recruitment standards. The satisfaction of college graduates with the employment guidance work of the college remained good, which was the same as in 2020. Through logistic regression analysis, it was found that the variables include gender, household registration, family economic status, school type, school level, subject type, subject level, personal education, academic performance, entrepreneurial willingness etc., had significant differences in the impact of different categories on employment. The same as in 2020, men have a significant employment advantage over women. This employment advantage existed in the comparison of economically non-difficult families with economically difficult families as well. Meanwhile, employment advantage was showed in the comparative between colleges of science and engineering and finance and agriculture and forestry colleges, national "Double Tops" universities and higher vocational college, humanities and social sciences, national double first-class disciplines and ordinary disciplines, master’s degrees and bachelor’s degrees. The graduates from top 20 percent of professional rankings had more advantage than the 20-60 percent and bottom 40 percent graduates. Difference from 2020, the findings show that rural household registration graduates have a higher employment probability than urban household registration graduates, current graduates from medical colleges and normal colleges had a higher employment probability than colleges of science and engineering graduates, the employment probability of undergraduate graduates was lower than that of college graduates, and the employment probability of agricultural medicine was higher than that of humanities.

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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
    Abstract834)   HTML302)    PDF (1287KB)(1383)      

    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.

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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
    Abstract837)   HTML127)    PDF (2774KB)(1368)      

    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.

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    Report on Chinese Adolescence’s Development of Social and Emotional Skills
    Zhenguo Yuan, Zhongjing Huang, Jingjuan Li, Jing Zhang
    Journal of East China Normal University(Educational Sciences)    2021, 39 (9): 1-32.   DOI: 10.16382/j.cnki.1000-5560.2021.09.001
    Abstract1062)   HTML1148)    PDF (2707KB)(1355)      

    This report analyzes the Suzhou data in the SSES main study on 10-year-old and 15-year-old students’ social and emotional skills. The results show that 10-year-old students score higher on all 15 social and emotional skills than 15-year-old students. Among 10-year-old students, girls score higher on cooperation, empathy, sociability, persistence, and tolerance than boys. Except for tolerance, among 15-year-old students, boys score higher on other social and emotional skills than girls. Optimism is by far the most closely related ability to life satisfaction and psychological well-being, followed by energy and trust. Stress resilience and optimism are most closely related to students’ test anxiety. In general, sense of school belonging and teacher-student relationship are positively correlated with all 15 social and emotional skills, in particular with optimism, curiosity and cooperation. In contrast, school bullying is negatively correlated with all 15 social and emotional skills, in particular with emotion regulation (optimism, emotional control and stress resilience).

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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
    Abstract942)   HTML90)    PDF (1388KB)(1346)      

    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.

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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
    Abstract794)   HTML69)    PDF (964KB)(1296)      

    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.

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    The Technical Report on OECD Study on Social and Emotional Skills of Chinese Adolescence
    Jing Zhang, Yipeng Tang, Jiajun Guo, Zhifang Shao
    Journal of East China Normal University(Educational Sciences)    2021, 39 (9): 109-126.   DOI: 10.16382/j.cnki.1000-5560.2021.09.007
    Abstract806)   HTML587)    PDF (780KB)(1255)      

    This technical report presents the analysis of Suzhou data in the SSES main study and the assessment of psychometric properties of items and scales that involve a series of iterative modelling and analysis steps. These steps included the calculation of Alpha and Omega to evalute the subscales’ relibiabilities; the application of confirmatory factor analysis (CFA) to evaluate constructs; multiple-group confirmatory factor analysis (MGCFA) to review measurement equivalence across groups (age cohorts and gender groups); and using the Item Response Theory (IRT) and Generalised Partial Credit Model (GPCM) to scale items and generate scores for study participants.

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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
    Abstract591)   HTML117)    PDF (915KB)(1229)      

    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.

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    An Application of a PBL+CT Teaching Model in Primary Mathematics for Cultivating Students’ Computational Thinking——Taking “How to Enclose the Largest Area” as an Example
    Yi Zhang, Jue Wang, ling Xie, Dandan Wang, Xing Li, Wei Mo
    Journal of East China Normal University(Educational Sciences)    2021, 39 (8): 70-82.   DOI: 10.16382/j.cnki.1000-5560.2021.08.006
    Abstract62)   HTML152)    PDF (1044KB)(1213)      

    “Computational thinking” has been an important indicator for evaluating students’ high-order thinking in K-12 education in recent years. It has common thinking modes with mathematics thinking in many fields such as problem-solving. Therefore, developing computational thinking in mathematics has become a global trend. A three-layer “PBL+CT” theoretical model in primary school mathematics to develop students’ computational thinking is built. In the model, the content layer is PISA-oriented and drives teaching through the problem, the teaching layer is the mathematical model of non-programming plugged that integrates with computational thinking elements and constructs the problem-solving teaching procedure, and the goal layer is of computational thinking in relevant to the six core skills of decomposition, abstraction, algorithmic thinking, critical thinking, problem-solving, and collaborative learning. The course How to Enclose the Largest Area in primary mathematics was taken as an example, specific teaching designs and implementations in terms of the theoretical model were conducted, and the effectiveness of the model was verified in this research. Classroom observations, questionnaires, self-assessments, and interviews were used to verify the effectiveness of the model in promoting the development of computational thinking in primary students. It is found that the “PBL+CT” teaching model in primary school mathematics can significantly cultivate students’ computational thinking, especially in the fields of decomposition, algorithmic thinking, and collaborative learning. Based on the results, the research further summarizes and reflects on the mathematics teaching of “PBL+CT” to promote the development of students’ computational thinking in primary school.

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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
    Abstract869)   HTML85)    PDF (2035KB)(1173)      

    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.

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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
    Abstract845)   HTML70)    PDF (640KB)(1172)      

    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.

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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
    Abstract2087)   HTML134)    PDF (755KB)(1145)      

    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.

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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
    Abstract2251)   HTML105)    PDF (1016KB)(1142)      

    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.

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