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Human-Centred Blended Citizens. A study of Digital Citizenship through Drama Pedagogy
No full textThis research investigates how drama pedagogy fosters responsible digital citizenship in NSW secondary schools. Existing digital citizenship frameworks often emphasise technical proficiency or rules-based compliance (ISTE, 2016; Ribble, 2015), neglecting the socio-emotional competencies essential for navigating the physical and digital worlds. To address this gap, I introduce the concept of the ‘blended citizen,’ an individual who engages responsibly across both spaces and practises empathy, ethical awareness, and critical reflection.
Conducted across four NSW high schools, this comparative case study draws on Augusto Boal’s Theatre of the Oppressed (1979) as a pedagogical framework, enabling students to examine the social and ethical dimensions of their online and offline actions through drama. Using techniques such as Forum and Image Theatre, students engaged critically and collaboratively to deepen their understanding of responsible citizenship in a blended world (Neelands, 1992).
The findings reveal that drama pedagogy provides a distinctive platform for experiential learning, encouraging students to reflect on digital identities, relationships, and the consequences of their online actions. These practices cultivate empathy, foster democratic participation, and promote collaborative problem-solving (O'Connor & Freebody, 2022). Findings also demonstrate how drama enables both students and teachers to explore identity and social interaction, strengthening ethical digital behaviours, wellbeing, and critical awareness through sustained reflection and engagement.
This research contributes to both digital citizenship and drama education by advocating for an integrated approach that balances technical skills with socio-emotional learning. It recommends that NSW educational policies expand beyond current frameworks and restrictions to create opportunities that strengthen the human-centred competencies essential for raising and educating responsible blended citizens
Assessing anti-tumour immunity and adoptive T cell therapies in solid tumours
No full textAdoptive T cell therapies have achieved tremendous progress in haematological malignancies and
represent an emerging modality for solid tumours with limited treatment options. This thesis aims to
improve their translation in pancreatic and appendiceal cancers. In pancreatic cancer, mesothelin
(MSLN)-targeting chimeric antigen receptor (CAR) T cells have shown limited efficacy, and the
antigen remains poorly characterised. In a cohort of 74 patients examined by immunohistochemistry
(IHC), high MSLN expression (H-score 62) was significantly associated with improved relapse-free
survival (p = 0.021). Analysis of transcriptomic datasets found that MSLN-high tumours exhibited an
immunosuppressive microenvironment with reduced CD8 T cell abundance, supported by IHC in a
small validation cohort (n = 10). In appendiceal cancer, adoptive T cell therapies have not been
explored, and CAR T cell and tumour infiltrating lymphocyte (TIL) therapies were assessed for the first time. MSLN and mucin-1 (MUC1) were detected in three of four patient-derived organoids by
flow cytometry and transcriptomic analyses. Two anti-MSLN CAR T cell products, SS1 and P4 CAR
T cells, demonstrated effective killing of antigen-positive organoid-derived monolayer cells in
impedance-based cytotoxicity assays. TIL expansion 15 million cells) was achieved from 73%
(8/11) of surgical specimens, yielding highly enriched CD3+ populations (>95%) with upregulated
functional responses to superantigen stimulation, but consistent tumour-specific activity was not
observed in co-cultures with matched, dissociated organoids. Overall, this thesis identifies a barrier
limiting the efficacy of MSLN-targeting CAR T cells in pancreatic cancer and demonstrates the
potential of CAR T cell and TIL approaches in appendiceal cancer, offering crucial insights to guide
the development of more effective adoptive T cell therapies in these malignancies of significant
unmet nee
Inference on Climate Indices Using Bayesian Variable Selection in Quantile Regression
No full textRainfall variability significantly affects ecosystems, agriculture, and water management in eastern Australia. Influenced by global climate phenomena, these fluctuations challenge management practices at different locations and times of the year. Understanding these drivers is essential to improve resource management and mitigate the impacts of extreme weather events such as droughts and floods.
