Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal International Journal of Educational Narratives Journal of Biomedical and Techno Nanomaterials
Harris, Oliver
Unknown Affiliation
Biochemistry, Genetics & Molecular Biology Education Social Sciences Other

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Telling the Land: Aboriginal Educational Narratives and Curriculum Integration in Australian Schools Harris, Oliver; Taylor, Sarah; Mitchell, Thomas
International Journal of Educational Narratives Vol. 3 No. 3 (2025)
Publisher : Yayasan Pendidikan Islam Daarut Thufulah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/ijen.v3i3.2211

Abstract

Background. Efforts to meaningfully integrate Aboriginal perspectives into Australian school curricula remain uneven and contested, often constrained by systemic limitations and a lack of culturally informed pedagogical frameworks. Aboriginal narratives, particularly those tied to Country, embody holistic systems of knowledge that challenge Western linear constructions of curriculum and offer alternative modes of understanding land, identity, and education. Purpose. This study explores how Aboriginal educational narratives are interpreted and integrated into curriculum practice by both Indigenous and non-Indigenous educators across diverse Australian school settings. Method. Employing a qualitative, multi-site case study approach, the research involved interviews with 22 educators and curriculum leaders, alongside analysis of classroom materials and reflective teaching journals. Results. The findings reveal that successful integration depends on deep, relational engagement with community knowledge holders, an ethic of cultural humility, and a willingness to reconfigure disciplinary boundaries. Educators who engaged in collaborative curriculum-making reported greater confidence in embedding Indigenous perspectives in ways that respect narrative sovereignty and pedagogical integrity. Conclusion. The study concludes that Aboriginal storytelling offers not only content but a method—transforming curriculum into a site of shared responsibility, ethical dialogue, and place-based learning.  
ARTIFICIAL INTELLIGENCE IN MEDICINE: A DEEP LEARNING CONVOLUTIONAL NEURAL NETWORK FOR PATHOLOGICAL IMAGE ANALYSIS AND CANCER GRADING Smith, James; Harris, Oliver; Anurogo, Dito
Journal of Biomedical and Techno Nanomaterials Vol. 2 No. 4 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jbtn.v2i4.2480

Abstract

The histopathological analysis of tissue slides is the gold standard for cancer diagnosis and grading. However, this process is labor-intensive, time-consuming, and prone to inter-observer variability, which can affect clinical outcomes. The advent of artificial intelligence (AI), particularly deep learning, presents a transformative opportunity to enhance diagnostic precision and efficiency in pathology. This study aimed to develop, train, and validate a deep learning convolutional neural network (CNN) for the automated analysis of pathological images to accurately classify malignancies and provide reliable cancer grading. A robust CNN model was trained on a comprehensive, curated dataset of thousands of annotated digital histopathology slides from multiple cancer types. The model’s performance was rigorously evaluated against the consensus diagnoses of expert pathologists using key metrics, including accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC-ROC). Our developed CNN model demonstrated exceptional performance, achieving an overall accuracy of 98.7% in distinguishing malignant from benign tissues. For cancer grading, the model yielded a Cohen’s Kappa score of 0.92, indicating almost perfect agreement with expert pathologists. The model also showed high robustness to variations in staining and image acquisition protocols. This research confirms that a deep learning CNN can function as a highly accurate and reliable tool for automated pathological image analysis and cancer grading. Integrating such AI systems into clinical workflows could significantly augment the capabilities of pathologists, leading to improved diagnostic consistency, reduced workload, and ultimately, better patient care.
Co-Authors Dito Anurogo, Dito Mitchell, Thomas Smith, James Taylor, Sarah