This study affirms the role of the shadoweducation as a support system to mainstream education. Shadoweducation enables students to have a good self-efficacy towards their studies. Though, the main goal of shadoweducation is to help students academically, the self-efficacy that was investigated revealed that it also influences the personal aspects of a student. Thus, in this context, shadoweducation is being recommended. Though it is clear that the role of a shadow teacher is to help students with their studies, this study suggests that shadoweducation teachers should be more responsible with their students since they affect students in terms of self-efficacy. As this study revealed that students who had more time spent with the tutors had a higher self-efficacy, shadow teachers should bear in mind that students, though of different grade levels, need equal help since their challenges are not equal. This research further implies that parents who entrusts their children to shadow teachers should not fully depend on tutors to do all the responsibility. Parental support is still needed to boost students’ self-efficacy. Same goes with the mainstream teacher to just allow the tutors to do all the teaching to the students. It can be observed that in general, students’ self-efficacy is ‘high’; still it did not reach the ‘very-high’ mark. Factors such as parental and teacher support could be a factor to increase students’ self-efficacy.
A qualitative approach was adopted because its emphasis was on personal experiences (McMillan & Wergin, 2010; Nunan & Bailey, 2009). The interview questions are open-ended. The interviewees can give their views without constraints to a particular format. Educational experience should be studied narratively to addressing the issues of complexity and cultural and human centredness in research (Clandinin and Connelly, 2000). Therefore, narrative inquiry through semi-structured focus group interviews was adopted to elicit students’ views of the impact of shadoweducation on learning English.
English, which is prime in an internationalized society, is one of the official languages in Hong Kong. Typically, it had the greatest demand among all subjects in shadoweducation (Bray & Kwok 2003). According to Bray et.al.(2014) ,65.2% of secondary school students took English tutoring, in which 58.5% and 72.4% of secondary three and six students received it respectively. Luk (2003) attributed this to that English was a compulsory school subject, a medium of instruction for other subjects in some secondary schools and in most local universities. Since meaningful opportunities for English communication with native English speakers are not sufficient. English is learnt mainly for its practical value and advantages in Hong Kong.
In recent years, a New York-based, self-declared Tiger Mom has gained significant press attention for her theory of raising exceptional children. Reading ShadowEducation and the Curriculum and Culture of Schooling in South Korea, reminded me of her recipe for accomplished children. Hakwons are “a major social policy issue in East Asia, and particularly South Korea” (Lee & Shouse, 2011, p. 212). Unsurprisingly, a form of hakwon has migrated to the coasts of continental U.S.: “Korean migrants to the United States largely retain the mentality that admission to prestigious universities is the surest route to success” (Yi, 2013, p.190). Yi invokes the Tiger Mom stereotype, and posits a new stereotype, the Liberal Elephant, as a Western counterpoint that contests the hakwon philosophy. When Korean immigrant parents attempt to import hakwon to the U.S., they come up against a liberal humanist culture that purports to value individual interests, talents, and growth, which Yi suggests is a cause supported by actively proselytizing Christian churches. The liberal elephants resist the cultural belief in hakwon: “Caught between Tiger Moms and Liberal Elephants, many Korean immigrant parents seemed to genuinely struggle over the way to raise their children in America” (Yi, 2013, p. 194). I think I came to this book as a liberal elephant with a good deal to learn.
The core explanatory variables of this study are family factors and participa- tion in shadoweducation. Family factors include family socioeconomic status and family membership. In this study, the principal component factor analysis of the parents’ highest education (No education = 1, primary school = 2, junior high school = 3, technical secondary school/technical school = 4, vocational high school = 5, high school = 6, college degree = 7, university undergraduate = 8, graduate student and above = 9), the highest occupational status of parents, and the current family economic conditions were used to obtain quantitative indica- tors of family socioeconomic status. Family membership is measured by “How is your relationship with your mother? Not close = 1, generally = 2, very close = 3”, “How is your relationship with Dad? Not close to = 1, generally = 2, very close = 3 “, “My parents have a good relationship? Not like this = 1, this is = 2”. Wheth- er to participate in shadoweducation consists of whether to participate in cul- tural class counseling (including Olympics, general mathematics, Chinese, Eng- lish) and whether to participate in interest class counseling (including painting, calligraphy, musical instruments, dance, chess, sports), both of which are dummy
Licensed under Creative Common Page 224 feature of educational systems of the countries where the practice of private tutoring is extensive is the existence of competitive entrance examinations to the universities (Tansel ve Bircan, 2006: 303). For example, in South Korea, Greece, Japan and Turkey, high school graduates are required to take nation-wide university entrance examinations. The period since the turn of the century has seen considerable expansion of what is widely called the shadoweducation system of private supplementary tutoring (Bray and Lykins, 2012:1). The literature on shadoweducation has historically been most visible in East Asia (Zeng, 1999; Bray 2009; Dawson, 2010). The terminology used to identify diversity in different education systems and countries. In Japan tutoring centers known as juku, in South Korea for as hagwons, in Taiwan for as buxiban and in the United Kingdom for as crammers (Harnisch, 1994; Zeng, 1999; Seth, 2002; Roesgaard, 2006; Bray, 2007; Liu, 2012). In Turkey, private tutoring centers called as dersane. These terms are sometimes translated as cram schools, though that description only addresses one dimension of the works of the institutions and tends to focus on the senior secondary level (Bray, 2013:19).