Advancements in physical and statistical climate models have enhanced our understanding of global climate phenomena in relation to daily rainfall extremes. However, these models often lack a probabilistic framework that can assess the relationship between climate drivers and the full distribution of monthly rainfall. A key challenge is the inadequate modeling of uncertainty and variability inherent in climate systems. Bayesian approaches with variable selection are valuable in this context, as they incorporate prior knowledge and provide robust parameter estimation. Nonetheless, applications of such models that comprehensively assess the relationship between climate indices and the full distribution of monthly rainfall—including extremes—remain limited. This gap highlights the need for further development to improve predictive capabilities for monthly rainfall patterns under varying climate conditions. This thesis introduces a novel approach that employs Bayesian variable selection within a spatial quantile regression framework to examine the relationship between global climate indices and monthly rainfall distribution in New South Wales (NSW), Australia. By analyzing different quantiles of rainfall, this approach aims to offer a spatially varying inference of how these climate indices impact the entire spectrum of rainfall. This approach utilizes a hierarchical Bayesian quantile regression model to address distinct modeling requirements and complexities at each hierarchical level
The Life of dPa' bo gTsug lag phreng ba (1504–1566) with a Focus on His Contribution to the Tibetan Scholastic Tradition
No full textdPa' bo gTsug lag phreng ba is a well-known 16th-century scholar and historian who lived during the heyday of Tibetan scholasticism, and a figure of particular importance to the Karma bKa' brgyud tradition of Tibetan Buddhism. Although his overall impact was remarkable, and his Tibetan religious history mKhas pa'i dga' ston is widely accepted as a significant, unique, and reliable source by Tibetan scholars and academics alike, his life and writings are little studied. His scholarly contributions to the study of Tibetan Buddhism are virtually unrecognised. Therefore, this thesis addresses this lacuna.
Using mainly philological and historical analysis, this research explores his life and work within the context of his time. With the study of life, this project is expected to contribute to the historical understanding of 16th-century Tibet in general and the history of the Karma bKa' brgyud school in particular. The study of his various texts sheds light on his scholarly contribution to Tibetan Buddhist studies. Treatment of the doctrinal stances he held also contributes to the discourse and intellectual history of the Tibetan scholastic tradition, especially of the Karma bKa' brgyud school.
The first chapter lays a foundation for this research by identifying its aims and objectives and outcomes. Research methodologies are also delineated in the first chapter. The second chapter mainly studies his life, including his education, recognition as a reincarnated master, and his scholarly deeds. The third chapter surveys his scholarly writings and provides an overview of his texts. The fourth chapter discusses his main commentaries and their significance. Some of his philosophical stances are also explored. The fifth chapter summarises the findings of this research
Educator Advisors in Australian Higher Education: Their Roles, Purpose and Contribution to Learning and Teaching
No full textAustralian higher education teaching and learning has undergone significant change in the last thirty years, influenced by the rapid evolution of information technologies. To help academics navigate increasingly complex practices, growing numbers of support staff have been employed in academic developer (AD), learning designer (LD) and educational technologist (ET) roles. These specialist roles are referred to collectively in this study as EdAdvisors, a portmanteau of Educator and Advisor.
This study advances understanding of EdAdvisor roles, practices, purpose, and the factors influencing their efficacy, using a practice theory and practice architectures lens in a mixed-methods study comprised of a survey and semi-structured interviews with EdAdvisors in 41 Australian higher education institutions. This study found that practitioners in EdAdvisor roles contribute to learning and teaching in alignment with their expertise, with ADs developing the pedagogical knowledge of academics, LDs designing and developing learning resources and activities, and ETs supporting and enabling appropriate education technologies. Examining the practices of all three EdAdvisor roles collectively has supported the development of practice bundles which inform rich new descriptions of these roles. Examination of factors influencing EdAdvisors’ practices identified organisational structures, attitudes toward learning and teaching, tensions between centralised and faculty-based areas of the university, and the divide between academic and professional staff as areas where actions may be taken to enhance EdAdvisor efficacy. The three EdAdvisor roles have rarely been considered together in scholarly research, but this thesis has demonstrated that doing so contributes to greater understanding of these roles, their interconnectedness, contribution to learning and teaching, and the factors which shape their efficacy
Characterisation of the immune response and investigation of autophagy as an immunotolerance mechanism in flying foxes infected by Australian bat lyssavirus
No full textAustralian bat lyssavirus (ABLV) is harboured by flying foxes and can result in zoonotic disease. It is known to cause a nonsuppurative meningoencephalitis, and intensity of inflammation is highly variable across individuals. Recent in vitro studies suggest that autophagy has the potential to clear ABLV infection in flying fox cell lines and is expressed at a higher level in these flying foxes. This study aims to characterise the inflammatory response observed in ABLV-infected flying foxes and explores autophagy as an anti-viral mechanism that could account for the variability in lesions across individuals.
