Higher Education Publication and Institutional and National Diversity

Educational scholarship is used by practitioners, policy makers, and scholars to shape educational practices. Since education takes place across the globe and incorporates students from a wide variety of backgrounds, educational scholarship should incorporate diverse perspectives. This study examines how institutionally and internationally diverse five leading journals of higher education are. Twelve years of publications are examined to determine the level of diversity among top higher education journals and compare diversity among these publications over time. Maps displaying the distribution of authors across the world are provided to illustrate the findings that higher education publication in the leading journals tends to come mostly from the US and to show the differences in distribution between leading journals

Scholarly Publishing at a Crossroads: Scholarly perspectives on Open Access

The cost of access to scholarly research creates inequity for readers with varying resources. Open access publishing is an avenue to address this inequity. This research employed a survey of scholars to discover what they know and think about open access. The survey elicited both faculty and doctoral student perspectives. Data were analyzed according to rank and discipline. Although the majority of scholars across disciplines agreed that their work should be freely available to all readers, there were significant differences between disciplines regarding whether scholars had distributed their publications through open access. The survey instrument was examined through Exploratory Factor Analysis

Promoting Institutional Repositories via Visualizations - A Changepoint Study

This paper examines whether implementing visualizations on an institutional repository webpage increases traffic on the site. Two methods for creating visualizations to attract faculty and student interest were employed. The first is a map displaying usage of institutional repository content from around the world. This map uses Tableau software to display Google Analytics data. The second method is a text mining tool allowing users to generate wordclouds from dissertation and thesis abstracts according to discipline and year of publication. The wordcloud uses R programming language, the Shiny software package, and a text mining package called tm. Change in the number of institutional repository website sessions was analyzed through changepoint analysis

A Brief Discussion on Wide Area Security and Stability Control of Power System Based on Response

At present, with the continuous development of China's social economy, the scale of domestic power system has been further expanded, which also makes the structure of China's power grid system gradually become more complex[1]. Therefore, it is necessary to continuously increase the single unit capacity of power equipment. The purpose is to make the single unit capacity match the operation of power system, so as to improve the operation performance of power system. Besides, it can also increase economic benefits. Based on this, this paper expounds the concept and control mode of power system stability. Then the key technology of wide area security and stability control of power system based on response is analyzed from four aspects. They are wide area dynamic feature information extraction, disturbed trajectory prediction, system stability discrimination and stability control. Finally, the practical application is discussed in detail. It hopes that the power sectors can improve the stability control level of power system wide area security

APPLYING PARTICLE SWARM OPTIMIZATION TO ESTIMATE PSYCHOMETRIC MODELS WITH CATEGORICAL RESPONSES

Current psychometrics tend to model response data hypothesized to arise from multiple attributes. As a result, the estimation complexity has been greatly increased so that traditional approaches such as the expected-maximization algorithm would fail to produce accurate results. To improve the estimation quality, high-dimensional models are estimated via a global optimization approach- particle swarm optimization (PSO), which is an efficient stochastic method of handling the complexity difficulties. The PSO has been widely used in machine learning fields but remains less-known in the psychometrics community. Details on the integration of the proposed approach to current psychometric model estimation practices are provided. The algorithm tuning process and the accuracy of the proposed approach are demonstrated with simulations. As an illustration, the proposed approach is applied to log-linear cognitive diagnosis models and multi-dimensional item response theory models. These two model families are fairly popular yet challenging frameworks used in assessment and evaluation research to explain how participants respond to item level stimuli. The aim of this dissertation is to fill the gap between the field of psychometric modeling and machine learning estimation techniques

Integrating Differential Evolution Optimization to Cognitive Diagnostic Model Estimation

A log-linear cognitive diagnostic model (LCDM) is estimated via a global optimization approach- differential evolution optimization (DEoptim), which can be used when the traditional expectation maximization (EM) fails. The application of the DEoptim to LCDM estimation is introduced, explicated, and evaluated via a Monte Carlo simulation study in this article. The aim of this study is to fill the gap between the field of psychometric modeling and modern machine learning estimation techniques and provide an alternative solution in the model estimation

Diagnostic Classification Models for Ordinal Item Responses

The purpose of this study is to develop and evaluate two diagnostic classification models (DCMs) for scoring ordinal item data. We first applied the proposed models to an operational dataset and compared their performance to an epitome of current polytomous DCMs in which the ordered data structure is ignored. Findings suggest that the much more parsimonious models that we proposed performed similarly to the current polytomous DCMs and offered useful item-level information in addition to option-level information. We then performed a small simulation study using the applied study condition and demonstrated that the proposed models can provide unbiased parameter estimates and correctly classify individuals. In practice, the proposed models can accommodate much smaller sample sizes than current polytomous DCMs and thus prove useful in many small-scale testing scenarios