Child Schooling in India: A Multilevel Approach

It is been widely established that child schooling has far reaching influence on shaping their subsequent living, in particular, and social and economic development of the society, in general. Although the Constitution of India provides free and compulsory education for all children until they reach 14 years, the schooling of children – enrollment and completion of primary school – still remains far below than the expectations. Using data from National Family Health Survey [NFHS], we attempted to understand primary school enrollment and completion in India. Results reveal that household characteristics play a significant role towards child schooling. The study suggests the importance of adult educational programs and opening of new schools, particularly all-girls schools, in needed areas. This revised version was published online in July 2006 with corrections to the Cover Date.     

“逆向思维”巧解题

正向思维固然是解决问题的正常途径 ,但对于一些问题却一筹莫展 ,至少是相当麻烦。如果使用逆向思维方法 ,可以使问题迎刃而解。本文通过逆向思维方法解决一些数学问题 ,显示出逆向思维不愧为重要的数学思维方法。     

Children's Behavioral Adjustment in Pre-Primary Schools in Tanzania: A Multilevel Approach

Research Findings: The present study concerns children's behavioral adjustment in the context of pre-primary schools in Tanzania. Twenty teachers and 320 children from 20 pre-primary schools participated in the study. Teacher–child relationships, children's behavioral adjustment, and teachers' cultural beliefs were reported by teachers; classroom emotional support was measured through classroom observation. The multilevel findings revealed that high-quality teacher–child relationships and high-quality teacher sensitivity were related to children's prosocial behavioral adjustment. In contrast, observed low-quality teacher–child relationships and low-quality teacher sensitivity were found to be related to children's aggression and anxiety. In addition, teachers' cultural beliefs, concerning play in particular, were found to be related to children's anxiety. The findings support the ecological theory regarding the importance of child characteristics and classroom context in shaping a child's behavioral adjustment in schools. Practice or Policy: The results have implications for pre-primary school teachers in Tanzania, to consider their relationships with children and their sensitivity to children as important aspects for children's behavioral adjustment in schools. They also inform policymakers about the role of pre-primary school teachers in the country.     

Evaluating Model Fit and Structural Coefficient Bias: A Bayesian Approach to Multilevel Bifactor Model Misspecification

Appropriate model specification is fundamental to unbiased parameter estimates and accurate model interpretations in structural equation modeling. Thus detecting potential model misspecification has drawn the attention of many researchers. This simulation study evaluates the efficacy of the Bayesian approach (the posterior predictive checking, or PPC procedure) under multilevel bifactor model misspecification (i.e., ignoring a specific factor at the within level). The impact of model misspecification on structural coefficients was also examined in terms of bias and power. Results showed that the PPC procedure performed better in detecting multilevel bifactor model misspecification, when the misspecification became more severe and sample size was larger. Structural coefficients were increasingly negatively biased at the within level, as model misspecification became more severe. Model misspecification at the within level affected the between-level structural coefficient estimates more when data dependency was lower and the number of clusters was smaller. Implications for researchers are discussed.     

The Influence of Institutional Retention Climates on Student Persistence to Degree Completion: A Multilevel Approach

Using multi-institutional data from the Cooperative Institutional Research Program’s (CIRP) annual survey of entering freshmen, the Higher Education Research Institute’s (HERI) Faculty Surveys, campus registrars’ offices, and IPEDS data, we evaluated the extent to which peer institutional retention climates and faculty perceived campus climates influenced individual 6-year retention rates. We used hierarchical generalized linear models (HGLM) to examine the extent to which institutional climate (as measured by peer institutional retention climate and faculty perceived climate), net of students’ ability, expectations, and family socioeconomic status, influence students’ persistence behavior. We found that institutional retention climate, as defined by a student body’s aggregated report of withdrawal intentions, did independently determine whether a student would persist or not. While past research has overlooked peer and faculty climates, the results from this work call for the attention of peer institutional retention climates.