Using 696 questionnaires from grade 12 students from four senior secondary schools in Beijing, this study investigates the types, cost, determinants and disparities caused by shadoweducation from the perspectives of the students. The paper investigates disparity through the parameters of ‘actual disparity’. Unlike disparity which focuses on numbers, actual disparity focuses on the reasons behind such decision and how a person feels about it. The results show that 53% of the students opted for shadoweducation during the last one year. Parents’ education and income appear to have a positive influence on their children’s tendencies to receive shadoweducation. The biggest reason for receiving it was to practice exam questions (enrichment as a group). Amongst those who did not receive it, only 21% stayed away due to unfavorable circumstances. A vast majority stayed away due to their own choice. These findings tentatively suggest that the notion that shadoweducation causes disparities amongst the students is exaggerated as most of them abstain by their own choice. It suggests a need to look at this issue more deeply, focusing more on the reasons and the feelings than mere numbers.
In this section, we compare three theories of shadow banking and securitization, and consider their implications for the financial crisis. All three theories see securitization as meeting the demand for safe debt by pooling and tranching cash flows so as to reduce the risk of securities thus manufactured. Early research on securitization focused on how this process can overcome adverse selection problems (see Gorton and Pennachi (1990), De Marzo and Duffie (1999), De Marzo (2005), and Dang, Gorton, and Holmstrom (2009)). According to this “textbook” view, the essential feature of safe securities is that all investors are symmetrically informed (or ignorant) about their payoffs, and therefore can trade them without fear of being ripped off. The informational symmetry among investors creates a liquid market for safe debt. In principle, this liquid market allows banks to sell off loans and reduce the riskiness of their balance sheets. Prior to the crisis, however, banks probably retained too much risk.
White is the strongest colour for drawing with on dark surfaces, and stands out the best during the day and night. The more contrast with the surrounding surface, the more visible the shadow will be. When choosing the location for the shadow, consider how it will be viewed. The heavier the traffic in the area, the more people will see it. The shadows are particularly striking when viewed from above, so consider drawing it near a higher viewing position, such as a bridge, public balcony, or steps.
This analysis of the shadow banking system explains a range of empirical phenomena. It accounts for the role of the wealth of extremely risk averse investors, which comes from the global imbalances, or institutional demand, in driving the demand for securitization (e.g., Farhi et al. 2008, Krishnamurthy and Vissing Jorgensen 2008). It explains how leverage and assets of intermediaries grow together (Adrian and Shin 2010). It explains how, in equilibrium, intermediaries pursuing a carry trade take marginal risky projects when interest rates are low (Jimenez et al. 2011). Finally, it explains how the diversification of idiosyncratic risk through securitization is accompanied by the concentration of systematic risks on the books of financial intermediaries (Acharya, Schnabl, and Suarez 2010). Under rational expectations, however, all these developments are benign.
Currently, there are a few techniques that generate soft shadows. These techniques include Plateaus, Heckbert and Herf, technique by ATI and penumbra wedges in shadow volume. The latest technique is by Kasper and Carsten using fast coverage calculation for spherical light sources. However, unlike those hard shadow algorithms, all these soft shadow algorithms have never been implemented in any game engines or any real-time applications.
