title: Designing an internet search interface for children

authors: Vanderschantz, Nicholas

subjects: computer science

abstract: Interaction design and interface design research for children is of critical importance to ensure an educated future generation ready for the technology-focussed world. Significantly, the empirical studies that guide designers, developers, and technologists to support the production of suitable tools for children are under-represented in the scholarly literature. Using evidence from the literature combined with our own recent investigations, we provide a list of requirements for interfaces for children’s educational information seeking. An interaction model and interface design are developed and tested with an expert heuristic evaluation and cognitive walkthrough.

text: Designing an internet search interface for children Interaction design and interface design research for children is of critical importance to ensure an educated future generation ready for the technology-focussed world. Significantly, the empirical studies that guide designers, developers, and technologists to support the production of suitable tools for children are under-represented in the scholarly literature. Using evidence from the literature combined with our own recent investigations, we provide a list of requirements for interfaces for children’s educational information seeking. An interaction model and interface design are developed and tested with an expert heuristic evaluation and cognitive walkthrough.

year: 2018

title: Self-supervised Feature Extractor Training for Alzheimer’s Disease Classification

authors: Zheng, Chen

abstract: Deep learning achieves encouraging performance in natural image classification. It has huge potential for detecting neural degenerative anomalies, but this is often limited by the availability of well-segmented neuroimages and computational
resources. One way to address this problem is to apply self-supervised learning methods that utilise unsegmented neuroimages and artificial labels. Another solution is developing surrogate tasks to learn the representations of neuroimages for classification. This thesis reports the investigations of different variants of self-supervised learning and pretext tasks to train feature extractors for downstream Alzheimer’s Disease classification.
Firstly, this thesis reviews the literature regarding Alzheimer’s Disease classification and possible data leakage issues. Then, a lightweight 3D CNN-based ensemble is trained to predict brain age using the 3D MRI data of cognitively normal subjects from the OASIS-3 dataset. The extracted features are evaluated in the binary classification of CN vs. AD patients from their brain MRI scans. This approach achieved competitive performance compared with state-of-the-art methods in the literature. 
The next part of this thesis developed four different self-supervised learning pretext tasks for feature extractor training: brain age prediction, brain sMRI reconstruction, brain sMRI rotation classification, as well as a combination of all three approaches into one single multi-task predictor. To further explore the feasibility of employing synthetic neuroimaging data in the self-supervised learning setting, the proposed approaches are trained on the LDM100K dataset followed by evaluation using real-world OASIS and ADNI datasets. The real-world data training and testing leads to the best classification performance. The random cropping data augmentation technique can improve feature extractor training on 3D MRI data. Due to high computational expense and time limitations, the results of the training using synthetic data are not as satisfactory as those using real-world data. Future research is needed to develop more advanced feature extractor architectures and more complex pretext tasks that can learn more discriminative features. Another area of research to improve training efficiency would involve developing specialised software and hardware for processing 3D neuroimaging data.

text: Self-supervised Feature Extractor Training for Alzheimer’s Disease Classification Deep learning achieves encouraging performance in natural image classification. It has huge potential for detecting neural degenerative anomalies, but this is often limited by the availability of well-segmented neuroimages and computational
resources. One way to address this problem is to apply self-supervised learning methods that utilise unsegmented neuroimages and artificial labels. Another solution is developing surrogate tasks to learn the representations of neuroimages for classification. This thesis reports the investigations of different variants of self-supervised learning and pretext tasks to train feature extractors for downstream Alzheimer’s Disease classification.
Firstly, this thesis reviews the literature regarding Alzheimer’s Disease classification and possible data leakage issues. Then, a lightweight 3D CNN-based ensemble is trained to predict brain age using the 3D MRI data of cognitively normal subjects from the OASIS-3 dataset. The extracted features are evaluated in the binary classification of CN vs. AD patients from their brain MRI scans. This approach achieved competitive performance compared with state-of-the-art methods in the literature. 
The next part of this thesis developed four different self-supervised learning pretext tasks for feature extractor training: brain age prediction, brain sMRI reconstruction, brain sMRI rotation classification, as well as a combination of all three approaches into one single multi-task predictor. To further explore the feasibility of employing synthetic neuroimaging data in the self-supervised learning setting, the proposed approaches are trained on the LDM100K dataset followed by evaluation using real-world OASIS and ADNI datasets. The real-world data training and testing leads to the best classification performance. The random cropping data augmentation technique can improve feature extractor training on 3D MRI data. Due to high computational expense and time limitations, the results of the training using synthetic data are not as satisfactory as those using real-world data. Future research is needed to develop more advanced feature extractor architectures and more complex pretext tasks that can learn more discriminative features. Another area of research to improve training efficiency would involve developing specialised software and hardware for processing 3D neuroimaging data.

