title: Platforms of Learning: The Ongoing Professonal Learning of Experienced Principals in Aotearoa New Zealand

authors: Grant, Peter

subjects: principal

abstract: Little is known about the professional learning of experienced school principals in New Zealand. How do they maintain sufficient learning to meet a diverse and fluid variety of leadership expectations after at least five years as principal? This research examines the professional learning habits and preferences of fifty two experienced school principals from a variety of schools covering subgroups of Years 1 – 13 within the Waikato and Bay of Plenty regions. A self-selected self-administered survey elicited responses on their use of over twenty three potential sources of learning. This approach was used to provide a fresh look at an under-researched topic. Research findings demonstrate that respondents used all learning sources to varying degrees according to factors such as personal preference, availability, cost, perceived quality, time and distance. A single best practice model remains elusive; those surveyed adopted eclectic approaches to their professional learning. The impact of information technologies and the role of professional principals’ associations in monitoring, promoting and supporting principals’ access to high quality professional learning are also discussed. Consideration of links between survey data and existing theory has resulted in the development of a framework of learning platforms for experienced principals. In addition, a number of dualities are highlighted. Together the proposed learning platforms and dualities provide a conceptual mechanism for the planning of deliberate professional learning and directions for further research.

text: Platforms of Learning: The Ongoing Professonal Learning of Experienced Principals in Aotearoa New Zealand Little is known about the professional learning of experienced school principals in New Zealand. How do they maintain sufficient learning to meet a diverse and fluid variety of leadership expectations after at least five years as principal? This research examines the professional learning habits and preferences of fifty two experienced school principals from a variety of schools covering subgroups of Years 1 – 13 within the Waikato and Bay of Plenty regions. A self-selected self-administered survey elicited responses on their use of over twenty three potential sources of learning. This approach was used to provide a fresh look at an under-researched topic. Research findings demonstrate that respondents used all learning sources to varying degrees according to factors such as personal preference, availability, cost, perceived quality, time and distance. A single best practice model remains elusive; those surveyed adopted eclectic approaches to their professional learning. The impact of information technologies and the role of professional principals’ associations in monitoring, promoting and supporting principals’ access to high quality professional learning are also discussed. Consideration of links between survey data and existing theory has resulted in the development of a framework of learning platforms for experienced principals. In addition, a number of dualities are highlighted. Together the proposed learning platforms and dualities provide a conceptual mechanism for the planning of deliberate professional learning and directions for further research.

year: 2011

title: Stacked generalization: when does it work?

authors: Ting, Kai Ming

subjects: stacking

abstract: Stacked generalization is a general method of using a high-level model to combine lower-level models to achieve greater predictive accuracy. In this paper we resolve two crucial issues which have been considered to be a ‘black art’ in classification tasks ever since the introduction of stacked generalization in 1992 by Wolpert: the type of generalizer that is suitable to derive the higher-level model, and the kind of attributes that should be used as its input. 
We demonstrate the effectiveness of stacked generalization for combining three different types of learning algorithms, and also for combining models of the same type derived from a single learning algorithm in a multiple-data-batches scenario. We also compare the performance of stacked generalization with published results of arcing and bagging.

text: Stacked generalization: when does it work? Stacked generalization is a general method of using a high-level model to combine lower-level models to achieve greater predictive accuracy. In this paper we resolve two crucial issues which have been considered to be a ‘black art’ in classification tasks ever since the introduction of stacked generalization in 1992 by Wolpert: the type of generalizer that is suitable to derive the higher-level model, and the kind of attributes that should be used as its input. 
We demonstrate the effectiveness of stacked generalization for combining three different types of learning algorithms, and also for combining models of the same type derived from a single learning algorithm in a multiple-data-batches scenario. We also compare the performance of stacked generalization with published results of arcing and bagging.

year: 1997-01

title: The use of Māori words in National Science Challenge online discourse

authors: Calude, Andreea S.

abstract: This paper presents data relating to the use of Maori borrowings as they occur in a corpus of scientific discourse on the websites of the eleven National Science Challenges (NSCs) and their associated Twitter feeds.

text: The use of Māori words in National Science Challenge online discourse This paper presents data relating to the use of Maori borrowings as they occur in a corpus of scientific discourse on the websites of the eleven National Science Challenges (NSCs) and their associated Twitter feeds.

year: 2018