Student-teacher relationship and classroom goal structure profiles: Promoting achievement and preventing externalizing and internalizing behaviors

This study investigates which profiles, defined based on students reports of student-teacher relationships (closeness; conflict) and classroom goal structures (mastery; performance), were optimal to support adjustment (externalizing and internalizing behaviors) and achievement in Math and Language. Elementary students (Grades 3–6, N = 703) and their teachers (N = 33) completed questionnaires in November 2011 and April 2012. Latent profile analysis identified four profiles, replicated across boys and girls: Average (44.46%), Mastery-Closeness (39.13%), Conflict (6.49%), and Approach-Closeness (9.92%). Students from the Conflict profile displayed more externalizing and internalizing behaviors than other students, whereas those from the Mastery-Closeness and Approach-Closeness profiles displayed the least difficulties. Students from the Approach-Closeness profile displayed the lowest achievement at the beginning of the year but the steepest increase over time.     

Predictive and adaptive Drift Analysis on Decomposed Healthcare Claims using ART based Topological Clustering

Fraud in healthcare services dissipates funds that are important for improving the quality of life of people, thus enhancing the interest in predictive fraud analysis. The predictive analysis of fraudulent activity can be done by looking for unusual patterns in healthcare claims. However, unusual patterns may also occur due to sudden changes, isolated events, or concept drifts that frequently happen in healthcare which should not be considered fraud. Furthermore, analyzing drifts also supports predicting future trends and behaviors. In this study, we propose a novel approach, Drift Analysis on Decomposed Healthcare Claims (DADHC), to analyze the hidden patterns that hinder the performance of fraud prediction and detection. Our proposed model decomposes the series of healthcare claims into regular and irregular patterns using Psuedo Additive Decomposition (PAD) integrated with Simple Moving Average (SMA) smoothing technique. Then ART (Adaptive Resonance Theory) based Topological Clustering (TC) is used to analyze unusual patterns and identify the actual fraudulent activities. Our proposed model also incorporates correntropy based vigilance testing in ART to enhance adaptivity. Empirical evaluation on CMS Part B claims shows that our proposed approach has significantly improved detection accuracy compared to existing models due to the drift analysis.     

基于两级传播理论的在线健康社区意见领袖交互行为研究——以百度“自闭症吧”为例

[目的/意义]通过对在线健康社区意见领袖交互行为的分析,探讨其识别、特征、影响和作用,为社区的良好运转和管理提供建议。[方法/过程]构建两级传播理论下的在线健康社区意见领袖模型,并以百度"自闭症吧"数据为例,结合社会网络分析方法研究社区整体网络特征、意见领袖个体网络特征和用户间交互网络。[结果/结论]百度"自闭症吧"整体网络中间密集,边缘稀疏,具有小世界效应;通过中心度分析及k核分析发现识别出的意见领袖各项个体指标数值均偏高,处在整体网络核心位置,有效连接信息传播两级,推动信息在来源层、意见领袖层和受众层间稳定流动,为其他用户提供帮助鼓励;验证了所建模型并根据研究发现提出两点针对性建议。     

A social recommendation method based on an adaptive neighbor selection mechanism

Recommender systems are techniques to make personalized recommendations of items to users. In e-commerce sites and online sharing communities, providing high quality recommendations is an important issue which can help the users to make effective decisions to select a set of items. Collaborative filtering is an important type of the recommender systems that produces user specific recommendations of the items based on the patterns of ratings or usage (e.g. purchases). However, the quality of predicted ratings and neighbor selection for the users are important problems in the recommender systems. Selecting suitable neighbors set for the users leads to improve the accuracy of ratings prediction in recommendation process. In this paper, a novel social recommendation method is proposed which is based on an adaptive neighbor selection mechanism. In the proposed method first of all, initial neighbors set of the users is calculated using clustering algorithm. In this step, the combination of historical ratings and social information between the users are used to form initial neighbors set for the users. Then, these neighbor sets are used to predict initial ratings of the unseen items. Moreover, the quality of the initial predicted ratings is evaluated using a reliability measure which is based on the historical ratings and social information between the users. Then, a confidence model is proposed to remove useless users from the initial neighbors of the users and form a new adapted neighbors set for the users. Finally, new ratings of the unseen items are predicted using the new adapted neighbors set of the users and the $top\_N$ interested items are recommended to the active user. Experimental results on three real-world datasets show that the proposed method significantly outperforms several state-of-the-art recommendation methods.     

