从技术化到智慧化:我国教师远程培训的研究进展与发展走向

远程培训作为教师培训的主要途径之一,具有规模大、成本低、跨时空等特点,对提高教师培训质量、促进教师专业发展具有重要作用。通过对我国教师远程培训研究文献进行可视化分析,绘制出热点知识图谱,从中可以发现:教师远程培训研究发文量在一段时间内呈上升趋势;研究热点主要集中在现状与策略、需求与服务、课程与模式、技术与平台等领域;研究方向逐渐从重视经验介绍、技术讲解、平台展示的“技术化”价值取向,转向重视培训教师的素养和参训教师的需求、落实教师专业发展措施的“智慧化”价值取向。新时代下,我国教师远程培训的发展势如破竹,在教师教育振兴进程中发挥着越来越重要的作用。相关研究显示,教师远程培训在对象拓展、课程设置、模式优化、评价运用、平台开发等方面也呈现出“智慧化”趋向。向“智慧化”教师远程培训的广度、深度和效度发力,需要着重做好几个方面的工作:开展培训对象的系统化探索、加强培训课程的立体化开发、重视培训模式的混合化构建、注重培训评价的大数据分析以及加强培训平台的智能化研发。     

The Campus Alberta Applied Psychology Counselling Initiative: Web-Based Delivery of a Graduate Professional Training Program

SUMMARY

The Campus Alberta Applied Psychology: Counselling Initiative is a collaboration between three universities in western Canada to offer a predominantly Web-based Master of Counselling program. This paper describes the basic structure of the program, the Web-based delivery system, communication tools employed, and the philosophical, pedagogical, programmatic, and administrative principles and concepts foundation to the development and implementation. Development of curricular content and learning processes have been driven by explicit examination of learner needs, a program-level competency matrix reflective of professional practice standards in the discipline of psychology, and deliberate and continuous formative evaluation and learner engagement in the process.     

Usability Study of a Distance Continuing Education Website for Human Service Professionals

ABSTRACT

A usability test of a distance continuing education website for human service professionals is discussed. The website's purpose was to help meet the continuing education needs of human service professionals in a largely rural Midwestern state. The purpose of the usability evaluation was to assess the website's ease of use, efficiency, and user satisfaction in a representative sample of human service professionals. Users completed eleven basic search tasks with the site and report their impressions of the information presented. In addition, performance data including success, time on task, and efficiency were gathered. Implications of user satisfaction and performance data highlight the importance of stakeholder's involvement in the early stages of website design.     

Framing distance: local vs. non-local news in Hong Kong press

Geographical location as a “natural” attribute of news has always been a source of news manipulation. This research investigates how different community newspapers select, interpret, and package events originating in places of varying distances. Contextualized in Hong Kong, three media frames (authority, conflict, and attribution) are closely examined and theoretically connected to three types of newspapers (mass appeal, elite, and pro-establishment) across four locales (local, mainland China, Asian, and international). To test the hypotheses, a large-scale content analysis of 14 daily newspapers in Hong Kong was conducted. On the whole, findings supported the three hypotheses, showing that local news was systematically differentiated from non-local news in terms of the frequency of political authorities cited, presence of diversified opinions, and the likelihood of either individuals or institutions being blamed for social wrongs. Considerable variations were detected across different types of newspapers as well. Theoretical and social implications of the empirical findings are discussed.     

Opportunities of the minimum Anderson–Darling estimator as a variant of the maximum likelihood method

We reveal that the minimum Anderson–Darling (MAD) estimator is a variant of the maximum likelihood method. Furthermore, it is shown that the MAD estimator offers excellent opportunities for parameter estimation if there is no explicit formulation for the distribution model. The computation time for the MAD estimator with approximated cumulative distribution function is much shorter than that of the classical maximum likelihood method with approximated probability density function. Additionally, we research the performance of the MAD estimator for the generalized Pareto distribution and demonstrate a further advantage of the MAD estimator with an issue of seismic hazard analysis.     

Outlier detection in high-dimensional regression model

An outlier is defined as an observation that is significantly different from the others in its dataset. In high-dimensional regression analysis, datasets often contain a portion of outliers. It is important to identify and eliminate the outliers for fitting a model to a dataset. In this paper, a novel outlier detection method is proposed for high-dimensional regression problems. The leave-one-out idea is utilized to construct a novel outlier detection measure based on distance correlation, and then an outlier detection procedure is proposed. The proposed method enjoys several advantages. First, the outlier detection measure can be simply calculated, and the detection procedure works efficiently even for high-dimensional regression data. Moreover, it can deal with a general regression, which does not require specification of a linear regression model. Finally, simulation studies show that the proposed method behaves well for detecting outliers in high-dimensional regression model and performs better than some other competing methods.     

Computation of finite population parameters and approximate probability values for multi-response randomized block permutation procedures (mrbp)

An algorithm is presented for computing the finite population parameters and the approximate probability values associated with a recently-developed class of statistical inference techniques termed multi-response randomized block permutation procedures (MRBP).     

ROBUSTNESS OF PROCEDURES FOR THE BEHRENS-FISHER PROBLEMS: EXTENSION TO BIVARIATE NORMAL MIXTURES

In the applied sciences, it is often important to be able to compare the mean values of two populations. However, testing a hypothesis can be complex, if the two populations are heteroscedastic and exhibit non-normality in the data. This paper reviews currently available strategies for the multivariate Behrens-Fisher problem. It then carries out Monte Carlo comparisons of selected procedures to assess their robustness when applied to data from normal mixture distributions. The overall conclusion is that Johansen's procedure appears to work best for small sample data both in terms of empirical power and significance level. Johansen's procedure works reasonably well even with mixture data. The simulation also provides researchers with specific guidelines to follow at the early designing and planning stages of the investigation.     

An approach to measure distance between compositional diet estimates containing essential zeros

For many applications involving compositional data, it is necessary to establish a valid measure of distance, yet when essential zeros are present traditional distance measures are problematic. In quantitative fatty acid signature analysis (QFASA), compositional diet estimates are produced that often contain many zeros. In order to test for a difference in diet between two populations of predators using the QFASA diet estimates, a legitimate measure of distance for use in the test statistic is necessary. Since ecologists using QFASA must first select the potential species of prey in the predator's diet, the chosen measure of distance should be such that the distance between samples does not decrease as the number of species considered increases, a property known in general as subcompositional coherence. In this paper we compare three measures of distance for compositional data capable of handling zeros, but not satisfying some of the well-accepted principles of compositional data analysis. For compositional diet estimates, the most relevant of these is the property of subcompositionally coherence and we show that this property may be approximately satisfied. Based on the results of a simulation study and an application to real-life QFASA diet estimates of grey seals, we recommend the chi-square measure of distance.     

Robust Methods of Building Regression Models—An Application to thè Housing Sector

This article studies robustification strategies for the linear model in the presence of outliers. The advantages of an internal analysis of the robustness of least squares for a given sample are pointed out. The application of this methodology is illustrated by building an explicit model of the determinants of rental housing values in the Madrid Metropolitan Area.