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"Environmental sciences Study and teaching China."
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Developing AI Literacy for Primary and Middle School Teachers in China: Based on a Structural Equation Modeling Analysis
As smart technology promotes the development of various industries, artificial intelligence (AI) has also become an important driving force for innovation and transformation in education. For teachers, how to skillfully apply AI in teaching and improve their AI literacy has become a necessary goal for their sustainable professional development. This research examines the correlations among the dimensions of AI literacy of teachers in order to promote the effectiveness of class teaching and the adoption of artificial intelligence literacy (AIL). Our findings are based on the analysis of 1013 survey results, where we tested the level of AI literacy of teachers, including Knowing and Understanding AI (KUAI), Applying AI (AAI), Evaluating AI Application (EAIA), and AI Ethics (AIE). We find that AAI had a significant, positive effect on the other three dimensions. Thus, based on the analysis, the government should take action to cultivate teachers’ AI literacy. In order to improve teachers’ AI literacy, the choice of curriculum, content, methods, and practical resources for special training should be diverse and committed to making AI literacy an essential enabler for teachers’ sustainable future development.
Journal Article
Pedagogical Design of K-12 Artificial Intelligence Education: A Systematic Review
In response to the growing popularity of artificial intelligence (AI) usage in daily life, AI education is increasingly being provided at the K-12 level, with relevant initiatives being launched worldwide. Examining how these programs have been implemented and summarizing useful experiences is thus imperative. Although prior reviews have described the characteristics of AI education programs in publications, the papers reviewed were mostly nonempirical reports, and the analysis typically only involved a descriptive summary. The current review focuses on the most recent empirical studies on AI teaching programs in K-12 contexts through a systematic search of the Web of Science database from 2010 to 2022. To provide a comprehensive overview of the status of AI teaching and learning (T&L), 32 empirical studies were analyzed both descriptively and thematically. We analyzed (1) the research status, (2) the pedagogical design, and (3) the assessments and outcomes of the AI teaching programs. An increasing number of studies have focused on AI education at the K-12 stage, but most of them have a small sample size. Moreover, the data were mostly collected through interviews and self-reports. We reviewed the pedagogical design of AI teaching programs by using Gerlach and Ely’s pedagogical design model. The results comprehensively delineated current AI teaching programs through nine dimensions: learning theory, pedagogical approach, T&L activities, learning content, scale, teaching resources, prior knowledge prerequisite, aims and objectives, assessment, and learning outcome. The results highlighted the positive impact of current AI teaching programs on students’ motivation, engagement, and attitude. However, we observed a lack of sufficient research objectively measuring students’ knowledge acquisition as learning outcomes. Overall, in this paper, we discussed relevant findings in terms of research trends, learning content, teaching units, characteristics of the pedagogical design, and assessment and evaluation by providing illustrations of exemplary designs; we also discussed future directions for research and practice in AI education in the K-12 context.
Journal Article
Promoting STEAM Education in Primary School through Cooperative Teaching: A Design-Based Research Study
The COVID-19 pandemic has highlighted the importance of students’ information literacy, computer skills, and research competencies for self-regulated learning and problem solving. STEAM education, with interdisciplinary knowledge building and higher-order thinking development as its main purpose, is considered essential for students’ sustainable development in the post-pandemic era. However, STEAM education in China’s K-12 schools is facing several problems, such as insufficient qualified teachers, unsustainable development, and difficulty in achieving meaningful discipline integration. To address these problems, this study proposes an innovative STEAM education model supported by cooperative teaching and theories of project-based learning and collaborative learning. After two iterations of design, evaluation, and revision, the proposed STEAM education model and a set of instructional design principles were validated. The resulting model features a multi-teacher cooperative strategy, detailed and diverse scaffolding, familiar themes for students, the integration of STEAM education into formal curricula, and extended instruction hours. The study results suggest that cooperative teaching can facilitate meaningful discipline integration and can alleviate the STEAM faculty shortage. This study produced five proven instructional design principles for conducting STEAM education supported by cooperative teaching in primary schools.
Journal Article
Artificial Intelligence and New Technologies in Inclusive Education for Minority Students: A Systematic Review
Artificial intelligence (AI) and new technologies are having a pervasive impact on modern societies and communities. Given the potential of these new technologies to transform the way things are done, it is important to understand how they can be used to support inclusive education, particularly regarding minority students. This systematic review analyzes the advantages and challenges of using AI and new technologies in different sociocultural contexts, and their impact on minority students. In terms of advantages, this review found that AI and new technologies (a) improved student performance, (b) encouraged student interest in STEM/STEAM, (c) promoted student engagement, and (d) showed other advantages. This review also identifies the main challenges associated with the use of AI and new technologies for inclusive education: (a) technological challenges, (b) pedagogical challenges, (c) dataset limitations, (d) low satisfaction using technology, and (e) cultural differences. This review proposes some solutions to these challenges at the pedagogical, technological, and sociocultural levels, and also explores important aspects of inclusive education that address the students’ sociocultural diversity. The findings and implications will aid teachers, practitioners, and policymakers in making decisions on the effective use of AI and new technologies to support sociocultural inclusiveness in education.
Journal Article
Education Development in China: Education Return, Quality, and Equity
As the biggest developing country with the largest population in the world, China has made great achievements in education development, which has contributed tremendously to reducing poverty and boosting prosperity in the past decades. However, in the course of education development, many problems and issues have emerged, which have also been extensively studied by scholars in various fields in both China and international contexts. Among the myriad of research topics, three research foci stand out as the most concerning and studied: education return, education quality, and education equity. This paper draws on both international research literature and evidence from China to discuss education development issues including education return, education quality, and education equity, and suggests future directions for research and practice to enhance education development and to achieve a sustainable future.
