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Drivers of generative AI acceptance in an ODFL institution through the lens of the Technology Acceptance Model (TAM).
No full textThe evolution of education is often spearheaded by advancements in Information Technology. Artificial Intelligence (AI) is heralding a new era in education, offering unprecedented opportunities and unlocking new potential for teaching and learning. Generative AI (Gen AI) applications are leading the race in this development. Despite the enormous promise and potential of Gen AI in Open, Distance and Flexible Learning (ODFL) settings, there is a unanimous call to tread cautiously due to ethical concerns surrounding its use. Nonetheless, it is important to explore the motivations for adoption and use Gen AI in ODFL settings. Studies on Gen AI in ODFL are still very few and evolving. Following technology acceptance model (TAM) as a theoretical lens, this study proposes a model of intrinsic motivation towards Gen AI acceptance and use in ODFL settings
Poverty and relationships in small town Aotearoa New Zealand.
No full textOral Presentation.Topic: An exploration of how people living in poverty talk about relationships which are meaningful to them. The presentation is based on a qualitative study where the stories of people who self-identified as experiencing poverty were gathered and the aim of the study was to give people experiencing poverty and living a rural community a ‘voice’. The presentation shares what the participants said about their relationships and how their relationships helped them manage daily life and/or added to their level of stress. Participants in the study lived in a rural location and geography combined with poverty made building and maintaining relationships problematic.
Basis of presentation: The presentation is based on a qualitative research project involving 28 participants who self-identified as experiencing poverty.
Findings: For many of the study participants experiencing poverty and living in a rural community resulted in social isolation and limited social capital. Relationships were strained by the ongoing stress of ‘making ends meet’, but all participants worked hard to keep their social connections intact and were keen to reciprocate in relationships with others, including those with social services. Having an awareness of the daily lived experience of poverty is important for people working in social work and welfare. Hearing the ‘voices’ of people experiencing poverty is a critical part of learning for ākonga who plan to work in social services
Enhancing work-life balance and family well-being: Insights from working fathers in the UAE.
No full textPoster Presentation
Towards ensemble feature selection for lightweight intrusion detection in resource-constrained IoT devices.
No full textAppropriation of engineering discourse through the technological design process.
No full textOral Presentation
Teacher-educators as researchers: Crossing boundaries, developing bicultural habits of mind.
No full textOral Presentation
"I am fun and fun is me!": What's fun got to do with rights-based inclusive teaching and learning?
No full textOral Presentation
Is this the beginning of a beautiful friendship? Constructive alignment and artificial intelligence.
No full textThis practice paper is an emerging exploration of the connections between the guiding principle of constructive alignment and the uses of artificial intelligence. Our practice is situated in an Open, Distance and Flexible Learning (ODFL) environment, more specifically in the Open Polytechnic of New Zealand | Kuratini Tuwhera o Aotearoa context. Large language models (LLMs) have been increasingly embraced by the learning design community for design and development (Davis & Lee, 2024), and at Open Polytechnic, we
internally developed Course-o-Matic, a secure Application Programming Interface (API) that interacts with the OpenAI models. Our research investigates whether, based on the guiding principles of constructive alignment theory, we could use Course-o-Matic to assess the alignment between intended learning outcomes and learning activities. Our methodology involves investigating different prompting strategies, writing prompts that
could allow us to assess the constructive alignment, trialling the prompts using Course-oMatic 3000 and comparing the results from the different prompts with our own observations about the courses. Our results demonstrated that, regardless of the prompting strategy used, AI has the potential to support initial observations about alignment of courseware and provide helpful insights. However, prompts require carefully phrased constraints to ensure AI remains focused on alignment and identifies gaps appropriately. We also found that AI may struggle to adapt its responses to the OFDL context