Despite the importance of artificial intelligence in education, its effectiveness in this field requires more empirical research for corroborating evidence. In this study, a dance skills teaching, evaluation, and visual feedback (DSTEVF) system was developed based on AI technology and applied in a dance classroom. Forty participants from a vocational school studying dance were randomly divided into two groups: DSTEVF-based learning (experimental group, n = 19) and traditional teaching (control group, n = 21). The DSTEVF-based learning approach significantly improved students' dance skills and self-efficacy. However, there was no significant effect on students' motivation. Students with higher levels of motivation and self-efficacy benefitted more from DSTEVF-based learning than those with lower levels. Evidently, it is possible to establish a smart classroom by applying DSTEVF to the teaching activities of dance education, physical education, and other disciplines.

The allocation of high-quality teachers significantly impacts educational equity and quality. Current strategies for achieving balanced teacher distribution primarily adopt two approaches: (1) structural optimization through reallocation of existing teachers, and (2) incremental expansion via investments to diversify teacher roles and instructional services. This study examines an innovative government-led teacher distribution model implemented in Beijing Middle School teachers’ Open Tutoring Program. In this program, high-quality teachers are reallocated to meet students’ personalized tutoring needs through online tutoring modes such as “one-to-one tutoring” via the internet. Longitudinal analysis shows that sustained participation in the program correlates with improved student academic performance and enhances teachers’ professional development. Compared to conventional methods, the Open Tutoring Program offers a more efficient and flexible mechanism for deploying expert instructors. However, challenges remain in achieving precise student–tutor matching. Furthermore, from an educational ecology perspective, the program exerts complex multilevel influences across micro-, meso-, exo-, and macrosystem levels. These findings provide valuable insights for scaling similar educational innovations and can serve as references for optimizing teacher distribution models in various educational contexts.

The Expert-based Collective Advising Mechanism (ECAM) was embedded in the open online tutoring project to provide free tutoring for rural students and to help them improve their academic performance, and thus, to solve education inequity. Taking urban and rural students in Tongzhou District of Beijing as the objects, this study gathered data on the tutoring frequency, the durations students received tutoring, the number of teachers who tutored the same student, and the students' academic performance. The tutoring behavior and academic performance of rural and urban students were compared. The results showed that the ECAM could improve students' academic performance. Rural and urban students who received tutoring got the same degree of performance appreciation. Finally, gender, school type, and tutoring duration predicted academic performance positively.

This study examines the impact of mentor roles on teachers’ practical knowledge generation during mentor-apprentice dialogues, focusing on differences in cognitive network structures across mentoring styles. Using Epistemic Network Analysis (ENA), the study reveals that strategy knowledge and context knowledge play central roles in the development of practical knowledge, while self-knowledge is underdeveloped. The research highlights the importance of integrating expert knowledge transmission with novice teachers’ introspection in professional development and provides empirical support for refining mentoring approaches to ensure balanced and effective learning experiences for novice educators.

With the popularity of online one-to-one tutoring, there are emerging concerns about the quality and effectiveness of this kind of tutoring. Although there are some evaluation methods available, they are heavily relied on manual coding by experts, which is too costly. Therefore, using machine learning to predict instruction quality automatically is an effective way to reduce human costs. Three classification methods are analyzed in this article: 1) random forest algorithm with human-engineered descriptive features; 2) long and short-term memory algorithm with acoustic features generated by open speech and music interpretation by large space extraction toolkit; and 3) convolutional neural network algorithm with Mel spectrogram of the audio. The results show that the three approaches can complete the prediction task well, with the second approach exhibiting the best accuracy. The importance of the features in these classification models is analyzed according to eXplainable Artificial Intelligence techniques (i.e., XAI) and statistical feature analysis methods. In this way, key indicators of high-quality tutoring are identified. This study demonstrated the usefulness of XAI techniques in understanding why some tutoring sessions are of good quality and others are not. The results can be potentially used to guide the improvement of online one-to-one tutoring in the future.

