大学外语教学改革的价值取向:从对“法”的拘囿转向对人的关注

外语教学长期拘囿于寻找“最佳的教学方法”。我国大学外语教学领域存在着追赶国外“最新教学法”的现象，将外语学习和教学简单化、模式化，忽视了学习者和教师的认知与情感。Kumaravadivelu首次提出了“后方法”的概念，提出特定性、实践性和可能性三个基本参数的外语教学原则和十大宏观教学策略。教师在教学策略的指导下，结合三个基本参数转化成特定的教学措施，创造出各种不同的微观策略，应用于教学实践。“后方法”教学理念主张动态开放的外语教学，一方面关注对外语学习者的研究，倡导“以学生为中心”；另一方面注重教师对教学理论的反思与创设。教学法研究的重点从“法”的拘囿转向人的自主和自由。教师和学生的主体作用越来越受到关注。     

Joint modelling of longitudinal biomarker and gap time between recurrent events: copula-based dependence

In this paper, we will extend the joint model of longitudinal biomarker and recurrent event via copula function for accounting the dependence between the two processes. The general idea of joining separate processes by allowing model-specific random effect may come from different families distribution. It is a main advantage of the proposed method that a copula construction does not constrain the choice of marginal distributions of random effects. A maximum likelihood estimation with importance sampling technique as a simple and easy understanding method is employed to model inference. To evaluate and verify the validation of the proposed joint model, a bootstrapping method as a model-based resampling is developed. Our proposed joint model is also applied to pemphigus disease data for assessing the effect of biomarker trajectory on risk of recurrence.     

Disaster Management from a POM Perspective: Mapping a New Domain

We have reviewed disaster management research papers published in major operations management, management science, operations research, supply chain management and transportation/logistics journals. In reviewing these studies, our objective is to assess and present the macro level “architectural blue print” of disaster management research with the hope that it will attract new researchers and motivate established researchers to contribute to this important field. The secondary objective is to bring this disaster research to the attention of disaster administrators so that disasters are managed more efficiently and more effectively. We have mapped the disaster management research on the following five attributes of a disaster: (1) Disaster Management Function (decision‐making process, prevention and mitigation, evacuation, humanitarian logistics, casualty management, and recovery and restoration), (2) Time of Disaster (before, during and after), (3) Type of Disaster (accidents, earthquakes, floods, hurricanes, landslides, terrorism and wildfires etc.), (4) Data Type (Field and Archival data, Real data and Hypothetical data), and (5) Data Analysis Technique (bidding models, decision analysis, expert systems, fuzzy system analysis, game theory, heuristics, mathematical programming, network flow models, queueing theory, simulation and statistical analysis). We have done cross tabulations of data among these five parameters to gain greater insights into disaster research. Recommendations for future research are provided.     

Analysis of clustered spatially correlated binary data using autologistic model and Bayesian method with an application to dental caries of 3–5-year-old children

The autologistic model, first introduced by Besag, is a popular tool for analyzing binary data in spatial lattices. However, no investigation was found to consider modeling of binary data clustered in uncorrelated lattices. Owing to spatial dependency of responses, the exact likelihood estimation of parameters is not possible. For circumventing this difficulty, many studies have been designed to approximate the likelihood and the related partition function of the model. So, the traditional and Bayesian estimation methods based on the likelihood function are often time-consuming and require heavy computations and recursive techniques. Some investigators have introduced and implemented data augmentation and latent variable model to reduce computational complications in parameter estimation. In this work, the spatially correlated binary data distributed in uncorrelated lattices were modeled using autologistic regression, a Bayesian inference was developed with contribution of data augmentation and the proposed models were applied to caries experiences of deciduous dents.     

