title: Physical education down under: Fusion or confusion

authors: Culpan, Ian

subjects: physical education

abstract: At the International Council for Health, Physical Education, Recreation, Sport and Dance (ICHPER-SD) Conference held in Wellington in October 2006, the authors presented a keynote titled: Physical Education Down Under: Fusion or Confusion. This presentation was somewhat unusual as it took the form of a three act play. The story line was based around a final year teacher education student (Digger) who had a passion for physical education – doing it, studying it and wanting to teach it. In the first two scenes the story evolved around Digger planning for an in-class assessment – a 10 minute presentation on an issue relating to physical education and/or sport (in school). Digger was a dogmatic character who relished the opportunity to engage in arguments with his lecturers and fellow students about matters to do with physical edcuation. Although a ‘novice’ this helped him develop a good understanding about the purpose of physical education and its place in the school curriculum.
The setting for the third scene was a lecture room at the university where the students were to give their presentations. To ensure the students took this task seriously, the Professor made it a competition. The prize for the student with the best presentation was a trip to the ICHPER-SD conference. Five minutes into the scene Digger was asked to step forward to deliever his presentation to the Professor and ‘Lady PENZ’. It was titled ‘Food for Thought’.

text: Physical education down under: Fusion or confusion At the International Council for Health, Physical Education, Recreation, Sport and Dance (ICHPER-SD) Conference held in Wellington in October 2006, the authors presented a keynote titled: Physical Education Down Under: Fusion or Confusion. This presentation was somewhat unusual as it took the form of a three act play. The story line was based around a final year teacher education student (Digger) who had a passion for physical education – doing it, studying it and wanting to teach it. In the first two scenes the story evolved around Digger planning for an in-class assessment – a 10 minute presentation on an issue relating to physical education and/or sport (in school). Digger was a dogmatic character who relished the opportunity to engage in arguments with his lecturers and fellow students about matters to do with physical edcuation. Although a ‘novice’ this helped him develop a good understanding about the purpose of physical education and its place in the school curriculum.
The setting for the third scene was a lecture room at the university where the students were to give their presentations. To ensure the students took this task seriously, the Professor made it a competition. The prize for the student with the best presentation was a trip to the ICHPER-SD conference. Five minutes into the scene Digger was asked to step forward to deliever his presentation to the Professor and ‘Lady PENZ’. It was titled ‘Food for Thought’.

year: 2007

title: Machine learning techniques for accurate prediction and improved matching of renewable energy production, storage, and consumption

authors: Pasandideh, Seyedmostafa

abstract: In recent decades, the electricity industry has undergone considerable transformation, characterized by the expansion of competitive electricity markets, integration of renewable energy sources, and the deployment of digital systems. These developments have contributed to increased complexity and variability within power systems, highlighting the need for more accurate, adaptive, and intelligent methods for managing electricity generation, storage, and consumption. This thesis focuses on addressing these challenges, with a particular emphasis on advancing New Zealand’s renewable energy ambitions through the application of machine learning (ML) and digital twin technologies for optimizing renewable energy forecasting and management in both industrial and residential contexts.
In the industrial domain, the research develops real-time solar power monitoring systems and predictive models employing convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) networks. These models are designed to enhance forecasting precision and system efficiency by accounting for the inherent variability in renewable energy generation. Complementing this, fuzzy logic-based control strategies are implemented to manage electricity consumption adaptively in industrial processes, including applications within the meat processing industry. The incorporation of digital twin technologies further facilitates real-time monitoring, predictive maintenance, and agile response to evolving operational conditions, thereby supporting system resilience and efficiency.
In residential and community energy systems, the thesis introduces an innovative adaptive energy management framework combining streaming machine learning (SML) with a fractal-structured microgrid architecture. This framework utilizes incremental learning algorithms and digital twin principles to enable real-time adaptive control of energy flows, local trading of electricity, and dynamic adjustments in response to fluctuating supply and demand. The approach aims to enhance battery management, support local energy autonomy, and potentially reduce dependency on centralized grids. Comparative evaluations of centralized, distributed, and hybrid battery storage configurations provide insights into the trade-offs between grid interaction, storage longevity, and operational flexibility, offering practical solutions for optimizing residential microgrids.
In conclusion, this thesis explores a combined, case-study based approach to enhancing renewable energy utilization through the combined application of advanced machine learning and digital twin technologies. By addressing both industrial and residential energy systems, the research contributes to the broader vision of developing reliable, efficient, and sustainable power infrastructures. The findings align with New Zealand’s commitment to reducing carbon emissions and advancing renewable energy technologies, offering case-study evidence and potential approaches to support the transition toward a low-carbon energy future.

