基于转型视阈中的高校继续教育组织机构革新

受制于社会转型又有助于社会转型的高校继续教育,正沿着学历取向型-职业取向型-学习取向型的轨迹嬗变。为推进高校继续教育转型,高校继续教育的组织机构必须树立创业型组织、服务型组织、学习型组织等现代理念,使传统的1字型、一字型、十字型组织结构形式逐渐过渡到结构流畅、运行高效的矩阵制非实体型协作式管理的组织形式上来,为高校继续教育转型发挥组织机构应有的职能。     

民汉双语文化背景下白族学生英语习得问题研究

通过对自语与英语的语言特点（语音、语法等方面）异同之对比,分析母语干扰白族学生英语学习,包括语音干扰、语序干扰等。提出一些针对白族学生英语教学的建议和措施。     

社会文化学理论和第二语言教师教育

社会文化学理论是基于前苏联心理学家和教育家维果斯基的理论而发展出来的一个学术流派。最近二三十年来,这一理论在第二语言教师教育领域得到了广泛重视。本文介绍社会文化学理论在第二语言教师教育领域引起的改变,说明第二语言教师教育领域应用该理论时所采用的具体方法,并指出该理论中值得注意的若干问题。     

德国高等教育国际化研究

高等教育国际化是时代发展的趋势,而德国作为世界上较发达国家之一,研究其国际化能更好地帮助人们理解高等教育国际化的进程.因此,分析德国为促进本国高等教育国际化实施的主要政策,对管理国际化的机构、资金支持、国际合作和学生流动4个指标进行探讨与分析.     

基于GameFlow模型的教育游戏黏着度分析研究

黏着度的产生会让玩家对游戏产生感情而愿意追加成本,比如时间、精力、金钱等。为此,增强教育游戏对学生的持续吸引力,即尽可能地提高教育游戏对学生的"黏着度",是教育游戏设计和开发亟待解决的问题。通过分析游戏黏着度产生的原因,包括审美体验、成就感、归属感和好奇心;进而以GameFlow模型为基础提出了教育游戏黏着度提高的切入点,认为应从注意、挑战、技能、目标、反馈、控制、沉浸和交互几个方面着手;最后提出了教育游戏黏着度的提高策略,详细分析了玩家视角、"叙事"和"交互设计"在教育游戏中的实施方案。     

对分课堂模式在药用植物学与生药学教学中的应用

药用植物学与生药学是高校药学专业的核心课程。对分课堂是一种崭新的教学模式,有效融合了传统讲授式教学与讨论式教学的优点,能够提高学生学习的积极性和主动性。本文将对分课堂教学模式应用到药用植物学与生药学的教学实践中,发现该模式有效地提高了学生的课堂参与度,激发了学生的学习积极性,促进了独立思考能力和团队协作能力。     

初中道法课要体现“真”

初中道法课部编教材体现了以学生为中心的学科思想,这对思政理论课老师提出了更高的要求。思政理老师不仅要会教书,更要育好人,坚持立德树人,五育并举,在新时期的教学之路上努力打造出真实的课堂、有效的课堂,使教学更“真”。     

Anisotropic fluoride nanocrystals modulated by facet-specific passivation and their disordered surfaces

Rutile-type fluorides have been proven to be active components in the context of emerging antiferr-omagnetic devices. However, controlled synthesis of low-dimensional, in particular two-dimensional (2D), fluorides in a predictable and deterministic manner remains unrealized because of a lack of efficient anisotropic control, which impedes their further development in reduced dimensions. We report here that altered passivation of {110} growing facets can direct the synthesis of rutile-type fluoride nanocrystals into well-defined zero-dimensional (0D) particulates, one-dimensional (1D) rods and 2D sheets in a colloidal approach. The obtained nanocrystals show positive exchange bias and enhanced magnetic transition temperature from the coexistence of long-range antiferromagnetic order and disordered surface spins, making them strong alternatives for flexible magnetic devices and sensors.     

Predicting future dynamics from short-term time series using an Anticipated Learning Machine

Predicting time series has significant practical applications over different disciplines. Here, we propose an Anticipated Learning Machine (ALM) to achieve precise future-state predictions based on short-term but high-dimensional data. From non-linear dynamical systems theory, we show that ALM can transform recent correlation/spatial information of high-dimensional variables into future dynamical/temporal information of any target variable, thereby overcoming the small-sample problem and achieving multistep-ahead predictions. Since the training samples generated from high-dimensional data also include information of the unknown future values of the target variable, it is called anticipated learning. Extensive experiments on real-world data demonstrate significantly superior performances of ALM over all of the existing 12 methods. In contrast to traditional statistics-based machine learning, ALM is based on non-linear dynamics, thus opening a new way for dynamics-based machine learning.     

Programmable deformation of patterned bimorph actuator swarm

Graphene-based actuators featuring fast and reversible deformation under various external stimuli are promising for soft robotics. However, these bimorph actuators are incapable of complex and programmable 3D deformation, which limits their practical application. Here, inspired from the collective coupling and coordination of living cells, we fabricated a moisture-responsive graphene actuator swarm that has programmable shape-changing capability by programming the SU-8 patterns underneath. To get better control over the deformation, we fabricated SU-8 micropattern arrays with specific geometries and orientations on a continuous graphene oxide film, forming a swarm of bimorph actuators. In this way, predictable and complex deformations, including bending, twisting, coiling, asymmetric bending, 3D folding, and combinations of these, have been achieved due to the collective coupling and coordination of the actuator swarm. This work proposes a new way to program the deformation of bilayer actuators, expanding the capabilities of existing bimorph actuators for applications in various smart devices.