Histological changes were scored to determine the overall severity of inflammation, and immunohistochemistry (IHC) was used to label the associated inflammatory cells. Meningitis, gliosis, and perivascular cuffing were the most frequent lesions. In ABLV qPCR-positive flying foxes, macrophages were the most abundant, with a 3-fold increase compared to negative controls. T-lymphocytes and B-lymphocytes exhibited a 10-fold increase in ABLV-positive animals and were significantly more numerous in the meninges. Lymphocyte counts correlated with greater lesion severity, and T-lymphocytes were significantly associated with reduced viral load. No significant variation in macrophage distribution was observed between anatomical regions.
Autophagy was assessed via IHC of autophagy marker LC3 and immunolabelling was compared between ABLV-positive and ABLV-negative flying foxes, to ascertain whether autophagy levels are elevated in association with ABLV infection. ABLV-positive flying foxes had a lower immunolabelling of the LC3 autophagy marker compared to ABLV-negative flying foxes. Additionally, the percentage of immunolabelling cells did not appear to correlate with type/severity of inflammation or viral load. Hence, the underlying cause for the reduced LC3 immunolabelling in infected animals remains to be unravelled and would be an opportunity for further investigation
Pattern Classification-based Electricity Price Interval Forecasting with Discrete-Continuous Inputs
No full textThe worldwide restructuring of electricity markets toward deregulation over the past few decades has attracted significant research attention to electricity price forecasting due to its importance for market participants. In recent years, market participants are more concerned with forecasting the general price level than the exact price value, as this better supports bidding strategies and investment decision making. However, current interval forecasting approaches face key challenges in efficiency, robustness against volatility, and ineffective periodicity extraction technique. To address the limitations of existing electricity market price forecasting methods and to develop more practical strategies for real-world market conditions, we propose a novel model that combines TimesBlock —a state-of-the-art frequency-domain method capable of extracting multi-periodic patterns from data— with LSTM, characterized by strength in capturing long-term temporal dependencies. This integration enables accurate pattern classification-based interval forecasting of electricity prices. A discrete-continuous hybrid data input strategy is innovatively designed to enhance the model’s ability to handle volatile data while preserving essential periodic information. The experimental results demonstrate that our model provides reliable electricity price interval forecasts and outperforms other deep learning and machine learning models on multiple metrics. Additionally, the Discrete-Continuous hybrid data input improves computational efficiency, achieving several times faster running speed compared to traditional continuous-input only methods
Stabilisation, nano-spectroscopic analysis and charge-driven liposomal encapsulation of bacteriophages for advanced therapeutic delivery
No full textThe global antibiotic-resistance crisis has renewed interest in bacteriophage (phage) therapy as a targeted treatment for multidrug-resistant respiratory infections. However, phage stability during formulation, storage, and aerosol delivery remains a critical challenge. Conventional characterization methods often cannot detect subtle nanoscale changes affecting phage stability. To address this challenge, advanced nanocharacterisation techniques—including scattering scanning near-field optical microscopy (s-SNOM) and atomic force microscopy–infrared spectroscopy (AFM-IR)—were employed to probe individual phage particles and reveal molecular-level alterations. Using these tools, phage chemical heterogeneity was mapped and the impact of external stressors (organic solvents, heat, and pH shifts) on two distinct phage morphotypes was assessed.
Results show that phage stability depends on phage type and environmental conditions. For instance, myoviruses tolerated moderate organic solvent levels better than podoviruses, whereas severe heat or acid stress caused capsid damage and genome release, especially in short-tailed phages. Building on these insights, an electrostatically driven liposomal encapsulation method was developed to improve formulation efficiency.
