Search results

The purpose of this paper is to identify the papers published on urban education and analyze the characteristics of these papers to contribute to the urban education research over the recent years.

Researchers generated a comprehensive list of the articles published between 2010 and 2017 in the field of “urban education” by searching the Social Sciences Citation Index database, using the keywords of “urban” and “education.” As an additional criterion, all the articles published between 2010 and 2017 in the journals of Urban Education and Education and Urban Society were included.

There are a total of 2,123 publications that were checked and retrieved in the field of “urban education” between 2010 and 2017. Although the number of publications differs year by year, the sum of the citations received the increase from year to year on a regular basis. There are 35 articles that were cited more than h-index, as h-classics in this field for this period and top 10 of these h-classics are reported in this study. Urban Education and Education and Urban Society were revealed as the most prominent journals in the field of urban education. Also, the results show that the most cited articles, the most prolific authors and organizations, and top journals in this field are from the USA.

The scope of this study is not extensive enough, and the data obtained are not comprehensive enough. The survey was limited to the keyword of “urban education” in the fields of title, abstract, author keywords and keywords plus.

This bibliometric analysis contributes to the literature of urban education through a historical perspective. Results show that the “urban education” field attracts more attention of the researchers and the impacts of these publications are increasing from year to year. Also, there is a prevalence of the USA in the field of urban education.

The information presented in this paper provides insight into the development of urban education research over the recent years. The data obtained in this study can be used as a reference for future researchers in urban education.

The purpose of this study is to build a personalized learning intervention system, which can support students' personalized learning, improve teachers' teaching efficiency and students' learning effect.

The research proposes a personalized learning intervention method based on a collaborative filtering algorithm and knowledge map. The application of knowledge map makes learning content organized, and the use of collaborative filtering algorithm makes it possible to provide personalized learning recommendations for students. This personalized learning intervention system can monitor students' learning development and achieve the combination of personalized and efficiency. For the study, 152 seventh graders were assigned to a control group and an experimental group. Traditional learning intervention was used in the control group, and individualized learning intervention was used in the experimental group.

SPSS was used for data organization and analysis. The effectiveness of the personalized learning intervention system is verified by quasi-experimental research, and the influence of the system on students' learning effect is discussed. The result found that personalized learning interventions were more effective than traditional approaches in improving students’ achievement. However, for students of different learning levels, personalized learning intervention system has different effects on learning confidence and learning anxiety.

The personalized learning intervention system based on the collaborative filtering algorithm and knowledge map is effective in improving learning effect. And, it also has a certain influence on students' psychology.

Organizations in many domains generate a considerable amount of heterogeneous data every day. Such data can be processed to enhance these organizations’ decisions in real time. However, storing and processing large and varied datasets (known as big data) is challenging to do in real time. In machine learning, streaming feature selection has always been considered a superior technique for selecting the relevant subset features from highly dimensional data and thus reducing learning complexity. In the relevant literature, streaming feature selection refers to the features that arrive consecutively over time; despite a lack of exact figure on the number of features, numbers of instances are well-established. Many scholars in the field have proposed streaming-feature-selection algorithms in attempts to find the proper solution to this problem. This paper presents an exhaustive and methodological introduction of these techniques. This study provides a review of the traditional feature-selection algorithms and then scrutinizes the current algorithms that use streaming feature selection to determine their strengths and weaknesses. The survey also sheds light on the ongoing challenges in big-data research.

This chapter reviews the status of Education For All (EFA) in China and identifies four gaps: between rural and urban residents, between residents of geographic regions, between ethnicity groups, and between the genders. It turns to examine the educational situation and interests of girls weighed down by the crushing burden of multiple disadvantages in “left-behind” Western China. Based on analysis of macro-level socio-economic and educational conditions, along with rich micro-level data on girls’ vigorous pursuit of education, the authors argue that the changing conditions of rural girls’ lives and their education can best be understood from a critical empowerment perspective. Summarizing the global discourse and cross national evidence on the benefits of girls’ education, the chapter and looks beyond a utilitarian perspective and argues for the cogency of a critical empowerment framework. Filled with telling stories and case studies of Han Chinese, Tibetan, and Muslim girls, this chapter proposes that prioritizing girls’ education in Western China is crucial and required for achieving the MDG of gender parity. Even though girls are often stranded by family financial conditions, their actions and ideas seeing education as their future reflect a changing gender identity and role in the family and society. The fieldwork suggests that educating girls promotes localized development, reduces dangerous levels of economic gaps and social instability, but also advances hard to measure effects: personal and civil empowerment, and sustainable, harmonious cultural change – as well as MDG.