Adaptive Functioning in Williams Syndrome: A Systematic Review

Literature on the level of adaptive functioning and relative strengths and weaknesses in functioning of individuals with Williams syndrome (WS) was reviewed. The electronic databases PsycINFO, PubMed, Expanded Academic, Web of Science, Scopus and ProQuest were searched electronically for relevant articles and dissertations using the search terms ‘Williams syndrome’ or ‘Williams-Beuren syndrome’ combined with ‘adaptive function*’, ‘adaptive behavio*’, ‘independ*’ and ‘autonomy’. Selection criteria included English language articles, theses and book chapters, participants with a diagnosis of Williams syndrome and inclusion of a standardised assessment of adaptive functioning. Twenty-two published articles and two PhD. dissertations (one subsequently published as a poster presentation) met the selection criteria for inclusion in the review. Fifteen investigated adaptive functioning in children and adolescents aged up to 19 years of age, five investigated adaptive functioning in adults and four included participants across a wider age range and included both children and adults. Along with identifying methodological issues, the review addressed the following areas: overall level of adaptive functioning in WS, domain strengths and weaknesses, evidence of heterogeneity, relationship to intellectual ability, changes with chronological age, relationship with maladaptive behaviour, gender differences and the potential influence of environmental factors which may be related to adaptive functioning in WS.     

基于小世界网络的知识网络结构演化模型研究

利用WS小世界网络模型构建知识网络的结构演化模型,通过度与度分布、平均最短路径长度及集聚系数等参数变化反映结构的时序变化,通过平均知识存量及标准差计算反映知识主体的行为变化,从而有助于明确知识网络结构演化微观动力机制及其结构演化过程细节.模型中知识网络主体的价值优化预期是网络结构演化动力,网络结构演化具体过程则涉及知识网络价值计算、知识节点耦合成本计算以及关系边权重计算三方面.     

对我国建立健全侵害债权行为责任制度的思考

在我国确立侵害债权行为责任制度需注意以下几个问题：在我国民法体系中的位置;适用范围;债务人能否成为侵害债权行为的主体;主观方面的要件;承担责任的举证方式;非法侵害行为的种类;损害赔偿责任的具体承担。     

少数民族大学生课堂表现差异与考试成绩关系的研究

本文主要通过调查问卷对少数民族大学生的学习取向、口头参与、争强好胜、寻求帮助、自我表露、自信,以及和同学交往七个方面的课堂表现差异与大学英语考试成绩之间的关系进行分析研究,发现学生的课堂表现差异与考试成绩之间存在着密切的关系.在今后的大学英语教学中,教师要更加注重以学生为中心,充分调动学生的课堂学习积极性,把学生培养成为自律性学习者.     

当代大学生网络集体行为意向驱动机理研究——基于技术接受模型（TAM）的实证分析

厘清大学生群体网络集体行为意向的驱动机理能够为信息时代大学生行为引导与思想政治教育提供理论依据。文章基于扩展的技术接受模型，以在校大学生为调查对象，利用问卷调查与结构方程模型研究当代大学生网络集体行为意向的形成机理。研究发现:道德义务、身份认同、集体情绪、信息感知均能够直接或者间接影响大学生网络集体行为意向，其中道德义务可以通过身份认同、集体情绪两条途径，影响大学生网络集体行为意向。研究结果进一步探索了网络集体行为演变机制，能够为政府、高等学校合理预判舆情、应对大学生网络治理风险提供参考与借鉴。     

颠覆数字图书馆的大趋势

在引入破坏性技术概念后,描述了可能颠覆当前数字图书馆模式的技术、需求与机制,包括数字学术出版、科学数据、语义化出版等新技术,弱信息需求下模糊探索、战略性阅读下计算分析和研究性合作式学习下动态信息交互等新需求,以及图书馆同质化、业务外包与众包、价值证明压力等运营机制新挑战,建议采取驾驭“破坏与颠覆”的态度、观念,预测可能的战略发展方向。图9。表1。参考文献46。