This paper introduces Graph Convolutional Recurrent Network (GCRN), a deep learning model able to predict structured sequences of data. Precisely, GCRN is a generalization of classical recurrent neural networks (RNN) to data structured by an arbitrary graph. Such structured sequences can represent series of frames in videos, spatio-temporal measurements on a network of sensors, or random walks on a vocabulary graph for natural language modeling. The proposed model combines convolutional neural networks (CNN) on graphs to identify spatial structures and RNN to find dynamic patterns. We study two possible architectures of GCRN, and apply the models to two practical problems: predicting moving MNIST data, and modeling natural language with the Penn Treebank dataset. Experiments show that exploiting simultaneously graph spatial and dynamic information about data can improve both precision and learning speed. arXiv:1612.07659v1 [stat.ML] 22 Dec 2016 Under review as a conference paper at ICLR 2017 Figure 1: Illustration of the proposed GCRN model for spatio-temporal prediction of graph-structured data. The technique combines at the same time CNN on graphs and RNN. RNN can be easily exchanged with LSTM or GRU networks.
Despite an increase in ethnic diversity within the country, the English language teaching workforce remains undeniably binary in Korea. Using an intersectionality lens, this study was an exploration of the racialized experiences of one Ugandan female teacher of English working in Korean ELT. Semi-structured interviews were conducted to investigate how she perceived herself as an English speaker and teacher and how Koreans’ stereotypes of ideal English teachers and Black people affected her professional identity and self-perceptions. Findings suggest that the Ugandan woman was rejected by the formal accreditation process and faced considerable challenges in her efforts to be accepted as a qualified English teacher in Korea. On the other hand, she perceived herself as a native-like English speaker and a fully qualified English teacher with an MA degree in TESOL and years of English teaching experience. This study reveals not only the practical difficulties of a biased assessment system, but also the narrow discourse concerning who can legitimately be recognized as an English teacher in Korea, which is at odds with the Korean policy of a pursuing multicultural society and honoring diversity and with the global trend of recognizing multiple English.
Korean parents are strongly committed to investing time, effort, and money into improving their children's English competence in order to maximize their human capital in the era of globalization, a phenomenon often referred to as ‘English fever’ (Park, 2009). However, because of the Korean socio-economic structure, there is considerable disparity among families in the financial investment they can afford. Therefore, in the educational privileges and especially English learning support that Korean children receive is an inequity often overlooked by the majority of Koreans. One response to this issue is the newly emerging movement among Korean mothers called ‘maternal English education’ (eommapyo yeongeo), which has become a widely used term throughout the country.
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