二元学习与创新绩效的作用机制——组织内部协作网络的调节作用

组织学习是驱动企业创新、实现技术追赶与超越追赶的关键因素,如何合理组织企业的利用式和探索式学习是企业构建研发体系的关键问题。学界对二元学习是否有利于企业的创新绩效存在截然不同的观点。同时,组织内部的情境因素是影响组织二元学习和创新绩效关系的最根本因素,组织内部研发协作网络是二元学习的重要载体。基于美国半导体行业企业十年间的专利数据,实证研究发现,不同维度的二元学习对企业创新绩效具有不同的影响,联合二元学习能够积极影响企业的创新绩效,平衡二元学习则会降低企业的创新绩效;内部研发协作网络密度在二元学习和创新绩效的关系中具有显著的调节作用,企业的内部协作网络密度越高,联合二元学习对企业创新绩效的积极影响越弱,平衡二元学习对创新绩效的负影响则加剧。     

多语际语言语音习得中的质性案例研究

语言迁移现象既发生在母语与第一外语学习过程中,也同样发生在学习第二外语的过程中,并且因此而变得复杂有趣。多种语言本身的独特性和差异性,使多语际语言基础对第一、第二外语语音学习过程产生了相互影响和作用。本研究以西班牙大学生和中国西班牙语专业大学生为研究对象,通过对比研究中文普通话、英语和西班牙语在语音方面的特点,探索母语、第一外语、第二外语之间的相互关系,及其对语音学习所产生的作用和影响,为多语际语音习得与外语教学提供有价值的借鉴和经验。     

Improving Risk Management: From Lame Excuses to Principled Practice

The three classic pillars of risk analysis are risk assessment (how big is the risk and how sure can we be?), risk management (what shall we do about it?), and risk communication (what shall we say about it, to whom, when, and how?). We propose two complements as important parts of these three bases: risk attribution (who or what addressable conditions actually caused an accident or loss?) and learning from experience about risk reduction (what works, and how well?). Failures in complex systems usually evoke blame, often with insufficient attention to root causes of failure, including some aspects of the situation, design decisions, or social norms and culture. Focusing on blame, however, can inhibit effective learning, instead eliciting excuses to deflect attention and perceived culpability. Productive understanding of what went wrong, and how to do better, thus requires moving past recrimination and excuses. This article identifies common blame‐shifting “lame excuses” for poor risk management. These generally contribute little to effective improvements and may leave real risks and preventable causes unaddressed. We propose principles from risk and decision sciences and organizational design to improve results. These start with organizational leadership. More specifically, they include: deliberate testing and learning—especially from near‐misses and accident precursors; careful causal analysis of accidents; risk quantification; candid expression of uncertainties about costs and benefits of risk‐reduction options; optimization of tradeoffs between gathering additional information and immediate action; promotion of safety culture; and mindful allocation of people, responsibilities, and resources to reduce risks. We propose that these principles provide sound foundations for improving successful risk management.     

非英语专业学生学习策略调查研究——以大学英语分级教学模式下的B级学生为例

通过对50名非英语专业B级学生的实证研究发现,英语学习观念滞后,尤其对学习策略的片面性认识在很大程度上制约了学生英语水平的提高。根据分析结果得出相应的教学启示,以期对提高B级学生的英语综合能力提供有价值的借鉴和参考。     

Learning in experiments: Dynamic interaction of policy variables designed to deter tax evasion

While neoclassical economic theory sheds insight into the way that audit rates and penalty rates interact when individuals decide to declare income for taxation, it predicts far lower levels of compliance than observed levels of compliance. This paper analyses experimental responses to explore a dynamic interaction between audit and penalty rates as individuals learn how to comply with taxation. It compares the responses of subjects in experiments with responses that are predicted when individuals rely on an adaptive learning process (that offers information feedback about decision payoffs). This comparison suggests that learning is an important consideration when explaining differences between predicted and observed levels of tax compliance.     