Mixing cationic lipids with phage suspensions in scalable microfluidic systems produced uniformly nanosized liposome–phage formulations with ~90% encapsulation efficiency and preserved infectivity. Encapsulation also protected phages during nebulization, minimizing titer loss and enabling efficient lung delivery. Collectively, this work provides a framework—from molecular characterization to formulation and aerosol delivery—for enhancing phage stability and efficacy, advancing inhalable phage therapeutics against multidrug-resistant bacterial infections
Designing and Evaluating Digital Planning Tools to Support Preparation for Peer Tutoring
No full textThis research explores the design and evaluation of two digital planning tools that support peer
tutoring preparation. Peer tutoring is widely recognised for its potential to enhance learning,
yet its modest effects indicate room for improvement. For student tutors having neither deep
domain nor pedagogical knowledge, better preparation is the only strategy for addressing these
challenges. Despite evidence that preparing to teach can promote deep learning, the preparation
phase remains empirically under-researched. The study was thus motivated by this gap, aiming
to enhance individual tutor learning and collective knowledge construction through supporting
the design of tutoring plans.
This project employed a design-based research approach and conducted two iterative studies
in science education. Guided by a conjecture map framework, the first study developed tuPlan,
a dialogue-based planning tool that supported student tutors with structured prompts and visual
graphs. An experimental comparison of 50 tuPlans with 50 slide-based plans found that tuPlans
contained more tutoring elements, such as question-answer turns and varied guidance levels.
Qualitative results revealed that tuPlans more frequently incorporated evaluative thinking and
metacognitive knowledge than the slide decks. Nonetheless, there were also considerable
differences in quality within the tuPlan group.
Building on the findings and lessons learned, the second study developed tuMap, which
integrated additional prompts such as goal settings and reflections on guidance and difficulties.
An iterative comparison of 50 tuMaps with 50 tuPlans indicated that tuMap led to a higher
frequency of metacognitive regulation. Large language model-assisted qualitative coding,
grounded in Bloom’s taxonomy, also indicated a greater presence of higher-order cognitive
processes and conceptual knowledge in tuMap plans. Moreover, tuMap plans appeared more
coherent quality within the group.
This study contributes to the learning sciences by illustrating how structured dialogue planning
supports a transition in peer tutors from knowledge telling towards deeper processes of
knowledge construction. These findings suggest implications for peer-assisted learning in
higher education and the training of peer tutors. Given the limitation of focusing on a single
content domain and exclusively on the planning phase, future research will expand the
evaluation to cross-disciplinary contexts and incorporate additional knowledge technologies to
advance the tool design for supporting one-to-one tutoring sessions
Software pipelines from the 3D PAWC & constraint mapping project that process soil analysis data and proximal data to create automated models which produce maps of soil properties and soil constraints to depth
No full textThe 3D PAWC and constraints mapping project utilised R to create software pipelines that process field boundaries, access privately available proximal surveys, and download private and publicly available terrain and climate data. These datasets are stored as a datacube which is used in the following 3 steps of the project: producing a stratified random sample design, summarising lab analysis results to report to growers, and running an automated modelling process to map soil properties.
Within each section, there is a working script that can run on a predefined farm from the project.
Sample design: This pipeline transforms the compiled datacube into strata across a farm and then randomly samples these strata given a predefined sample size. The R code can be made available for this summary reporting process, subject to an agreement with the University of Sydney and the GRDC.
Models and mapping: This pipeline extracts covariates from a farm's datacube to the point locations of lab analysis sites. For any measured soil property, several models are compared for prediction quality over analysis depth intervals (0-15 cm, 15-30 cm, 30-60 cm, 60-100 cm). The best performing model is selected for each soil property and used to produce maps across the sampled fields of the farm. The R code can be made available for this automated modelling, subject to an agreement with the University of Sydney and the GRDC.
Files are stored in .rmd format and require input from .csv files.
The software pipelines are stored on the USYD-RDS at \\shared.sydney.edu.au\research-data\PRJ-MLCons. Data access is restricted as the code links to private APIs with access to restricted and sensitive private farm data. Third-parties will need to request access from GRDC and the University of Sydney.
For further enquiries, please contact Dr Patrick Filippi at [email protected]