Promoting the online flow of high-quality teacher resources has emerged as an important way to pursue coordinated progress in burden reduction and quality improvement under the “double reduction” policy. Based on the Open Online Tutoring Program of Secondary School Teachers (hereinafter referred to as “the open tutoring program”) in City A, China, this study examined the factors influencing teachers’ continuance intention to participate in the open tutoring program and their structural relationships, using a structural equation modeling analysis based on 1,159 sample data. The results show that: Firstly, teachers’reputation enhancement, perceived playfulness, perceived usefulness, and perceived ease of use have a positive effect on teachers’continuance intention to participate in online after-school teaching services. Secondly, online teaching efficacy has no direct effect on teachers’ continuance intention, but rather impacts their reputation enhancement and beliefs about the value of the open tutoring program. Thirdly, social influence has a positive effect on teachers’ reputation. Fourthly, perceived ease of use has a positive effect on teachers’ perception of the value of the open tutoring program. In light of the above, relevant suggestions are proposed to encourage teachers to continue participating in online after-school teaching services.

Video-based teacher online learning enables teachers to engage in reflective practice by watching others' classroom videos, providing peer feedback (PF) and reviewing others' work. However, the quality and reliability of PF often suffer due to variations in teaching proficiency among providers, which limits its usefulness for reviewers. To improve the quality of PF, hybrid intelligence is a promising approach that enhances human evaluation with AI-based techniques. Thus, we developed a hybrid intelligence feedback (HIF) system, where PF is categorized and summarized by large language models (LLM), and accompanied with AI multimodal data analysis, all in accordance with a knowledge structure. To investigate the effectiveness of the HIF, we conducted a study involving 58 pre-service mathematics teachers. After their initial feedback provision on a classroom video, they were divided into two groups. One group received HIF, while the other received traditional PF. Both groups revised their initial feedback based on the same video with PF or HIF report, and were assigned two tasks, namely in-depth reflection and extensive reflection. We analysed the reflective writings generated in in-depth reflection using the Structure of Observed Learning Outcomes taxonomy, and examined the diversity of teachers' attentions in extensive reflection using information entropy. Compared to traditional PF, our findings indicated that HIF (a) facilitated more comments added in feedback revision, (b) promoted multi-structural and extended abstract level thinking in in-depth reflection, (c) encouraged more diverse attentions in extensive reflection. These results demonstrate the effectiveness of HIF in enhancing PF to promote reviewers' reflection. This efficacy can be attributed to the utilization of LLM to identify common elements within PF, guided by the human knowledge-based framework, as well as the integration of data-driven evidence to complement PF.Practitioner notes What is already known about this topic? Video-based teacher online learning allows teachers to reflect on their own or others' videos flexibly while providing and reviewing peer feedback using reflection tools. While the benefits of reflecting on one's own videos with peer feedback are widely recognized, there is limited empirical evidence supporting the advantages of reflecting on others' videos with peer feedback. The effectiveness of this process may be affected by the quality and reliability of the peer feedback provided. Using natural language processing techniques to enhance peer feedback can be feasible and effective. However, it is primarily used to address textual-level issues and is less effective in promoting professionalism. Multimodal data analysis has shown effectiveness in enhancing teaching behaviours and facilitating reflection. However, despite the widespread use of AI-based analysis on classroom videos, they often lack educational interpretations. Hybrid intelligence is a novel concept in learning science research, aiming to leverage both human and machine intelligence to enhance the overall effectiveness. What this paper adds? This study applied the concept of hybrid intelligence to video-based teacher learning by proposing a hybrid intelligence feedback (HIF) system, aiming to promote teachers' reflection on others' videos during the peer feedback review process. This study proposed the design of HIF module, where human peer feedback was enhanced by large language models, and machine data analysis was complemented with educational interpretations, all structured according to an expert knowledge structure. The results showed that the HIF was effective to stimulate teachers' higher-level thinking in in-depth reflection and enhanced the diversity of their attentions in extensive reflection. However, it may still be challenging for novices to comprehend and integrate newly noticed pedagogical strategies in the HIF with their internal knowledge structures during reflection. Implications for practice and/or policy With the rapid advancement of generative artificial intelligence, the utilization of large language models becomes more flexible and effective, enabling multitasking enhancement for peer feedback in collaboration with human's professional knowledge. Multimodal data analysis effectively collaborates with human observations by managing low-level observation aspects, allowing humans to concentrate on higher-level thinking guided by the educational interpretations. The effectiveness of the HIF system is influenced by teachers' pedagogical knowledge, prior feedback provision experience and data literacy. In the future research, these diversities need to be taken into account in the design of video-based PD incorporating HIF to assess its long-term efficacy.

