title: "It's about empowering the whānau": Māori adult students succeeding at university

authors: Williams, Daphne Christina (Tina)

subjects: māori academic success

abstract: A large number of studies have demonstrated that Māori students are not performing well in education. The literature in the general area of Māori education paints a very grim picture that demonstrates a clear preoccupation with failure and underachievement. These studies often overlook the experiences of Māori adult students who return to study long after leaving school and experience considerable success. This paper focuses on the academic success of 16 indigenous students at a New Zealand university. It is based on the findings of a qualitative investigation that explored the experiences of Māori adult students who entered university with very few formal school qualifications and then went on to attain undergraduate degrees. This paper pays particular attention to the role of the whānau in facilitating Māori educational success at university.

text: "It's about empowering the whānau": Māori adult students succeeding at university A large number of studies have demonstrated that Māori students are not performing well in education. The literature in the general area of Māori education paints a very grim picture that demonstrates a clear preoccupation with failure and underachievement. These studies often overlook the experiences of Māori adult students who return to study long after leaving school and experience considerable success. This paper focuses on the academic success of 16 indigenous students at a New Zealand university. It is based on the findings of a qualitative investigation that explored the experiences of Māori adult students who entered university with very few formal school qualifications and then went on to attain undergraduate degrees. This paper pays particular attention to the role of the whānau in facilitating Māori educational success at university.

year: 2011

title: Modelling loanword success a sociolinguistic quantitative study of Māori loanwords in New Zealand English

authors: Calude, Andreea S.

subjects: loanwords

abstract: Loanword use has dominated the literature on language contact and its salient nature continues to draw interest from linguists and non-linguists. Traditionally, loanwords were investigated by means of raw frequencies, which are at best uninformative and at worst misleading. Following a new wave of studies which look at loans from a quantitatively more informed standpoint, modelling “success” by taking into account frequency of the counterparts avail- able in the language adopting the loanwords, we propose a similar model of loan-use and demonstrate its benefits in a case study of loanwords from Māori into (New Zealand) English. Our model contributes to previous work in this area by combining both the success measure mentioned above with a rich range of linguistic characteristics of the loanwords (such as loan length and word class), as well as a similarly detailed group of sociolinguistic characteristics of the speakers using them (gender, age and ethnicity of both, speakers and addresses). Our model is unique in bringing together of all these factors at the same time. The findings presented here illustrate the benefit of a quantitatively balanced approach to modelling loanword use. Furthermore, they illustrate the complex interaction between linguistic and sociolinguistic factors in such lan- guage contact scenarios.

text: Modelling loanword success a sociolinguistic quantitative study of Māori loanwords in New Zealand English Loanword use has dominated the literature on language contact and its salient nature continues to draw interest from linguists and non-linguists. Traditionally, loanwords were investigated by means of raw frequencies, which are at best uninformative and at worst misleading. Following a new wave of studies which look at loans from a quantitatively more informed standpoint, modelling “success” by taking into account frequency of the counterparts avail- able in the language adopting the loanwords, we propose a similar model of loan-use and demonstrate its benefits in a case study of loanwords from Māori into (New Zealand) English. Our model contributes to previous work in this area by combining both the success measure mentioned above with a rich range of linguistic characteristics of the loanwords (such as loan length and word class), as well as a similarly detailed group of sociolinguistic characteristics of the speakers using them (gender, age and ethnicity of both, speakers and addresses). Our model is unique in bringing together of all these factors at the same time. The findings presented here illustrate the benefit of a quantitatively balanced approach to modelling loanword use. Furthermore, they illustrate the complex interaction between linguistic and sociolinguistic factors in such lan- guage contact scenarios.

year: 2017

title: Interactive machine learning–letting users build classifiers

authors: Ware, Malcolm

subjects: computer science

abstract: According to standard procedure, building a classifier is a fully automated process that follows data preparation by a domain expert. In contrast, interactive machine learning engages users in actually generating the classifier themselves. This offers a natural way of integrating background knowledge into the modeling stage–so long as interactive tools can be designed that support efficient and effective communication. This paper shows that appropriate techniques can empower users to create models that compete with classifiers built by state-of-the-art learning algorithms. It demonstrates that users–even users who are not domain experts–can often construct good classifiers, without any help from a learning algorithm, using a simple two-dimensional visual interface. Experiments demonstrate that, not surprisingly, success hinges on the domain: if a few attributes can support good predictions, users generate accurate classifiers, whereas domains with many high-order attribute interactions favor standard machine learning techniques. The future challenge is to achieve a symbiosis between human user and machine learning algorithm.

text: Interactive machine learning–letting users build classifiers According to standard procedure, building a classifier is a fully automated process that follows data preparation by a domain expert. In contrast, interactive machine learning engages users in actually generating the classifier themselves. This offers a natural way of integrating background knowledge into the modeling stage–so long as interactive tools can be designed that support efficient and effective communication. This paper shows that appropriate techniques can empower users to create models that compete with classifiers built by state-of-the-art learning algorithms. It demonstrates that users–even users who are not domain experts–can often construct good classifiers, without any help from a learning algorithm, using a simple two-dimensional visual interface. Experiments demonstrate that, not surprisingly, success hinges on the domain: if a few attributes can support good predictions, users generate accurate classifiers, whereas domains with many high-order attribute interactions favor standard machine learning techniques. The future challenge is to achieve a symbiosis between human user and machine learning algorithm.

year: 2000-03