教学的探究本性及其实现策略

教学本身就具有探究的性质,但由于各种因素的影响,现实教学中教学的探究本性被有意无意地忽略了。新的时代背景下,教学改革要深刻认识教学的探究本性及其意义,创设机制,建立适宜学生探究的教学制度和氛围,注重学生问题意识和探究习惯的培养,灵活运用各种探究教学方式,注重与学生生活经验的联系,融合接受教学,发挥教学的整体效益,努力提高教学探究的有效性。     

“基于项目的科学”教学模式实施效果的研究

“基于项目的科学（Project—Based Science，简称PBS）”是一种探究教学模式．研究发现：采用PBS学习科学的学生在NAEP这样的大型测试中，大多数题目的平均得分高于国家样本．在科学探究问题上得分要高于国家样本．随着问题的拓展，PBS学生得分会更高，说明PBS探究教学模式能扩展学生的思维，发展学生的探究能力．     

Supporting accomplished facilitation: examining the use of appreciative inquiry to inform the development of learning resources for medical educators

This paper examines the use of appreciative inquiry (AI) to guide development of web-based learning resources for medical educators who facilitate simulation-based learning experiences for doctors-in-training. AI can be viewed as a positive form of action research, which seeks to avoid deficit-based analyses and solutions, and commonly associated defensiveness. The use of AI to guide research and curriculum development has hitherto received scant attention.

Simulated medical practice allows learners’ needs to take top priority because simulated patients replace real patients (whose care would otherwise be top priority). Each episode of simulated clinical practice is followed by facilitated debriefing. Facilitators complete an initial ‘train the trainers’ course (typically one or three days). Our learning resources aimed to complement and extend initial courses.

AI informed data collection and analysis. It focused attention on identifying and understanding what was good about contemporary debriefing practices. We identified examples of practical wisdom and designed resources to help make these more transparent and accessible to all facilitators. Selected video records and interview excerpts demonstrated key points. We found opportunities to introduce ideas that would extend facilitators’ expertise.

We critique AI as an approach to action research and curriculum development, and recommend further use of AI in other contexts.     

论小学语文教学中的探究性学习

21世纪是知识经济时代,我们必须树立崭新的教育理念。当前,改变学生的学习方式,培养学生发现问题、研究问题、解决问题的能力,已成为世界各国教育改革共同关注的焦点。在小学语文教学中,开展探究性学习有着十分重要的现实意义。     

基于VFP查询设计的探讨

查询是数据库信息管理系统的重要功能,是数据库管理系统(DBMS)的核心操作。通过SQL语句和设计查询程序,实现良好的查询数据界面,使用户可以选取任意输出字段,设置任意查询条件,实现语句可变,条件可变的查询设计要求。     

基于任务驱动的探究性学习的实施策略

依托常见的"任务驱动"教学法,从三个方面论述发挥学生主体性、培养学生探究能力的策略:一是教学过程中把握好探究程度,努力挖掘探究性学习的因素。二是注重教学过程中的探究途径的设计策略,即分析式探究、综合式探究及混合式探究。三是从探究性学习的主要环节入手,给出相关的教学策略。     

关于职业学校开展研究性学习的思考与尝试

研究性学习是一种通过研究某些课题来获取知识、形成能力的教学活动。与常规教学相比,它更重知识的实用性和可生成性,更重学生学习主动性的发挥和创新能力的培养。在职业学校开展研究性学习,应特别注意转变学生的观念,帮助其树立能够学好的自信,要在充分考虑学生的原有知能结构和未来的发展方向的基础上确立研究课题和进行研究活动。     

Can the development of a patient’s condition be predicted through intelligent inquiry under the e-health business mode? Sequential feature map-based disease risk prediction upon features selected from cognitive diagnosis big data

The data-driven mode has promoted the researches of preventive medicine. In prediction of disease risks, physicians’ clinical cognitive diagnosis data can be used for early prevention of diseases and, therefore, to reduce medical cost, to improve accessibility of medical services and to lower medical risk. However, researches involved no physicians’ cognition of patients’ conditions in intelligent inquiry under e-health business mode, offered no diagnosis big data, neglected the values of the fused text information generated by joint activities of online and offline medical data, and failed to thoroughly analyze the phenomenon of redundancy-complementarity dispersion caused by high-order information shortage from the online inquiry data-driven perspective. Besides, the risk prediction simply based on offline clinical cognitive diagnosis data undoubtedly reduces prediction precision. Importantly, relevant researches rarely considered temporal relationships of different medical events, did not conduct detailed analysis on practical problems of pattern explosion, did not offer a thought of intelligent portrayal map, and did not conduct relevant risk prediction based on the sub-maps obtained from the map. In consequence, the paper presents a disease risk prediction method with the model for redundancy-complementarity dispersion-based feature selection from physicians’ online cognitive diagnosis big data to realize features selection from the cognitive diagnosis big data of online intelligent inquiry; the obtained features were ranked intelligently for subsequent high-dimensional information shortage compensation; the compensated key feature information of the cognitive diagnosis big data was fused with offline electronic medical record (EMR) to form the virtual electronic medical record (VEMR). The formed VEMR was combined with the method of the sequential feature map for modelling, and a sequential feature map-based model for disease risk prediction was presented to obtain online users’ medical conditions. A neighborhood-based collaborative prediction model was presented for prediction of an online intelligent medical inquiry user’s possible diseases in the future and to intelligently rank the risk probabilities of the diseases. In the experiments, the online intelligent medical inquiry users’ VEMRs were used as the foundation of the simulation experiments to predict disease risks in chronic obstructive pulmonary disease (OCPD) population and rheumatic heart disease (RHD) population. The experiments demonstrated that the presented method showed relatively good metric performances in the VEMR and improved disease risk prediction.     

农村寄宿制学校留守儿童心理健康教育有效方法探究——以甘肃省古浪县永丰滩初级中学为例

留守儿童心理健康教育是学校教育的一大难题。经过研究分析,在学校日常管理中就如何进行留守儿童心理健康教育提出了以下路径:家校联手找出原因,一人一策加以疏导;学校提供交流平台,增加与父母的联系;加强课堂管理,有效渗透心理健康教育;加强学校心理健康教育,关爱留守儿童心理健康;提高监护人的素质,改善家庭教育环境;建立“留守儿童”成长档案,使其可持续得到关注。     

打造思维课堂 助推探究增值

科学课堂呼唤主动探究、深度探究、自主探究、合作探究。要打造思维课堂,让学生大胆猜想,助推主动探究;由浅入深开展科学实验,助推深度探究;由表及里进行模型建构,助推自主探究;交流评价互动互惠,助推合作探究,追求探究增值。