Journal Article
Online Teaching during COVID-19 Pandemic: Teachers’ Experiences from a Chinese University
This paper explores the experiences of Chinese university teachers during the COVID-19 pandemic, with a particular emphasis on the teaching and learning methods adopted and the benefits and challenges encountered in the process. It is based on semi-structured interviews with 13 Chinese university teachers selected through purposive sampling. The findings suggest that the COVID-19 pandemic forced the university and teachers to adopt online teaching and learning without necessary preparations. Most of the teachers had no adequate ICT and pedagogical training to engage in online teaching and learning. The teachers used the little knowledge they had to learn creating videos and managing online classes gradually. In addition to the flexibility benefits, online learning is expected to transform the teaching and learning process in China to become more interactive and student-centered, which would be a significant achievement for teachers who have been practicing traditional teaching methods. This research provides a better understanding of the benefits and challenges of online learning, which could be vital for future adjustments or educational reforms.
Journal Article
Research Landscape of Adaptive Learning in Education: A Bibliometric Study on Research Publications from 2000 to 2022
Adaptive learning is an approach toward personalized learning and places the concept of “learner-centered education” into practice. With the rapid development of artificial intelligence and other technologies in recent years, there have been many breakthroughs in adaptive learning. Thus, it is important to gain insight into the evolution of related research and to track the research frontiers to further promote its development. This study used CiteSpace and VOSviewer to conduct a bibliometric analysis of 644 adaptive learning journal papers indexed in the WoS database from 2000 to 2022. This study presented a general view of the field of adaptive learning research over the last two decades using quantitative analysis. Currently, adaptive learning research is rapidly developing. In terms of the major research forces, a core group of authors including Qiao J. F., Han H. G. and Song Q has been formed; the major publishing country in this field is China; the core publishing journals include IEEE Transactions on Neural Networks and Learning Systems. Four major research topics in this field were identified using cluster analysis, namely the application of deep learning in educational data analysis, the development and application of adaptive learning model in AI education, the development and application of intelligent tutoring system in tutoring and teaching, cutting-edge modeling technology for feature modeling and knowledge tracing. Through evolution analyses, the logic of adaptive learning research’s development was determined; that is, technological changes have played a key role in the development of this field. Following the logic, we presented three frontiers of adaptive learning with burst terms: feature extraction, adaptation model and computational modeling. Adaptive learning is a core research topic for both computer science and educational technology disciplines, and it is also an important field where emerging technologies empowering education and teaching can play a part. The findings of the study clearly presented the current research status, evolutionary logic and research frontiers of this topic, which can provide references for the further development of this research field.
Journal Article
Do Playfulness and University Support Facilitate the Adoption of Online Education in a Crisis? COVID-19 as a Case Study Based on the Technology Acceptance Model
A large number of universities worldwide are paying more and more attention to the application and exploration of online education. As the group with the most significant number of online education users, their participation attitude and participation intention directly determine the teaching performance of online education. This research will incorporate playfulness teaching and scenario variables that reflect the universities’ ability to respond to emergencies. Based on the technology acceptance model, this research proposes an integrated research model of online education participation intention to investigate university students’ online education participation intention to reveal the key factors and mechanisms that affect online education participation intention. A structural equation model of participation intention is constructed, and 342 valid samples are obtained by questionnaire survey. The empirical results of PLS-SEM show that: (1) students’ participation attitude positively affects their participation intention; (2) the perceived ease of use and usefulness positively affect their participation attitude, and the perceived usefulness and ease of use affect their participation intention through the complete mediation of participation attitude; (3) the perceived playfulness does not have a significant impact on participation attitude but has a positive impact on participation intention; (4) the innovative discovery university support positively moderates the relationship between participation attitude and intention during such emergencies. The research found that improving students’ attitudes toward participation, perceived ease of use, usefulness, playfulness, and strengthening university support are all helpful to optimize students’ participation intention in online education. At the same time, it also explored operability suggestions for improving the quality of online education and optimizing future education.
Journal Article
Predict water quality using an improved deep learning method based on spatiotemporal feature correlated: a case study of the Tanghe Reservoir in China
The ecological health of water quality is an essential factor in the survival and development of human beings. By setting up multiple sensors in the water body, people observe the factual state of water quality, and collect a large number of sequence data containing time information. In order to effectively utilize water quality time series data and predict future water quality changes, this paper proposes an improved deep learning method based on spatiotemporal feature correlated, called the convolution recurrent basis expansion analysis architecture. The model improves the original model by using the ability of convolutional neural network structure to extract spatial features and the continuous memory ability of recurrent neural network structure. Then, the physical prior knowledge is integrated into the proposed network to limit the unreasonable results predicted in the feasible solution space to further improve the learning efficiency of the model. For specific research objects, the model can mine multi-dimensional features in water quality sequences at different depths, and learn the temporal features of the sequences hierarchically. In order to verify the performance of the proposed model, we apply the proposed model to the water quality dataset collected in Tanghe Reservoir for simulation. The results of the study demonstrate that our model is suitable for water quality prediction and has distinct advantages over traditional neural networks. Our model will make accurate predictions of future changes in water quality and provide technical support in the refined water quality automatic monitoring system.
Journal Article