text: Machine learning techniques for accurate prediction and improved matching of renewable energy production, storage, and consumption In recent decades, the electricity industry has undergone considerable transformation, characterized by the expansion of competitive electricity markets, integration of renewable energy sources, and the deployment of digital systems. These developments have contributed to increased complexity and variability within power systems, highlighting the need for more accurate, adaptive, and intelligent methods for managing electricity generation, storage, and consumption. This thesis focuses on addressing these challenges, with a particular emphasis on advancing New Zealand’s renewable energy ambitions through the application of machine learning (ML) and digital twin technologies for optimizing renewable energy forecasting and management in both industrial and residential contexts.
In the industrial domain, the research develops real-time solar power monitoring systems and predictive models employing convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) networks. These models are designed to enhance forecasting precision and system efficiency by accounting for the inherent variability in renewable energy generation. Complementing this, fuzzy logic-based control strategies are implemented to manage electricity consumption adaptively in industrial processes, including applications within the meat processing industry. The incorporation of digital twin technologies further facilitates real-time monitoring, predictive maintenance, and agile response to evolving operational conditions, thereby supporting system resilience and efficiency.
In residential and community energy systems, the thesis introduces an innovative adaptive energy management framework combining streaming machine learning (SML) with a fractal-structured microgrid architecture. This framework utilizes incremental learning algorithms and digital twin principles to enable real-time adaptive control of energy flows, local trading of electricity, and dynamic adjustments in response to fluctuating supply and demand. The approach aims to enhance battery management, support local energy autonomy, and potentially reduce dependency on centralized grids. Comparative evaluations of centralized, distributed, and hybrid battery storage configurations provide insights into the trade-offs between grid interaction, storage longevity, and operational flexibility, offering practical solutions for optimizing residential microgrids.
In conclusion, this thesis explores a combined, case-study based approach to enhancing renewable energy utilization through the combined application of advanced machine learning and digital twin technologies. By addressing both industrial and residential energy systems, the research contributes to the broader vision of developing reliable, efficient, and sustainable power infrastructures. The findings align with New Zealand’s commitment to reducing carbon emissions and advancing renewable energy technologies, offering case-study evidence and potential approaches to support the transition toward a low-carbon energy future.

year: 2025

title: Student belonging: critical relationships and responsibilities

authors: Berryman, Mere

subjects: Belonging

abstract: In this paper, we consider New Zealand’s education system to understand what can happen when we focus only on excellence and students reaching their potential, without simultaneously investing in their sense of belonging and wellbeing. National statistics suggest we are alienating and shortchanging an increasing number of students and, for disproportionate numbers of Indigenous students, these statistics are part of a world trend. The literature, and the students themselves, highlight the need to overturn the underlying racism that persistently disadvantages clearly identifiable groups of students over others. Until we do, using equity and excellence as the most powerful drivers for reform, will continue to promote conditions where our students’ sense of belonging and wellbeing are undermined throughout their education and we will risk, failing to address the ensuing negative statistics. We conclude with a response that we have learned from working with these same students.

text: Student belonging: critical relationships and responsibilities In this paper, we consider New Zealand’s education system to understand what can happen when we focus only on excellence and students reaching their potential, without simultaneously investing in their sense of belonging and wellbeing. National statistics suggest we are alienating and shortchanging an increasing number of students and, for disproportionate numbers of Indigenous students, these statistics are part of a world trend. The literature, and the students themselves, highlight the need to overturn the underlying racism that persistently disadvantages clearly identifiable groups of students over others. Until we do, using equity and excellence as the most powerful drivers for reform, will continue to promote conditions where our students’ sense of belonging and wellbeing are undermined throughout their education and we will risk, failing to address the ensuing negative statistics. We conclude with a response that we have learned from working with these same students.

year: 2019