Abstracts

Towards supply-side reform of professional development for basic education teacher in the digital age

Youqun Ren,Xiaoying Feng,and Chun He

Against the backdrop of increasing global competition,new IT revolution,and impact of the COVID-19 pandemic,there has arisen an urgent need for reform in basic education teacher professional development in the digital age.Challenges,it is argued,exist in such areas as resources provision,procedure,management and governance of teacher professional development.These challenges are a result of structural imbalance between the supply of traditional teacher professional development services and the individualized demands imposed by teachers in the digital age.It is argued that one solution to this situation is evidence-based policy-making by taking advantage of big data generated by digital learning and information management platforms for teachers.Put specifically,reform in professional development for teachers in the sector of basic education needs to be facilitated from three aspects:resources provision,services,and governance both of providers and beneficiaries.The supply-side reform should be administered by leveraging digital platforms and digitalized means with the aim of building Teacher Digital Cockpit to make the best use of teacher big data as a driver for reform,promote digital transformation of professional development and teacher management,and facilitate macro-analysis and top-level decision-making related to teaching staff development.

Keywords：teacher professional development;supply-side reform;teaching staff development;fit-for-purpose professional development;digital transformation

Recognition and visualization of emotion in online peer feedback:promoting teachers’deep reflection and addressing teaching problems

Axi Wang,Shengquan Yu,Zhiqiang Zhan and Xiuying Liu

Online lesson study refers to a kind of professional development engaging teachers in adapting teaching design,implementing teaching,observing classroom activities,exchanging views after class,and reflecting on teaching in the form of learning community via online learning platforms to deliver specific professional development objectives.Online lesson study generates massive feedback text data.Intelligent recognition and visualization of emotion in this feedback texts can help identify teachers’issues of consensus and key teaching problems.This study sets out to use machine learning to develop a model to recognize and visualize emotion in online peer feedback.The model is intended to collect peer feedback texts,recognize emotion in texts based on BERT model,extract text themes,and self-generate emotion visualization reports.The model is tested by adopting the methods of experiment and case study.Findings from the research show that the model has a high accuracy,can identify issues of consensus by teachers quickly and generate visualization reports to facilitate teacher reflection and address teaching problems.

Keywords:online lesson study;peer feedback;machine learning;BERT model;recognition of emotion in text;visualization report;teaching problem;teaching reflection

Resolving the contradiction between large-scale education and personalized education:logical framework and practical approach to data-driven largescale personalized teaching

Xianmin Yang and Yao Zhang

Resolving the contradiction between massive education and personalized education is an important task of educational reform and development in the intelligent age.Rapid advancement of the new generation of information technology such as the Internet,big data,and artificial intelligence provides technical support for high quality development of personalized education on a large scale,attributing new meanings to personalized teaching.As a source of driving force and intelligence,big data plays a vital role in promoting large-scale personalized teaching.It is argued that data-driven large-scale personalized teaching should apply three logics:the process logic of profiling students accurately and diversifying means of education to ensure personalized growth,the data logic utilizing the framework which relies on data related to student all-round development and the method of four-tier(descriptive,diagnostic,predictive and prescriptive)analysis,and the synergic logic of joint education by school,family,government,and society.The ultimate goal is to enable each student to develop in an all-round but individualized manner.Suggestions on how to implement data-driven large-scale personalized teaching are discussed in relation to the process logic.

Keywords:educational big data;data-driven;personalized teaching;large-scale education;personalized education;accurate student profiling;diversified talent cultivation;personalized growth

STEM education in rural areas:possibility,dilemma and solution

Jian Si and Lichang Zhang

Educational development in China’s rural areas directly determines whether education in the whole country can proceed into the stage of high-quality development.Offering STEM education to rural children is a way to allow all rural children access to“balanced and with special features,equitable and of high quality”education.Possibilities of rural STEM education are discussed in terms of STEM literacies and conditions for cross-disciplinary exploration available in rural areas.Three barriers to rural STEM education are identified in this article,i.e.the gap in the so-called space justice,shortage of cultural capital,and the habitus of simulation instead of creativity.The solutions proposed include establishment of a mechanism to provide professional development for rural STEM teachers so that they can co-develop with their urban counterparts,exploration of themed STEM education models fit for rural education by borrowing from urban models,and development of rural STEM models.

Keywords:rural revitalization;educational equity;high-quality educational system;urban and rural education unification;STEM education;education quality;discipline literacy;teacher professional development