Welcome to Research Bank, our open research repository that includes research produced by students and staff while affiliated with Unitec, Eastern Institute of Technology (EIT), Otago Polytechnic, Toi Ohomai and Southern Institute of Technology (SIT). It is intended to facilitate scholarly communication and shared access to our research outputs
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Item Toi Ako : Developing Māori arts pedagogy: A kaupapa Māori literature review(ePress, Unitec, Te Pūkenga, 2024-08-28)Toi Reo, Toi Ora, Whatuora, explores Māori arts-based pedagogy and practice to story the aspirations of three connected Māori-medium whānau in the Waitematā Kāhui Ako, in Tāmaki Makaurau Auckland. Through the Māori pedagogy and practice of whatu kākahu (cloak making), this research contributes to the scholarship and practice of Māori arts-based pedagogies as key language and cultural revitalisation practices within rūmaki reo (Māori-language immersion) education. Importantly, this research sets out to strengthen Māori-language community relationships through the pedagogy of whatu wānanga, to better support kura understandings of, and responses to, whānau aspirations for flourishing reo and tikanga. [...] The aim of this literature review is to support our argument that Māori creative practice, in this context of whatu, is more than the traditional finger-weaving practice used to create whatu kākahu (woven Māori cloaks) as a product. Instead, whatu is a practice, a set of ideas, and theory that has previously been characterised as kaupapa Māori research methodology (G. Smith, 2003; H. J. Smith, 2017; 2019; L. Smith, 2021) and is now being advanced as Māori arts pedagogy. [...] Māori creative practice would benefit from reflecting on how and why they teach the way they do, exploring questions such as: What is Māori about my pedagogy? How and why do I teach the way I do? How much of how I teach stems from the way that I was taught?Item Aotearoa New Zealand student nurses’ perceptions of working in aged care: August 2024(ePress, Unitec, Te Pūkenga, 2024-09-02)Supporting an ageing population is a globally recognised challenge (United Nations, 2020; World Health Organization, 2023). In the next ten years, the healthcare sector in Aotearoa New Zealand will confront this significant issue as the number of older adults markedly increases (Stats NZ, 2020). By 2036 over a quarter of the population of New Zealand will be over 65 years old (Te Pou o te Whakaaro Nui, 2019.) This demographic shift warrants significant attention because of increasing longevity and the number of older adults that will be living with complex or multiple diagnoses requiring supportive healthcare. As a consequence of technological and medical advances, adults will be living longer with chronic illness and the effects of ageing. Nurses are at the front line of healthcare and are ideally placed to respond to the changing demographic. It is imperative, therefore, that we understand how well we are preparing nurses for doing the work that will be required. As educators, we need to understand what curriculum developments might be needed to support a well-prepared future nursing workforce (Heath et al, 2023). This is the second of three reports about the future nursing workforce and aged care, in which student nurses’ perceptions of working with older adults are examined. In conjunction with the earlier report on the stocktake of clinical placements, these findings will form the basis of the final phase of the research, a consultation with the profession and broader community about their views on what should be included about older adult healthcare in pre-registration nursing programmes. It will be our opportunity to ensure the readiness of our future nursing workforce in Aotearoa New Zealand.Item AI Design and Policy for Education(2024-09-02)The increasing availability and testing of Artificial Intelligence in Education (AIED) is highlighting the concurrent gap and demand for ethical design and use. This paper proposes the design thinking framework for use in AI design. Design thinking inverts the current AI development process which builds the AI application first, then looks to apply this to human problems. In contrast, the human-centred focus of design thinking in AI development places empathy and agency with users and marginalised or affected parties at the heart of the design process. Design thinking shifts the dominant discourse from the technological merits of AI development to the merits of the AI design for the needs and interests of ākonga (students) and kaiako (teachers), as defined by them. It ensures that AI tools are not just those that are feasible but desirable from end-users’ perspectives. By applying design thinking principles, the AI applications are intrinsically aligned to ākonga needs. We consider design thinking to be grounded in consideration of human-centric ethical and cultural influences that shape educational technology uptake in Aotearoa New Zealand.Item Gen-AI chatbots for tertiary students using Cogniti.ai(2024-09-01)This paper shares initial results trialling Generative