Teacher networks and communities have played an important role in teacher professional development. In such contexts, teachers often receive extensive feedback from peers as part of social learning. However, many teachers have difficulty identifying essential information from a large amount of peer feedback, which may impede self-reflection and peer learning. This study proposes the use of computer-assisted visual analytics of peer feedback to address this challenge. A visualisation-based analytical dashboard was designed and applied to help teachers analyse and reflect on peer feedback in a lesson study community for Technological Pedagogical Content Knowledge (TPACK) development. Primary school teachers participated in the lesson study, in which they collaborated to discuss lesson plans, observe recorded lessons, and give peer ratings and comments using an online platform. By comparing the performance between those using the analytical dashboard and others not using it, the results show that the approach has promising effects on improving teachers' TPACK as reflected in the lesson plans and their perceived confidence in TPACK. The implications of the findings are also discussed.

In order to meet students’ increasingly personalized learning needs, the Chinese government has begun to try to carry out online after-school tutoring program. This study used the Propensity Score Matching (PSM) to analyze whether government-led online after-school tutoring program help students to improve their non-cognitive abilities, and further analyzed the factors influencing students’ choice of different forms of tutoring and the difference in the effect of students’ participation in different forms of tutoring. A questionnaire survey was conducted on 1833 7th-8th grade students in China. The results showed that disadvantaged students are more likely to participate in government-led online after-school tutoring program, and such program can effectively promote students’ non-cognitive abilities, while extracurricular commercial tutoring has a negative impact on students’ learning emotion. These findings can provide new ideas and suggestions on promoting the high-quality and balanced development of education.

提供具有公共服务性质的在线教育来完善教育服务体系,成为后疫情时代教育服务供给改革的方向之一.由于在线教育公共服务所涉及的参与主体在属性上具有多元性,治理理论成为学界在分析相关问题时会援用的思考角度.本文以治理理论为基础,以北京市"开放辅导"为例,通过分析"开放辅导"的价值目标、多元化主体角色,以及在在线教育服务提供与制度优化等关键环节中蕴含的运行机制,探讨应该如何组织个性化的在线教育公共服务供给.本文认为,建构基于数据的教育服务供给的关键是强化在线教育在回应个性化学习需求方面的优势,要借助数据实现角色职能与互动机制的创新,构建更大范围内的协同机制、培育更深厚的协同意识,推动不同层面的教育服务供给主体与接受者进入良性的互动循环,以释放多元主体的优势,促进在线教育服务供给健康发展.

This paper introduces a system that supports student-centered online one-to-one tutoring and evaluates the practical value of the system by running an experiment with 64 experienced mathematics teachers and 810 students in Grade 7. The experiment lasted for 50 days. A comprehensive evaluation was performed using students' academic performance before and after usage of the system and the system log files. By classifying the students into active and inactive usage groups, it was determined that active students significantly outperformed inactive students on posttests, but with a small effect size. The results also suggested that high prior knowledge students tended to benefit more from using the system than low prior knowledge students. An explanation for this result was that students with a high level of prior knowledge were more likely to have good-quality interactions with their teachers. Therefore, although some advantages of this type of student-centered online one-to-one tutoring are observed, in this system, both the students and the teachers need to be further facilitated to produce more effective tutoring interactions.

教研是提高教学质量和促进教师专业发展的主要方式.技术的出现变革了教研理念、方式和模式,正在赋能传统教研,使其向智慧教研转型.研究总结了智慧教研视域下技术对教研的赋能和支持作用,从教研的基本构成环节和要素角度,分析了智慧教研和传统教研的差异,并从模式融合、模式创生和系统变革三个阶段梳理了技术支持教师专业发展的方向、路径和典型应用模式.

