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试论双语教师培养模式

双语教育是我国少数民族教育的主要模式之一,制约双语教育发展的瓶颈是双语教师数量不足和质量欠佳。文章论证了加强双语教师培养的必要性,阐述了双语教师的培养目标,并根据不同层次双语教育发展的需要,设计了七种双语教师培养模式。     

初中生数学学习兴趣培养策略研究

初中阶段是培养学生数学学习兴趣的关键时期。教师不仅要教会学生知识与技能,而且应该培养学生的数学学习的兴趣。初中数学教育要真正为学生的可持续发展着想,就必须把数学学习兴趣培养研究放在突出的位置。本文针对数学学习特点,从三个方面说明初中数学学习兴趣培养的策略,即基于数学认知、情感和个体差异的教学策略。     

The Progress and Strategy of National Training for K-12 Teachers in China in the New Era

The reform of rural teacher team building has entered the deep-water zone where tough challenges must be met in the new era. In order to adapt to the new situation and cope with the new challenges, the national training of K-12 teachers in China should set up the concept of high-quality products, summarize the experience of the grass-roots level, aim at the prominent problems, give full play to the exemplary and leading role of the National Training Plan, and promote the comprehensive improvement of the quality and efficiency of teacher training. During the 13th Five-Year Plan period, the national training of K-12 teachers in China has achieved gratifying results, as well as considerable problems in the implementation of the National Training Plan. On the basis of sorting out the progress of the national training for K-12 teachers in the new era, this paper puts forward development strategies.     

乡村骨干教师培训需求分析:意义、困窘与突破

近年来,我国乡村教师队伍建设工作在政府政策和经费的大力支持下取得了显著成就,但部分培训低效仍是阻碍教师个人专业发展、教学质量提升的主要因素。培训需求分析是提升教师培训质量的重要路径。教师培训需求分析是指对教师专业素养应然与实然之间的差距进行诊断、确定的方法。当前,乡村骨干教师培训需求分析工作不容乐观。以骨干教师提升国培项目为例,在OTP模式指导下明确乡村骨干教师培训需求分析中现存的三个"缺乏",建议关注需求分析的覆盖对象、数据收集、对象需求类别、对象需求变动以及从战略高度层面进行分析,以期对乡村骨干教师培训需求分析工作的改善优化裨益加持,切实提升培训实效。     

乡村女教师培训意愿调查与需求审视 ——中部脱贫地区A省D县的实证研究

实施乡村地区教师精准培训近年来得到了党和政府的高度关注,统计数据显示,我国乡村地区女教师群体数量和占比越来越大,乡村女教师已经成为我国乡村教育发展的重要支撑,在乡村振兴中发挥着特殊作用。调查显示,乡村女教师能够接受教育培训的机会相对较少,成为影响她们扎根基层、扎根农村、潜心教学的“心痛”。研究采用实证方式,选取中部脱贫地区A省D县抽样调查了346名乡村女教师,了解她们接受教育培训的实际状况、个人意愿和现实处境等,分析她们参加教育培训的影响因素与存在困难、相互间的关联关系和影响程度等,探索提出解决问题的意见和建议,并呼吁政府部门和社会组织对乡村女教师培训给予更多支持和关注,以期为保障乡村教育的稳定、健康、优质发展,推动乡村振兴和教育高质量发展提供启示。     

A Convolutional Attention Network for Unifying General and Sequential Recommenders

General recommenders and sequential recommenders are two modeling paradigms of recommender. The main focus of a general recommender is to identify long-term user preferences, while the user’s sequential behaviors are ignored and sequential recommenders try to capture short-term user preferences by exploring item-to-item relations, failing to consider general user preferences. Recently, better performance improvement is reported by combining these two types of recommenders. However, most of the previous works typically treat each item separately and assume that each user–item interaction in a sequence is independent. This may be a too simplistic assumption, since there may be a particular purpose behind buying the successive item in a sequence. In fact, a user makes a decision through two sequential processes, i.e., start shopping with a particular intention and then select a specific item which satisfies her/his preferences under this intention. Moreover, different users usually have different purposes and preferences, and the same user may have various intentions. Thus, different users may click on the same items with an attention on a different purpose. Therefore, a user’s behavior pattern is not completely exploited in most of the current methods and they neglect the distinction between users’ purposes and their preferences. To alleviate those problems, we propose a novel method named, CAN, which takes both users’ purposes and preferences into account for the next-item recommendation. We propose to use Purpose-Specific Attention Unit (PSAU) in order to discriminately learn the representations of user purpose and preference. The experimental results on real-world datasets demonstrate the advantages of our approach over the state-of-the-art methods.     

Query Generation and Buffer Mechanism: Towards a better conversational agent for legal case retrieval

In legal case retrieval, existing work has shown that human-mediated conversational search can improve users’ search experience. In practice, a suitable workflow can provide guidelines for constructing a machine-mediated agent replacing of human agents. Therefore, we conduct a comparison analysis and summarize two challenges when directly applying the conversational agent workflow in web search to legal case retrieval: (1) It is complex for agents to express their understanding of users’ information need. (2) Selecting a candidate case from the SERPs is more difficult for agents, especially at the early stage of the search process. To tackle these challenges, we propose a suitable conversational agent workflow in legal case retrieval, which contains two additional key modules compared with that in web search: Query Generation and Buffer Mechanism. A controlled user experiment with three control groups, using the whole workflow or removing one of these two modules, is conducted. The results demonstrate that the proposed workflow can actually support conversational agents working more efficiently, and help users save search effort, leading to higher search success and satisfaction for legal case retrieval. We further construct a large-scale dataset and provide guidance on the machine-mediated conversational search system for legal case retrieval.     

Characterizing multi-domain false news and underlying user effects on Chinese Weibo

False news that spreads on social media has proliferated over the past years and has led to multi-aspect threats in the real world. While there are studies of false news on specific domains (like politics or health care), little work is found comparing false news across domains. In this article, we investigate false news across nine domains on Weibo, the largest Twitter-like social media platform in China, from 2009 to 2019. The newly collected data comprise 44,728 posts in the nine domains, published by 40,215 users, and reposted over 3.4 million times. Based on the distributions and spreads of the multi-domain dataset, we observe that false news in domains that are close to daily life like health and medicine generated more posts but diffused less effectively than those in other domains like politics, and that political false news had the most effective capacity for diffusion. The widely diffused false news posts on Weibo were associated strongly with certain types of users — by gender, age, etc. Further, these posts provoked strong emotions in the reposts and diffused further with the active engagement of false-news starters. Our findings have the potential to help design false news detection systems in suspicious news discovery, veracity prediction, and display and explanation. The comparison of the findings on Weibo with those of existing work demonstrates nuanced patterns, suggesting the need for more research on data from diverse platforms, countries, or languages to tackle the global issue of false news. The code and new anonymized dataset are available at https://github.com/ICTMCG/Characterizing-Weibo-Multi-Domain-False-News.     

Predicting future sedentary behaviour using wearable and mobile devices

Sedentarism is a common problem that can affect human health and wellbeing. Predicting sedentary behaviour is an emerging area that can benefit from data collected from sensors available in ubiquitous devices, such as wearables and smartphones. In this paper, we present an approach aiming at predicting the sedentary behaviour of a user from data collected from sensors installed in wearable/mobile devices. We compare personal and impersonal models using a real-life dataset consisting of sensing data of 48 users during 10 weeks. We found that impersonal models using Deep Neural Networks were able to accurately predict the subject’s future sedentary behaviour.