A Bayesian Approach for Estimating Multilevel Latent Contextual Models

In many applications of multilevel modeling, group-level (L2) variables for assessing group-level effects are generated by aggregating variables from a lower level (L1). However, the observed group mean might not be a reliable measure of the unobserved true group mean. In this article, we propose a Bayesian approach for estimating a multilevel latent contextual model that corrects for measurement error and sampling error (i.e., sampling only a small number of L1 units from a L2 unit) when estimating group-level effects of aggregated L1 variables. Two simulation studies were conducted to compare the Bayesian approach with the maximum likelihood approach implemented in Mplus. The Bayesian approach showed fewer estimation problems (e.g., inadmissible solutions) and more accurate estimates of the group-level effect than the maximum likelihood approach under problematic conditions (i.e., small number of groups, predictor variable with a small intraclass correlation). An application from educational psychology is used to illustrate the different estimation approaches.     

A Bayesian Approach to Multilevel Structural Equation Modeling With Continuous and Dichotomous Outcomes

Multilevel Structural equation models are most often estimated from a frequentist framework via maximum likelihood. However, as shown in this article, frequentist results are not always accurate. Alternatively, one can apply a Bayesian approach using Markov chain Monte Carlo estimation methods. This simulation study compared estimation quality using Bayesian and frequentist approaches in the context of a multilevel latent covariate model. Continuous and dichotomous variables were examined because it is not yet known how different types of outcomes—most notably categorical—affect parameter recovery in this modeling context. Within the Bayesian estimation framework, the impact of diffuse, weakly informative, and informative prior distributions were compared. Findings indicated that Bayesian estimation may be used to overcome convergence problems and improve parameter estimate bias. Results highlight the differences in estimation quality between dichotomous and continuous variable models and the importance of prior distribution choice for cluster-level random effects.     

Smart Training and Development: A Learner‐Guided Approach

There is great support for the research that says that how one uses knowledge is linked to how and where that knowledge is acquired—especially, knowledge that is connected directly to performance. This how and where approach is rooted in time‐tested and research‐supported learning strategies. Key emphasis is placed on competency identification, maximum use of the work environment for learning, self‐directed learning activities along with a contracting structure that allows self‐pacing, mastery, individualization, and various feedback options. This article describes how to combine these learning strategies so that training and development (T&D) costs, time to train, and employee performance and retention are improved. This learning model is called learner‐guided training and development—a smart, highly leveraged design model.     

智慧学习环境中的学习情景识别——让学习环境有效服务学习者

学习情景识别是个性化学习资源推送、学习伙伴联接以及学习活动建议的前提。本文首先分析了学习情景识别所需的六种要素,即学习者模型、学习目标空间、学习活动模型、领域知识模型、时空模型与情景模型,然后提出了一个学习情景识别的概念模型,包含了信息采集、动态建模和情景推理三个模块,并在此基础上讨论了学习者建模、学习活动建模、情景推理等方面的研究进展与关键技术。     

Integrating Response to Intervention (RTI) with neuropsychology: A scientific approach to reading

This article integrates the fundamental components of both Response to Intervention (RTI) and cognitive neuropsychology when identifying reading disorders in children. Both proponents of RTI and cognitive neuropsychology agree the discrepancy model is not a reliable or valid method to identify learning disorders in school. In addition, both proponents of RTI and cognitive neuropsychology agree that earlier intervention and the use of evidence‐based intervention techniques must permeate the thinking behind any educational reform. Lastly, both proponents of RTI and cognitive neuropsychology concur with the National Reading Panel's (2000) five core components of the reading process. Given the similarities between RTI and neuropsychological models of reading, a more integrative assessment model will be introduced to better diagnose and remediate subtypes of reading disorders in children. In summary, it is the author's belief that cognitive neuropsychology will emerge as the leading discipline in forging the inevitable alliance between science and education. © 2008 Wiley Periodicals, Inc.     

A Multilevel Modelling Approach to Investigating Factors Impacting Science Achievement for Secondary School Students: PISA Hong Kong Sample

This study utilized data from the 2006 Programme for International Student Assessment Hong Kong sample to investigate the factors that impact the science achievement of 15-year-old students. A multilevel model was used to examine the factors from both student and school perspectives. At the student level, the results indicated that male students, students from high socioeconomic status (SES) families, students with higher motivation and higher self-efficacy, and students whose parents highly value science are more likely to demonstrate achievement in science. At the school level, the results showed school science achievement differences can be explained by school enrolment size, school SES composition, and instruction time per week. Contrary to the negative influence of school size that was reported in previous studies, our findings suggested a positive relationship between school enrolment size and science achievement. This finding leads to an international discussion of school size.     