year: 2024

title: Repositioning within indigenous discourses of transformation and self-determination

authors: Berryman, Mere

subjects: research-whanau

abstract: This thesis reflectively and critically examines a series of research case studies initiated by a research-whānau. It explores the thinking, experiences and reflections of this research-whānau, as they worked to enhance the educational achievement of Māori students. Authorship of the thesis was undertaken by me (Mere Berryman). However, the methodology involved a collaborative, retrospective and critical reflection of research-whānau experiences and thinking, in the light of the research findings and experiences since the inception of this research-whānau in 1991. In the course of this work, the research-whānau have been able to explore what it has meant to put the principles of kaupapa Māori research into practice while working within a mainstream organisation (Specialist Education Services then the Ministry of Education). Our research work has involved repositioning ourselves from dependence on Western research methodologies to a better understanding and application of kaupapa Māori conceptualisations of research. 
The thesis begins by identifying mainstream and kaupapa Māori events that have historically and still continue to impact upon Māori students' educational experiences. These events provide the wider context for the work of this research-whānau at the interface of Te Ao Māori and Te Ao Pākehā, and for the 11 case studies that exemplify changes in our thinking and research practice over a period of 15 years. The thesis employs an indigenous (and specifically Māori) worldview as the framework for description, critical reflection, and theorising around these case studies. Common themes are collaboratively co-constructed then each theme is explained in relation to relevant Māori theory.
The thesis concludes with the shifts in theorising and practice made by the research-whānau during the course of our work as we sought to contribute in ways that were more transformative and self-determining. We argue that these shifts in theorising and practice are also required of others if we are to change the status quo and contribute constructively to improving Māori students' potential.

text: Repositioning within indigenous discourses of transformation and self-determination This thesis reflectively and critically examines a series of research case studies initiated by a research-whānau. It explores the thinking, experiences and reflections of this research-whānau, as they worked to enhance the educational achievement of Māori students. Authorship of the thesis was undertaken by me (Mere Berryman). However, the methodology involved a collaborative, retrospective and critical reflection of research-whānau experiences and thinking, in the light of the research findings and experiences since the inception of this research-whānau in 1991. In the course of this work, the research-whānau have been able to explore what it has meant to put the principles of kaupapa Māori research into practice while working within a mainstream organisation (Specialist Education Services then the Ministry of Education). Our research work has involved repositioning ourselves from dependence on Western research methodologies to a better understanding and application of kaupapa Māori conceptualisations of research. 
The thesis begins by identifying mainstream and kaupapa Māori events that have historically and still continue to impact upon Māori students' educational experiences. These events provide the wider context for the work of this research-whānau at the interface of Te Ao Māori and Te Ao Pākehā, and for the 11 case studies that exemplify changes in our thinking and research practice over a period of 15 years. The thesis employs an indigenous (and specifically Māori) worldview as the framework for description, critical reflection, and theorising around these case studies. Common themes are collaboratively co-constructed then each theme is explained in relation to relevant Māori theory.
The thesis concludes with the shifts in theorising and practice made by the research-whānau during the course of our work as we sought to contribute in ways that were more transformative and self-determining. We argue that these shifts in theorising and practice are also required of others if we are to change the status quo and contribute constructively to improving Māori students' potential.

year: 2008