title: Middle leaders dialogic practices: Facilitating professional learning for research academics in higher education settings

authors: Petrie, Kirsten Culhane

subjects: dialogic practices

abstract: Professional learning for academics is an important facet for teaching development in higher education. Facilitating this development work for research-intensive academics across different faculties is sometimes delegated to middle leaders. The practices of middle leaders in such situations where research academics have little experience or education for their role as teachers are undeniably complex. In this paper we present research examining a case study of dialogue practices encountered and promoted in a professional learning group in a higher education setting in Aotearoa New Zealand. In this case a middle leader responsible for leading professional learning created an intersubjective space where purposive dialogues helped facilitate shared learning about teaching practices for research-focused academics from the biological sciences. Analysis of the middle leader’s talk moves demonstrates that the dialogic practices of the middle leader create conditions for establishing genuine collegial collaboration, a pedagogically focused space and tentative knowledge building. These practices allow dialogues to be responsive to participants’ varied levels of experience and progress in becoming conscious of what is ‘best fit practice’ for their own tertiary teaching. Implications for providers of tertiary teaching development are proposed.

text: Middle leaders dialogic practices: Facilitating professional learning for research academics in higher education settings Professional learning for academics is an important facet for teaching development in higher education. Facilitating this development work for research-intensive academics across different faculties is sometimes delegated to middle leaders. The practices of middle leaders in such situations where research academics have little experience or education for their role as teachers are undeniably complex. In this paper we present research examining a case study of dialogue practices encountered and promoted in a professional learning group in a higher education setting in Aotearoa New Zealand. In this case a middle leader responsible for leading professional learning created an intersubjective space where purposive dialogues helped facilitate shared learning about teaching practices for research-focused academics from the biological sciences. Analysis of the middle leader’s talk moves demonstrates that the dialogic practices of the middle leader create conditions for establishing genuine collegial collaboration, a pedagogically focused space and tentative knowledge building. These practices allow dialogues to be responsive to participants’ varied levels of experience and progress in becoming conscious of what is ‘best fit practice’ for their own tertiary teaching. Implications for providers of tertiary teaching development are proposed.

year: 2025

title: Super-resolution of satellite imagery

authors: Bull, Daniel

subjects: Super-resolution

abstract: Can deep neural networks super-resolve satellite imagery to a high perceptual quality? This thesis explores the juxtaposition between the pixel accuracy and perceptual qualities of super-resolved imagery by comparing and combining a discriminative and a generative network. Rather than solving a theoretical problem, we tackle a real-world low-resolution scenario: Sentinel-2 imagery is super-resolved and evaluated against high-resolution aerial photos as ground truth; this is in contrast to super-resolving previously down-sampled data, which is the methodology used in most other studies. An existing feed-forward network architecture designed for super-resolution, called DeepSUM, is used to super-resolve multiple low-resolution images by a factor of four to obtain a single high-resolution image. DeepSUM is trained using a range of loss functions, to assess the e ect on network accuracy. A novel loss function is created, called variation loss, to help better define edges and textures to create a sharper, perceptually better product. Using an SSIM loss function gives the best result in terms of pixel-based performance. Running DeepSUM alone creates a superior output compared to bicubically up-sampling the input data, but the output is blurry and not photo-realistic. A probabilistic model from the literature, ESRGAN (Enhanced SRGAN), a Generative Adversarial Network, is trained against both raw Sentinel-2 data and the output of DeepSUM. Using ERS-GAN for super-resolution, creates a perceptually better, more realistic looking output. However, the ESRGAN output is less accurate than the DeepSUM output, as measured using pixel-based metrics. Combining ESRGAN with DeepSUM is found to inherit some of the advantages of both approaches. In an end-to-end process, using ESRGAN with the output of DeepSUM trained using variation loss is found to super-resolve an image to better show boundaries, textures and detail.

text: Super-resolution of satellite imagery Can deep neural networks super-resolve satellite imagery to a high perceptual quality? This thesis explores the juxtaposition between the pixel accuracy and perceptual qualities of super-resolved imagery by comparing and combining a discriminative and a generative network. Rather than solving a theoretical problem, we tackle a real-world low-resolution scenario: Sentinel-2 imagery is super-resolved and evaluated against high-resolution aerial photos as ground truth; this is in contrast to super-resolving previously down-sampled data, which is the methodology used in most other studies. An existing feed-forward network architecture designed for super-resolution, called DeepSUM, is used to super-resolve multiple low-resolution images by a factor of four to obtain a single high-resolution image. DeepSUM is trained using a range of loss functions, to assess the e ect on network accuracy. A novel loss function is created, called variation loss, to help better define edges and textures to create a sharper, perceptually better product. Using an SSIM loss function gives the best result in terms of pixel-based performance. Running DeepSUM alone creates a superior output compared to bicubically up-sampling the input data, but the output is blurry and not photo-realistic. A probabilistic model from the literature, ESRGAN (Enhanced SRGAN), a Generative Adversarial Network, is trained against both raw Sentinel-2 data and the output of DeepSUM. Using ERS-GAN for super-resolution, creates a perceptually better, more realistic looking output. However, the ESRGAN output is less accurate than the DeepSUM output, as measured using pixel-based metrics. Combining ESRGAN with DeepSUM is found to inherit some of the advantages of both approaches. In an end-to-end process, using ESRGAN with the output of DeepSUM trained using variation loss is found to super-resolve an image to better show boundaries, textures and detail.

year: 2021

title: Protecting Māori data: Can a Privacy Code of Practice provide a partial solution?

authors: Watts, David

abstract: This paper explores whether a Māori Data Privacy Code, developed under the Privacy Act 2020, could provide additional protection for classes of information that are regarded by Māori as tapu. The Act already covers the personal information of Māori individuals in the same way it does any other individual. We argue that a MDP Code could offer additional protection for particular classes of sensitive personal information – specifically whakapapa data and data pertaining to deceased Māori. A MDP Code could restrict the purposes for which such information can be collected, used, disclosed, retained, stored and disposed of; put stronger requirements on agencies to demonstrate that their collection and use of this information is justified; require free, prior and informed consent for its use and disclosure; and provide for greater transparency and independent Māori oversight.

text: Protecting Māori data: Can a Privacy Code of Practice provide a partial solution? This paper explores whether a Māori Data Privacy Code, developed under the Privacy Act 2020, could provide additional protection for classes of information that are regarded by Māori as tapu. The Act already covers the personal information of Māori individuals in the same way it does any other individual. We argue that a MDP Code could offer additional protection for particular classes of sensitive personal information – specifically whakapapa data and data pertaining to deceased Māori. A MDP Code could restrict the purposes for which such information can be collected, used, disclosed, retained, stored and disposed of; put stronger requirements on agencies to demonstrate that their collection and use of this information is justified; require free, prior and informed consent for its use and disclosure; and provide for greater transparency and independent Māori oversight.

year: 2025-10