Anxiety and fear of childbirth as predictors of postnatal depression in nulliparous women

Background and purposePerinatal mental health problems have been studied in more than 90% of high income countries but this information is available only for 10% of low and middle income countries. A study on the relationship between anxiety during pregnancy and postpartum depression has not been performed in Iran. This prospective study aimed to investigate whether anxiety and fear of childbirth during pregnancy is an independent predictor of postpartum depressive symptoms.MethodsIn this prospective longitudinal study participants were 160 women with a gestational age of 28–30 weeks from 10 prenatal care clinics in Qom, Iran. Subjects were interviewed and the State-Trait Anxiety Inventory (STAI) and Childbirth Attitudes Questionnaire (CAQ) were completed at 28 and 38 weeks of gestation. They were followed up 45 days and 3 months after childbirth. Postpartum depression was defined as a score ≥ 13 on the Edinburgh Postpartum Depression Scale (EPDS).ResultsAntenatal state anxiety (odds ratio [OR] = 3.2; P = 0.002 and OR = 2.91; P = 0.007 at 28 and 38 weeks of gestation, respectively) and trait anxiety (OR = 3.33; P = 0.001 and OR = 3.30; P = 0.003 at 28 and 38 weeks of gestation, respectively) increased the risk of postpartum depression 45 days after birth (P < 0.05). Likewise, the presence of antenatal state and trait anxiety at 28 and 38 weeks of gestation significantly increased the risk of postpartum depression during the first three months after childbirth (P < 0.05). On the contrary, prenatal fear of childbirth was not a significant predictor of postpartum depression symptoms (P > 0.05).ConclusionsThe findings from this study suggest that antenatal state and trait anxiety, assessed by interview, is an important predictor of postpartum depression. Therefore, it should be routinely screened in order to develop specific preventive interventions.     

Phase I monitoring of social network with baseline periods using poisson regression

Social network analysis is an important analytic tool to forecast social trends by modeling and monitoring the interactions between network members. This paper proposes an extension of a statistical process control method to monitor social networks by determining the baseline periods when the reference network set is collected. We consider probability density profile (PDP) to identify baseline periods using Poisson regression to model the communications between members. Also, Hotelling T² and likelihood ratio test (LRT) statistics are developed to monitor the network in Phase I. The results based on signal probability indicate a satisfactory performance for the proposed method.     

A Contrast of HRM and TQM Approaches to Performance Management: Some Evidence*

Using a survey, this paper provides information about the current state of performance management (appraisal) from a sample of UK‐based EFQM‐affiliated organizations. It particularly focuses on several critical issues of performance management in the context of TQM including: the effectiveness of TQM programmes; the rationale for performance management; degree of internal consistency between TQM assumptions and performance management systems; and the relationship among performance management, effectiveness of TQM programmes, employee satisfaction and overall organization performance. Although the fundamental precepts advocated by founders of TQM appear to be in conflict with performance management practices, however, the article argues that, rather than being contradictory, both can add value to the operations of the other in the interest of the organization as a whole. More precisely, the paper explains how a successful TQM strategy requires a rethinking and changing the organization's performance management system, otherwise it is highly likely to result in a disaster. To conclude, the survey evidence is used, combined with previous literature, to discuss the implications of these results for designing a contextually appropriate performance management for TQM and in the interest of the future research on TQM and HRM.     

NGO Behavior and Development Discourse: Cases From Western India

This paper examines the effects of shifts in development discourse on the behavior of nongovernmental organizations (NGOs). Drawing upon detailed case histories of two well-established NGOs in western India, it is demonstrated that (1) the case NGOs have been profoundly influenced by discourses prevailing during their initial, formative stages; (2) NGO behavior is subject to changes in global development discourses that are transmitted to them via a range of mechanisms including consultants, conditions of funding, and reporting requirements; and (3) these NGOs have been able to challenge and adapt certain discourses to suit their own needs and circumstances, sometimes even sparking wider structural change.     

A statistical approach to social network monitoring

Social network monitoring consists of monitoring changes in networks with the aim of detecting significant ones and attempting to identify assignable cause(s) contributing to the occurrence of a change. This paper proposes a method that helps to overcome some of the weaknesses of the existing methods. A Poisson regression model for the probability of the number of communications between network members as a function of vertex attributes is constructed. Multivariate exponentially weighted moving average (MEWMA) and multivariate cumulative sum (MCUSUM) control charts are used to monitor the network formation process. The results indicate more efficient performance for the MEWMA chart in identifying significant changes.