English Movie:A Smart Method to Teach English

English movies are the cultural carriers of English-speaking countries with veritable colloquial expressions, authentic environment and abundant cultural connotation. Teaching English through English movies can not only help students learn English language, but also acquire English culture. It’s also an effective way to improve students’ oral English and their accultural communication ability.     

English Movie:A Smart Method to Teach English

English movies are the cultural carriers of English-speaking countries with veritable colloquial expressions, authentic environment and abundant cultural connotation. Teaching English through English m...     

Lower Level Mediation Effect Analysis in Two-Level Studies: A Note on a Multilevel Structural Equation Modeling Approach

This article presents a didactic discussion of a multilevel covariance structure modeling approach to estimation of lowest level mediation effect indexes in two-level studies. The procedure is useful when addressing questions about relations among total and indirect effects between variables of interest while accounting for the hierarchical structure of analyzed data. The discussed method also permits interval estimation and hypothesis tests with respect to related quantities of relevance when evaluating mediated effects with clustered data, and is illustrated on a two-level data set.     

正向多级决策最优原理与方法-对动态规划的研究

本文提出了按决策物理顺序进行多阶段决策的理论与方法,这是与一直被应用的贝尔曼理论不同的.文中给出了应用示例并对有关问题做了讨论.     

RTI models as alternatives to traditional views of learning disabilities: Response to the commentaries

Two commentaries in this issue, one by Torgesen and one by Fuchs and Fuchs, are largely supportive of our views of the value of implementing response to intervention (RTI) models in schools. In contrast, Reynolds and Shaywitz's criticisms of RTI represent traditional and often nonempirical views of learning disabilities (LDs). The evidence base on RTI is not empty, and Reynolds and Shaywitz fail to provide research support for their views of either LDs or RTI.     

Teachers' perceptions and attitudes about Response to Intervention (RTI) in their schools: A qualitative analysis

As Response to Intervention (RTI) models are increasingly implemented in United States' schools, it is important to consider the perceptions of those directly involved. The current study assessed teachers' perceptions of RTI and utilized qualitative methodology and a computer-based text search program to explore teachers' perceptions of RTI. Constant comparison analysis yielded four emergent themes related to teacher perception of RTI: (a) overall understanding of RTI, (b) teachers' perceptions of barriers to RTI in their schools, (c) teachers' suggestions to improve RTI, and (d) teachers' suggestions for making paperwork more efficient. We conclude with implications for training and RTI implementation.     

Exploring Person Fit with an Approach Based on Multilevel Logistic Regression

The idea that test scores may not be valid representations of what students know, can do, and should learn next is well known. Person fit provides an important aspect of validity evidence. Person fit analyses at the individual student level are not typically conducted and person fit information is not communicated to educational stakeholders. In this study, we focus on a promising method for detecting and conveying person fit for large-scale educational assessments. This method uses multilevel logistic regression (MLR) to model the slopes of the person response functions, a potential source of person misfit for IRT models. We apply the method to a representative sample of students who took the writing section of the SAT (N = 19,341). The findings suggest that the MLR approach is useful for providing supplemental evidence of model–data fit in large-scale educational test settings. MLR can be useful for detecting general misfit at global and individual levels. However, as with other model–data fit indices, the MLR approach is limited in providing information regarding only some types of person misfit.     

Modeling Instructional Sensitivity Using a Longitudinal Multilevel Differential Item Functioning Approach

Students’ performance in assessments is commonly attributed to more or less effective teaching. This implies that students’ responses are significantly affected by instruction. However, the assumption that outcome measures indeed are instructionally sensitive is scarcely investigated empirically. In the present study, we propose a longitudinal multilevel‐differential item functioning (DIF) model to combine two existing yet independent approaches to evaluate items’ instructional sensitivity. The model permits for a more informative judgment of instructional sensitivity, allowing the distinction of global and differential sensitivity. Exemplarily, the model is applied to two empirical data sets, with classical indices (Pretest–Posttest Difference Index and posttest multilevel‐DIF) computed for comparison. Results suggest that the approach works well in the application to empirical data, and may provide important information to test developers.