ABSTRACT: According to the characteristics of urban high- resolution image remote sensing, we can work on shadow detection and removal method considering the object orientation. In this project work, shadow affected area are taken segmentation, and according to the statistical features the suspected shadows are extracted. After that, the dark objects which could be mistaken for shadows are taken off according to object properties and spatial relationship between objects. For the object detection we apply new technique which might be extension of Tsai method. In this method we apply color image transformation and global thresholding, morphological erosion convolution filtering. Experimental result shows that the accuracy of the new method is more. For shadow removal we use avalanche histogram equalization.
Of 47 unrated countries from an original 55 unrated coun- tries for which Ratha, De, and Mohapatra (2011) generated predicted ratings, 7 countries are likely to be investment grade, 10 are likely to be in the BB category, 20 in the B category, and 10 in the CCC or lower category. The countries just below the investment grade but at or above CCC are comparable to many emerging market countries with regular market access. For ex- ample, in our analysis, Swaziland’s shadow rating from Stan- dard & Poor’s ranges from B+ to BB, which puts the country in a similar bracket as Indonesia. Several other unrated develop- ing countries (for example, Algeria, Bhutan, Djibouti, Equato- rial Guinea, Maldives, and the Syrian Arab Republic) have shadow ratings in the B category or above.
dramatic. Consumerisation within the enterprise – having what you want, when and how you want it, at the price you want to pay – coupled with outdated technologies and IT models, has accelerated the adoption of cloud solutions by business units and individual users. Shadow Cloud – the unsanctioned and uncontrolled use of cloud services – has emerged as today’s equivalent of the Shadow IT problem creating both risks and opportunities for business.
The definition of the shadow economy plays an important role in assessing its size. By having a clear definition, a number of ambiguities and controversies can be avoided. In general, there are two types of underground economic activities: illicit employment and the production of goods and services mostly consumed within the household. 7 The following analysis focuses on the former type and excludes illegal activities such as drug production, crime and human trafficking. The latter type includes the production of goods and services, consumed within the household, or childcare and is not part of this analysis either. Thus, it only focuses on productive economic activities that would normally be included in the national accounts but which remain underground due to tax or regulatory burdens. 8 Although such legal activities contribute to the country’s value added, they are not captured in the national accounts because they are produced in illicit ways (e.g. by people without proper qualification or without a master craftsman’s certificate) 9 . From the economic and social perspective, soft forms of illicit employment, such as moonlighting (e.g. construction work in private homes) and its contribution to aggregate value added can be assessed rather positively.
Bank regulation exists for very good reasons. Banks must set aside cushions of capital to protect the banks against downturns and other unforeseen events, to prevent problems turning into systemic panics. Such panics are rare, but the collapse of Britain’s Northern Rock last year was a clear example of one. In the shadow banking system, institutions were able to sidestep this kind of regulation and borrow money against much smaller capital cushions than traditional regulators would accept. As a result, systemic risk increased dramatically. “Perhaps,” the BIS report said, “it is simply that no one saw any pressing need to ask hard questions about the sources of profits when things were going so well.”
On a Technical basis, shadow detection methods can also be classified as property-based and physics –based shadow detection methods. Physics-based techniques need some prior knowledge, such as light and geometry, camera calibration, or indoor scenes. However, it is a complicated process to obtain the accurate model for a random scene because the complexity of environments and variation of light sources from time to time and from place to place. Hence, mostly physics-based techniques are used for particular applications, such as moving cast shadow detection and shadow detection in aerial images. Scene knowledge is utilized in Physics-based methods due to which these methods are only used in specific applications they are designed for. The algorithm may not work well when the application environments are different. Property-based techniques utilize shadow features for shadow detection. The most obvious and prominent feature of a shadow is that it darkens the surface it spreads on, and this feature is used by almost all methods. Other features like edge, histograms, texture, geometry , color ratios, and gradient are also used widely. Sometimes, only one feature is not enough. For example, we have the feature that shadows usually have lower pixel values, but pixels with lower values may not necessarily be shadows.
Video object segmentation is of fundamental importance in many advanced video applications such as tracking and interpreting human behavior, surveillance, motor traffic analysis or environmental monitoring, and so on [1-4]. Many segmentation approaches for background subtraction have been proposed over the past decades [5-9]. Some methods include parametric and non- parametric background density estimates and spatial cor- relation approaches . However, most video objects extracted results are usually unsatisfactory in case that shadows exist in every frame of video sequence. So, moving shadow detection is critical for accurate object detection in video streams since shadow points are often misclassified as object points, causing errors in segmen- tation and tracking .