AI (Gen-AI) agents or chatbots using Cogniti.ai in a tertiary setting in New Zealand. The report evaluates the utility and value of Gen-AI chatbots in the context of personalised learning and equity of access for edtech and learning technology. The speed of change with this technology makes it imperative we explore the capabilities of chatbots as quickly as possible in 2024, to make recommendations for use in our tertiary sector with a wide range of chatbot uses already in use described by Liu, (2023). The initial findings indicate that AI agents or chatbots are valuable for students in preparing for high-stakes testing scenarios.Item Enhancing forthcoming trend estimation in trading platform based on multiphase combination of sentiment analysis and LSTM(2024)Since its inception, the stock market has piqued the curiosity of researchers, and several attempts have been made to forecast its future patterns. The market is dynamic, and the value of an organisation’s stock price is influenced by a variety of elements, including previous stock data and public attitudes and opinions. Researchers have shown that the "public sentiment" which split them into two factions, one in favor and the other against is the most prevalent of these characteristics. It got simpler to analyse the relationship between them as Artificial Intelligence (AI) advanced and strong Machine Learning (ML) and Deep Learning (DL) algorithms were introduced. The number of outlets where the public can express their opinions has grown exponentially as the world entered the internet era. The researchers have experimented with several data sources, such as social networking websites, financial news websites, online global newsrooms, Yahoo finance websites, and other forums for exchanging opinions, in order to gather information about investor and public feelings as well as stock historical data. The forecasts have become more accurate over time as a result of testing various algorithms. Most of these studies have focused on larger stock markets, such as those in the USA, India, China, Japan, and Europe. Comparatively smaller stock markets cannot use the same strategies since they are virtually independent and heavily influenced by local news and public opinion, which might differ greatly from those of the rest of the globe. In this research, this gap has been addressed and focus has been made on the New Zealand Stock Exchange (NZX). This thesis explores the field of stock market forecasting with a particular emphasis on the NZX, a market that has received relatively little attention due to its distinct architecture and autonomous operations. In order to improve Future Trend estimation in trading platforms, this dissertation makes use of a multiphase machine learning and deep learning methods. The suggested method combines sentiment analysis with historical stock data from Yahoo Finance and financial news data from "sharechat.co.nz". Five of the largest New Zealand organisations stock data sets has been obtained. These companies has been selected because the textual data had more mentions of them than of others, which allows the sentiment scores to accurately reflect changes in the company. In this research, Future Trend prediction models are built employing deep learning techniques, specifically Long Short-Term Memory (LSTM) networks, and five different subprocesses. Across various sub-processes, the impact of sentiment news and stock history data on Future Trend estimates is examined. Furthermore, feature selection strategies are used to reduce the possibility of overfitting. A thorough literature analysis, a thorough explanation of the research methods, and the nuances of data collecting and pre-processing have all been included in the thesis format. It goes on to explain how the suggested prediction model was put together and clarifies the ramifications of the study’s findings. Therefore, the principal contributions of this thesis are outlined as follows: 1) development of an innovative multiphase framework for Future Trend prediction for NZX by integrating sentiment analysis data from regional news with historical stock data; 2) deployment and comparative evaluation of both LSTM and machine learning models namely RF and SVM throughout five sub-processes, showcasing their efficacy in forecasting future patterns for five well-known New Zealand businesses and insights into how well they work and how they could potentially leveraged for boosting the accuracy of stock market trend predictions; 3) analysis of the special characteristics of the NZX, emphasising how sensitive it is to regional news and public opinion in contrast to other markets; 4) demonstration of how pre-processing methods like feature selection and data normalisation enhanced the predictive models accuracy and effectiveness; 5) recognising the difficulties and developments in acquiring and applying highquality financial news data for NZ; 6) highlighting how the prediction framework aid in improving accuracy for Future Trend prediction being used for the same goal at various sub-processes.
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