表里相形虚实相生--诗词审美信息略论

诗词的审美是由表层的直接信息和深层的潜在信息组成，潜在信息对诗词的审美具有重要作用。诗词的直接信息和潜在信息是表里相成、虚实相生的。文章具体分析了在诗词的情与景、事与理、形与神、虚与实中，以一方为直接信息，另一方即为潜在信息。信息的生成规律对诗词鉴赏具有重要意义，它能使我们挖掘出诗词的全部审美信息，把握诗词美的真谛。     

Mapping students' interest in a new domain: Connecting prior knowledge,interest, and self-efficacy with interesting tasks and a lasting desire to reengage

How students experience educational environments and the interconnections between their readiness, task experiences and their long-term desire to reengage with course content are critical questions for educators. Research postgraduate students (n = 310) at a research-intensive university in Hong Kong, engaging in a 24-h introductory teaching course, participated in this study. Learner readiness for the course was assessed as prior Domain interest, self-efficacy, and knowledge. Subsequently, students completed four formative assessments, reported their on-task interest in seven strategically chosen tasks and end-of-course Course and Domain interest. Longitudinal-SEM tested interconnections between readiness components, Task, Course and Domain interest. Initial self-efficacy beliefs for teaching predicted early Task interest, while Domain interest was a predictor of Task interest in explicitly practical task experiences. Strong interconnections between Task interest across the study were evident. Individual written and social discussion tasks presented strong contributions to future Course/Domain interest. Implications for theory and practice are discussed.     

推荐系统中的隐语义模型研究?

隐语义模型是一种有效的隐含语义分析技术,其核心思想是通过潜在特征联系用户和物品。本文从理论方法的角度,详细介绍了隐语义模型的工作原理、模型表示、学习方法和评价指标等,并通过具体实验分别讨论几种典型隐语义模型算法的推荐效果,以全面认识和理解该模型在推荐系统中的应用特点。     

彼得·圣吉的五项修炼对挖掘馆员个体隐性知识的启示

当知识管理引入了新的组织管理模式,扩大了默认知识的共享范围,使得组织成为人们获得知识的重要来源,成为一个学习和创新的系统。图书馆要运用“系统思考”的理念整体地、动态地挖掘馆员个体的隐性知识,实现知识的交流与共享。     

Student Engagement and School Burnout in Finnish Lower-Secondary Schools: Latent Profile Analysis

Self-ratings of behavioural engagement, cognitive engagement and school burnout were used in person-centred analyses to identify latent profiles among 2,485 Finnish lower-secondary school students. Three profiles were identified: high-engagement/low-burnout (40.6% of the sample), average-engagement/average-burnout (53.9%), and low-engagement/high-burnout (5.5%). Another sample of lower-secondary school students was used to validate the 3 profiles. The factors most strongly associated with the high-engagement/low-burnout profile of lower-secondary school students’ were high levels of support from teachers and family, good academic performance, and lack of truancy. The study indicated that teacher and family support and students’ academic achievement are pivotal in understanding student engagement and school burnout.     

Bayesian Regularized Multivariate Generalized Latent Variable Models

We consider a multivariate generalized latent variable model to investigate the effects of observable and latent explanatory variables on multiple responses of interest. Various types of correlated responses, such as continuous, count, ordinal, and nominal variables, are considered in the regression. A generalized confirmatory factor analysis model that is capable of managing mixed-type data is proposed to characterize latent variables via correlated observed indicators. In addressing the complicated structure of the proposed model, we introduce continuous underlying measurements to provide a unified model framework for mixed-type data. We develop a multivariate version of the Bayesian adaptive least absolute shrinkage and selection operator procedure, which is implemented with a Markov chain Monte Carlo (MCMC) algorithm in a full Bayesian context, to simultaneously conduct estimation and model selection. The empirical performance of the proposed methodology is demonstrated through a simulation study. An application of the proposed method to a study of adolescent substance abuse based on the National Longitudinal Survey of Youth is presented.     

Number of Factors in Growth Curve Modeling

Basic growth curve models parameterize the mean and covariance structure of a set of repeated measures by latent factors that represent the polynomial influences of time. In practice it may be hard to choose the number of factors, i.e., the order of the polynomial. Simple calculations are proposed to estimate this order.     

Time-Varying Effect Sizes for Quadratic Growth Models in Multilevel and Latent Growth Modeling

Multilevel and latent growth modeling analysis (GMA) is often used to compare independent groups in linear random slopes of outcomes over time, particularly in randomized controlled trials. The unstandardized coefficient for the effect of group on the slope from a linear GMA can be transformed into a model-estimated effect size for the group difference at the end of a study. Because effect sizes vary nonlinearly in quadratic GMA, the effect size at the end of a study using quadratic GMA cannot be derived from a single coefficient, and cannot be used to estimate effect sizes at intermediate time points with backward extrapolation. This article formulates equations and associated input commands in Mplus for time-varying effect sizes for quadratic GMA. Illustrative analyses that produced these time-varying effect sizes were presented, and a Monte Carlo study found that bias in the effect sizes and their confidence intervals was ignorable.     

时代文化的压力与文体规范的变异--"潜在写作"文体特征的一种探讨

文体选择不仅是作家艺术特长和喜好的一种表现,也是由时代文化对作家施压所导致。“潜在写作”在文体上显现的边缘化特征,就是作家在20世纪50至70年代政治文化压力下坚持批判现实和个性化表达努力在文学形式上的一种体现,它呼唤一种更加开放的文学史视野对其进行历史的归纳评价。     

Latent Markov Factor Analysis for Exploring Measurement Model Changes in Time-Intensive Longitudinal Studies

When time-intensive longitudinal data are used to study daily-life dynamics of psychological constructs (e.g., well-being) within persons over time (e.g., by means of experience sampling methodology), the measurement model (MM)—indicating which constructs are measured by which items—can be affected by time- or situation-specific artifacts (e.g., response styles and altered item interpretation). If not captured, these changes might lead to invalid inferences about the constructs. Existing methodology can only test for a priori hypotheses on MM changes, which are often absent or incomplete. Therefore, we present the exploratory method “latent Markov factor analysis” (LMFA), wherein a latent Markov chain captures MM changes by clustering observations per subject into a few states. Specifically, each state gathers validly comparable observations, and state-specific factor analyses reveal what the MMs look like. LMFA performs well in recovering parameters under a wide range of simulated conditions, and its empirical value is illustrated with an example.