Selection procedures in sports: Improving predictions of athletes’ future performance

The selection of athletes has been a central topic in sports sciences for decades. Yet, little consideration has been given to the theoretical underpinnings and predictive validity of the procedures. In this paper, we evaluate current selection procedures in sports given what we know from the selection psychology literature. We contrast the popular clinical method (predictions based on overall impressions of experts) with the actuarial approach (predictions based on pre-defined decision rules), and we discuss why the latter approach often leads to superior performance predictions. Furthermore, we discuss the “signs” and the “samples” approaches. Taking the prevailing signs approach, athletes’ technical-, tactical-, physical-, and psychological skills are often assessed separately in controlled settings. However, for predicting later sport performance, taking samples of athletes’ behaviours in their sports environment may result in more valid assessments. We discuss the possible advantages and implications of making selection procedures in sports more actuarial and sample-based.     

浅谈启发式教学在C语言程序设计教学中的应用

在程序设计教学中 ,数据类型、语句结构是基础 ,但分析问题、解决问题 ,以至独立设计出符合用户要求的程序是最终目的。本文以C语言教学为例 ,着重谈谈启发式教学对培养学生程序设计能力的重要性及具体应用。     

柬埔寨中学英语教材（EFC）改革刍议——以EFC1为例

在教学过程当中,教材是最重要因素之一。EFC1是柬埔寨目前正在使用的公办中学英语教材的第一本。本文就该教材讨论了其特点以及存在的问题,并就如何完善EFC提出了一些建议。     

欧美高等职业教育定位--比较的视角

欧洲国家的高等职业教育是分立竞争型的高等教育系统,美国高职教育是弥散选择型的高等职业教育定位模式。欧洲和高等职业教育定位的差异主要源于不同的知识价值观、学分制的推广以及竞争体制的差异。     

Latent Class Trees with the Three-Step Approach

Latent class (LC) analysis is widely used in the social and behavioral sciences to find meaningful clusters based on a set of categorical variables. To deal with the common problem that a standard LC analysis may yield a large number classes and thus a solution that is difficult to interpret, recently an alternative approach has been proposed, called Latent Class Tree (LCT) analysis. It involves starting with a solution with a small number of “basic” classes, which may subsequently be split into subclasses at the next stages of an analysis. However, in most LC analysis applications, we not only wish to identify the relevant classes, but also want to see how they relate to external variables (covariates or distal outcomes). For this purpose, researchers nowadays prefer using the bias-adjusted three-step method. Here, we show how this bias-adjusted three-step procedure can be applied in the context of LCT modeling. More specifically, an R-package is presented that performs a three-step LCT analysis: it builds a LCT and allows checking how splits are related to the relevant external variables. The new tool is illustrated using a cross-sectional application with multiple indicators on social capital and demographics as external variables and with a longitudinal application with a mood variable measured multiple times during the day and personality traits as external variables.