拓展教育资源共享平台,构建教师帮扶协同机制,助力教育薄弱地区教师高质量专业发展是促进教育公平的重要内容.立足于被指导教师的自身教育实践和教育情景,及时为其提供同行、专家的支持和辅导是促进其专业个性化发展的重要逻辑.该研究依托"首都教育远程互助工程"之教育教学专项能力提升项目,探索发达地区中学在职优秀教师与偏远地区教师开展网络结对指导时,指导教师担任了怎样的角色,以及在交流对话中如何促进被指导教师的知识生成和发展.通过25835条编码分析发现:基于实践逻辑的跨区域在线伴随式指导能够为被指导教师提供个性化专业支持,促进了其TPACK知识的发展.在教研过程中,指导教师共体现了四种不同的指导角色身份,其指导角色的选择与双方间的交流内容彼此推动.当指导教师在交流中发挥主导时,更易引入与教学实践相关的话题,并就相关问题进行直接指令式指导,从而构建出其主要权威者的身份.同时,教学实践问题的交流与教师的指令式指导能推动被指导教师高层次知识的生成.该研究为进一步提高教师培养指导质量,以教育扶智促进教育公平化提供理论和实践依据.

This study constructed an online learning behavior framework consisting of two dimensions: knowledge processing and learning context. A C program practice APP named D-quiz was used to track the learning behavior of 543 freshmen over one semester. The collected data were then transformed into seven indicators that corresponded to the online learning model. Using five machine learning algorithms, this study analyzed the factors that impact online learning. Results demonstrated that both knowledge processing and learning context significantly impact online learning, with the Random Forest algorithm achieving the highest F1 Score of 0.743. Furthermore, the feature importance ranking indicated that learning context exerts a stronger influence on online learning effect than knowledge processing.

This study developed an online discussion forum that motivate students' knowledge seeking and sharing. The forum enables students post questions anonymously and provides a question or response prompt that invite students to engage in the discussion after completing a quiz. A quasi-experimental approach was conducted with a sample of 100 students. The results indicated that the students in the experimental group exhibited more active participation in knowledge seeking and sharing, as well as showed a higher density of group's social network. The findings provide some evidence for the value of the motivational design, in terms of anonymous questioning and prompt features, to address the challenge of under-contribution in online discussion forums.

线上线下相结合的课堂观察活动具有跨越时空、支持大规模协同等优势,已成为促进中小学教师专业发展的重要活动.但是,其在应用中存在突出问题,如难以从大量非结构化的多源反馈数据中及时发现教师集体共识信息,难以精确诊断与改进教学问题,难以实现教师同伴之间互助学习.为解决上述问题,该研究依据观察学习理论与学习分析技术,构建了包含课堂观察活动全过程数据采集、多元分析、可视化反馈报告、社会知识网络自动推荐、精准改进等要素的课堂观察多元分析与改进模型,并开发了支撑系统.研究结果表明该模型实现了对教师教学知识技能、观察参与行为与情感状态的及时诊断,能够推动课堂观察向数据驱动的科学决策与精准改进转变,实现观察者与被观察者的协同发展.

为学生提供丰富、优质的课后服务成为促进教育公平、解决社会民生问题的路径之一.北京市通过实施中学教师开放型在线辅导计划为经济落后地区学生提供优质课后服务,以丰富学生的学习活动,提高学生的学习成绩.但是学生在利用课后服务方面却表现出不同的行为特征,对学生产生了不同的作用.利用行为动力学方法和回归分析方法对7,999名学生参加课后服务的数据和学习成绩进行分析发现,学生在接受在线学习辅导次数、辅导时长和辅导时间间隔方面存在具有重尾效应的幂律特征,学生参加课后服务行为分布存在不均匀性.参加辅导的学生学习成绩增值显著大于未参加辅导学生的学习成绩增值,而且学生的辅导次数能够显著预测成绩增值.在在线课后服务过程中,引导学生长期、持续地参加在线辅导,对于改善学生的学习成绩具有重要意义.

本文借助点阵笔的纸笔数字书写技术和概念图的构思形式,研究学生内在构思过程的运演及与作文的关系及规律,基于此提出了发展学生作文构思能力的策略,为写作教学提供借鉴.