title: Delivering on sustainability's global and local orientation

authors: Collins, Eva Marie

subjects: New Zealand

abstract: A sustainability instructor takes her graduate-level sustainability class to an eco-village on an island off of Auckland, New Zealand. The students have the opportunity to make mud bricks for homes, visit houses powered "off the grid" from solar and wind energy, and tour community gardens that provide fresh fruit and vegetables. Rain water is collected from roofs for use in the homes. At the end of the day, several New Zealand students approach the instructor and enthuse about how inspired they are by this alternative lifestyle. Two international students also approach the instructor; one student is from China and the other from Tajikistan. And although they approach the instructor separately, they have the same message: "This is how we live at home. We don't want to live this way. We want to live the way you do here in New Zealand."

text: Delivering on sustainability's global and local orientation A sustainability instructor takes her graduate-level sustainability class to an eco-village on an island off of Auckland, New Zealand. The students have the opportunity to make mud bricks for homes, visit houses powered "off the grid" from solar and wind energy, and tour community gardens that provide fresh fruit and vegetables. Rain water is collected from roofs for use in the homes. At the end of the day, several New Zealand students approach the instructor and enthuse about how inspired they are by this alternative lifestyle. Two international students also approach the instructor; one student is from China and the other from Tajikistan. And although they approach the instructor separately, they have the same message: "This is how we live at home. We don't want to live this way. We want to live the way you do here in New Zealand."

year: 2010

title: Scalable Multi-label Classification

authors: Read, Jesse

subjects: multi-label

abstract: Multi-label classification is relevant to many domains, such as text, image and other media, and bioinformatics. Researchers have already noticed that in multi-label data, correlations exist between labels, and a variety of approaches, drawing inspiration from many spheres of machine learning, have been able to model these correlations. However, data sources from the real world are growing ever larger and the multi-label task is particularly sensitive to this due to the complexity associated with multiple labels and the correlations between them. Consequently, many methods do not scale up to large problems. This thesis deals with scalable multi-label classification: methods which exhibit high predictive performance, but are also able to scale up to larger problems. The first major contribution is the pruned sets method, which is able to model label correlations directly for high predictive performance, but reduces overfitting and complexity over related methods by pruning and subsampling label sets, and can thus scale up to larger datasets. The second major contribution is the classifier chains method, which models correlations with a chain of binary classifiers. The use of binary models allows for scalability to even larger datasets. Pruned sets and classifier chains are robust with respect to both the variety and scale of data that they can deal with, and can be incorporated into other methods. In an ensemble scheme, these methods are able to compete with state-of-the-art methods in terms of predictive performance as well as scale up to large datasets of hundreds of thousands of training examples. This thesis also puts a special emphasis on multi-label evaluation; introducing a new evaluation measure and studying threshold calibration. With one of the largest and most varied collections of multi-label datasets in the literature, extensive experimental evaluation shows the advantage of these methods, both in terms of predictive performance, and computational efficiency and scalability.

text: Scalable Multi-label Classification Multi-label classification is relevant to many domains, such as text, image and other media, and bioinformatics. Researchers have already noticed that in multi-label data, correlations exist between labels, and a variety of approaches, drawing inspiration from many spheres of machine learning, have been able to model these correlations. However, data sources from the real world are growing ever larger and the multi-label task is particularly sensitive to this due to the complexity associated with multiple labels and the correlations between them. Consequently, many methods do not scale up to large problems. This thesis deals with scalable multi-label classification: methods which exhibit high predictive performance, but are also able to scale up to larger problems. The first major contribution is the pruned sets method, which is able to model label correlations directly for high predictive performance, but reduces overfitting and complexity over related methods by pruning and subsampling label sets, and can thus scale up to larger datasets. The second major contribution is the classifier chains method, which models correlations with a chain of binary classifiers. The use of binary models allows for scalability to even larger datasets. Pruned sets and classifier chains are robust with respect to both the variety and scale of data that they can deal with, and can be incorporated into other methods. In an ensemble scheme, these methods are able to compete with state-of-the-art methods in terms of predictive performance as well as scale up to large datasets of hundreds of thousands of training examples. This thesis also puts a special emphasis on multi-label evaluation; introducing a new evaluation measure and studying threshold calibration. With one of the largest and most varied collections of multi-label datasets in the literature, extensive experimental evaluation shows the advantage of these methods, both in terms of predictive performance, and computational efficiency and scalability.

year: 2010

title: Addressing diversity: Race, ethnicity, and culture in the classroom

authors: Bishop, Russell

subjects: New Zealand

abstract: The widely accepted educational goals for Maori, established at the first Hui Taumata Matauranga held in 2001, are that Maori ought to be able to live as Maori, actively participate as citizens of the world, and enjoy both good health and high standards of living (Durie, 2001). Together with the government goals of equipping learners with twenty-first century skills and reducing systemic underachievement in education, these goals inform the new 2008-2012 Maoni Education Strategy, Ka Hikitia Managing for Success (Ministry of Education, 2007), which has as its main strategic outcome: Maori students enjoying education success as Maori. Within this frame, there are four student outcomes for Maori: learning to learn, making a distinctive cultural contribution, contributing to Te Ao Maori, and contributing to Aotearoa/New Zealand and the world.

text: Addressing diversity: Race, ethnicity, and culture in the classroom The widely accepted educational goals for Maori, established at the first Hui Taumata Matauranga held in 2001, are that Maori ought to be able to live as Maori, actively participate as citizens of the world, and enjoy both good health and high standards of living (Durie, 2001). Together with the government goals of equipping learners with twenty-first century skills and reducing systemic underachievement in education, these goals inform the new 2008-2012 Maoni Education Strategy, Ka Hikitia Managing for Success (Ministry of Education, 2007), which has as its main strategic outcome: Maori students enjoying education success as Maori. Within this frame, there are four student outcomes for Maori: learning to learn, making a distinctive cultural contribution, contributing to Te Ao Maori, and contributing to Aotearoa/New Zealand and the world.

year: 2009