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A foreground is formed through the possibilities, tendencies, propensities, obstructions, barriers, hindrances, et cetera, which his or her context provides for a person. Simultaneously, a foreground is formed through the person’s interpretations of these possibilities, tendencies, propensities, obstructions, barriers, hindrances. A foreground is a fragmented, partial, and inconsistent constellation of bits and pieces of aspirations, hopes, and frustrations. It might be both promising and frightening; it is always being rebuilt and restructured. Foregrounds are multiple as one person might see very different possibilities; at the same time they are collective and established through processes of communication. In this article educational meaning is discussed in terms of relationships between the students’ foregrounds and activities in the classroom. I illustrate how students’ dreams might be kept in cages, and how this has implications for how they engage or do not engage in learning processes. I investigate how a foreground might be ruined, and in what sense a ruined foreground might turn into a learning obstacle. Finally, I discuss processes of inclusion and exclusion with reference to the notion of foreground.

Unsupervised learning represents one of the most interesting challenges in computer vision today. The task has an immense practical value with many applications in artificial intelligence and emerging technologies, as large quantities of unlabeled images and videos can be collected at low cost. In this paper, we address the unsupervised learning problem in the context of segmenting the main foreground objects in single images. We propose an unsupervised learning system, which has two pathways, the teacher and the student, respectively. The system is designed to learn over several generations of teachers and students. At every generation the teacher performs unsupervised object discovery in videos or collections of images and an automatic selection module picks up good frame segmentations and passes them to the student pathway for training. At every generation multiple students are trained, with different deep network architectures to ensure a better diversity. The students at one iteration help in training a better selection module, forming together a more powerful teacher pathway at the next iteration. In experiments, we show that the improvement in the selection power, the training of multiple students and the increase in unlabeled data significantly improve segmentation accuracy from one generation to the next. Our method achieves top results on three current datasets for object discovery in video, unsupervised image segmentation and saliency detection. At test time, the proposed system is fast, being one to two orders of magnitude faster than published unsupervised methods. We also test the strength of our unsupervised features within a well known transfer learning setup and achieve competitive performance, proving that our unsupervised approach can be reliably used in a variety of computer vision tasks.

Most learner achievement studies tend to focus on identifying individual characteristics, ignoring the learning strategies that promote mathematical resilience. The focus of the study is on the assets embedded in an individual and their interplay with the environment. It is expected that resilience plays a deciding role in learners’ foreground with the potential to affect learner mathematics achievement in constrained environments positively. This study, framed within the socio-ecological perspective of resilience, explored how disadvantaged learners learn mathematics in a disadvantaged environment in the Further Education and Training band in South Africa. A total of nine (five boys and four girls) Grade 12 learners learning mathematics in disadvantaged environments from Johannesburg West and Johannesburg Central districts were purposively selected and one-on-one semi-structured interviews were conducted with them. This thematic report pays attention to the qualitative phase of a broader study that employed a sequential exploratory design that draws from the work of Vygotsky, Carroll and Skovsmose. The framework focuses on the dynamic interactions between learners and the connection between the home and the school. Accordingly, the findings revealed two interrelated themes, namely foreground and growth strategies. These themes make apparent the connection between the context and the interpretation of the context by an individual as translated into decisional processes. Implications for teachers are discussed.

In Sweden often immigrant students' failure in mathematics is explained by referring to deficit discourses. To critique that our aim have been to highlight the complexity of the situation in which immigrant students are positioned, by interrogating their perspectives on mathematics homework and the importance of parental support, as well as how their views seemed to have been shaped by wider Discourses. The students describe their parents' background and education as inadequate, while \"Swedish\" parents' backgrounds were considered desirable. With no hope of changing their parents, they seemed to have accepted that they have limited possibilities for achieving in mathematics.