title: Conceptions of nature of technology in educational research: a systematic literature review

authors: Doyle, Andrew

abstract: Since the inception of Technology Education, the learning area and its advocates have struggled for legitimacy. Part of this challenge stems from difficulties in articulating the nature of technology (NoT) and its unique contribution to education. This article presents a systematic review of NoT in educational research, analysing empirical studies that investigate teachers’ conceptions of NoT. An analysis of the 22 included studies showed significant differences in the ways that NoT is theorised in educational research. In highlighting both the complexity of NoT and Technology Education as a learning area, 11 distinct aspects of NoT were identified in this study. This analysis also revealed tensions between a focus on specific technology/ies and Technology-as-phenomenon, suggesting the need for conceptual coherence in future research. Implications are drawn for understanding NoT, Technology Education, and the position of Technology Education in broader education narratives.

text: Conceptions of nature of technology in educational research: a systematic literature review Since the inception of Technology Education, the learning area and its advocates have struggled for legitimacy. Part of this challenge stems from difficulties in articulating the nature of technology (NoT) and its unique contribution to education. This article presents a systematic review of NoT in educational research, analysing empirical studies that investigate teachers’ conceptions of NoT. An analysis of the 22 included studies showed significant differences in the ways that NoT is theorised in educational research. In highlighting both the complexity of NoT and Technology Education as a learning area, 11 distinct aspects of NoT were identified in this study. This analysis also revealed tensions between a focus on specific technology/ies and Technology-as-phenomenon, suggesting the need for conceptual coherence in future research. Implications are drawn for understanding NoT, Technology Education, and the position of Technology Education in broader education narratives.

year: 2025

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: Aroha

authors: Maxwell, Te Kahautu

abstract: Waita Tira (choral), Lyrics composed by Te Kahautu Maxwell and Performed by Ōpotoki Mai Tawhiti (Kapa Haka group)

Waiata ā Tira: Aroha
Whakaeke: Pinepine Te Kura, Hau Te Kura
Mōteatea: Te Ao o Te Pārerā
Waiata ā Ringa: E pā to reo, Te Matatini, Herewini Parata
Poi: E ia e ia
Haka: Te Kōtiritiri, Te Kōtaratara
Whakawātea: Ōranga Tōiti, tōnui kōrero

text: Aroha Waita Tira (choral), Lyrics composed by Te Kahautu Maxwell and Performed by Ōpotoki Mai Tawhiti (Kapa Haka group)

Waiata ā Tira: Aroha
Whakaeke: Pinepine Te Kura, Hau Te Kura
Mōteatea: Te Ao o Te Pārerā
Waiata ā Ringa: E pā to reo, Te Matatini, Herewini Parata
Poi: E ia e ia
Haka: Te Kōtiritiri, Te Kōtaratara
Whakawātea: Ōranga Tōiti, tōnui kōrero

year: 2023-02-22