基于信息反馈的实践教学质量评价体系构建

提出了一种基于信息反馈的实践教学质量评价体系,依据实践教学过程中的各种信息反馈,动态地调整对于实践教学各指标的评价。评价的结果反映了实践教学各个阶段、各个环节的表现,从而提升教师参与实践教学的积极性与有效性,促进实践教学管理水平的提高。     

美国中小学服务学习课程模式的理论与实践

服务学习是美国中小学广泛实施的一种优秀的实践教学课程模式。不同于单纯的社会体验或学科教学,服务学习将课程学习、社会服务和体验反思三者有机地结合起来,不仅加强了学生课堂所学知识与现实世界的关联,同时也极大地丰富了中小学生的社会实践活动。其基本理念、特征、实践模式及实施经验对我国当前基础教育课程改革有着很高的借鉴价值。     

Does surprise enhance infant memory? Assessing the impact of the encoding context on subsequent object recognition

A discrepancy between what was predicted and what is observed has been linked to increased looking times, changes in brain electrical activity, and increased pupil dilation in infants. These processes associated with heightened attention and readiness to learn might enhance the encoding and memory consolidation of the surprising object, as suggested by both the infant and the adult literature. We therefore investigated whether the presence of surprise during the encoding context enhances subsequent encoding and recognition memory processes for the items that violated infants' expectations. Seventeen-month-olds viewed 20 familiar objects, half of which were labeled correctly, while the other half were mislabeled. Subsequently, infants were presented with a silent recognition memory test where the previously labeled objects appeared along with new images. Pupil dilation was measured, with more dilated pupils indicating (1) surprise during those labeling events where the item was mislabeled and (2) successful retrieval processes during the memory test. Infants responded with more pupil dilation to mislabeling compared to correct labeling. Importantly, despite the presence of a surprise response during mislabeling, infants only differentiated between the previously seen and unseen items at the memory test, offering no evidence that surprise had facilitated the encoding of the mislabeled items.     

学习迁移的系统理论:PPEE理论模型的建构和意义

学习是人类社会永恒的主题。为了更好地利用过去的经验，增强学习效果，个人和组织进行学习迁移具有重要意义。学习迁移是指将在以往通过学习或解决问题所获得的经验，用于解决之后所遇到的问题。本文力图建立系统的学习迁移理论，研究人、问题、环境的因素对于学习迁移的影响。本文采用理论推导的研究方法，在前人研究的基础上，提出相应的理论模型。本文运用系统的观点，构建了学习迁移的PPEE理论模型，认为人的因素、问题的因素、人和问题的交互作用、以及环境的调节作用，会共同影响学习迁移的效果。本理论主要从五个方面论述学习迁移的机制。首先，将人的因素按照不同层面划分为后端基本能力、中端心理过程、前端知识结构，讨论了其各自对学习迁移效果的直接影响。其次，将问题的因素划分为形象相似性和抽象相似性，并根据这两个维度构建了四种问题间关系类型，并探究了其与学习迁移效果之间的关系。第三，同时考虑人的因素和问题的因素，研究了二者之间的互动对于学习迁移效果的影响。第四，将环境因素划分为时间紧迫性和空间重要性两个维度，探讨了人和环境，以及问题和环境之间的互动对学习迁移效果的影响。第五，基于系统整体的方面，研究人、问题、环境三者之间的互动对学习迁移效果的交互影响。本文还认为，该模型不仅适用于个人，也同样适用于团队、组织、地区、甚至国家和社会等各个层面，影响各个层面主体的学习迁移。本文最后总结了研究的理论和实践贡献，并提出了未来研究课题。本文对丰富学习理论、特别是学习迁移理论，以及推动教育和管理实践的发展进步具有重要的意义。     

Soft Classification Trees

An extension of soft classification trees to multinomial outcomes is presented. Estimates of the method's predictive accuracy, as well as average tree size and tree depths, are systematically compared to those of the conventional Classification and Regression Tree (CART) approach by the means of simulations. A similar comparison is performed on real datasets. Results point to an advantage in favor of the soft tree.