Tuwhera Research Repository

The Tuwhera Research Repository provides sustainable open access and archiving to a broad range of AUT produced research - theses, research outputs and datasets.

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Recent Submissions

Access status: Open Access ,
Integrated Design Practice: A Reflection on Combining Animation, Visual Effects and Game Design for First Year Undergraduate Students
(Association for Computing Machinery (ACM), 2025-12-14) Ramsay, Emily; Todoroki, Masaya; Ung, Lesley; Komura, T; Wang, M
This paper reflects on the transition from the legacy Digital Design major to the Animation, Visual Effects, and Game Design (AVG) degree at Auckland University of Technology (AUT). It examines how first-year papers DESN511 and DESN512 support student engagement and pathway selection through inclusive, interdisciplinary, and culturally grounded teaching. Students explore animation, VFX, and game design through hands-on, concept-led projects that centre identity, storytelling, and experimentation. Activities such as collaborative storytelling and tactile self-portraiture foster connection and inclusion, aligning with Mātauranga Māori and the Bachelor of Design's educational philosophy. These experiences help students feel seen and supported, with reflective annotations and anecdotal feedback indicating increased confidence and clarity in creative direction. The paper also addresses challenges including cohort growth, neurodiversity, and staffing needs, proposing scalable strategies that balance foundational skill development with creative freedom. Animation is positioned as a shared language across disciplines, while VFX and game design offer entry points into narrative and interactive expression. By embedding writing into creative practice and fostering a culture of reflection, the AVG curriculum supports identity formation and interdisciplinary fluency, contributing to broader conversations about inclusive and future-focused design education.
Access status: Open Access ,
Application of Machine Learning Techniques in the Measurement of Ground Reaction Force (GRF) Waveforms Using Inertial Measurement Unit (IMU) Sensors-A Systematic Literature Review
(IOP Publishing, 2026-10-13) Akhter, Malik Naveed; Ali, Haider; Khan Niazi, Imran; Manj, Umair; Taylor, Denise
Three-dimensional ground reaction forces (GRFs) are important parameters in human motion analysis, yet their measurement is confined to laboratory settings. GRF estimation from wearable inertial measurement unit (IMU) data using machine learning and deep learning has emerged as a promising avenue for kinetic analysis outside the laboratory, but no consensus on standardized methods or best practices has emerged. This review synthesizes existing methods and provides a framework of current practices and recommendations for future work. The review followed PRISMA guidelines (PROSPERO CRD42024578576) and searched PubMed, Scopus, IEEE Xplore, and ScienceDirect from 2011 to 2026. Studies were included if they used IMUs and machine learning or deep learning to estimate GRFs and reported quantitative performance metrics. Data were extracted on participant characteristics, IMU specifications and placement, preprocessing, models, validation protocols, and error metrics. Twenty-six studies met the inclusion criteria, with sample sizes ranging from 5 to 47 (median = 15) and IMU counts ranging from 1-16. Most studies used triaxial accelerometers and gyroscopes sampled at 100-1000 Hz. Vertical GRF was generally estimated more accurately than the anteroposterior and mediolateral components within the same study. A single cross-study accuracy ranking was not defensible because studies used different normalization denominators, evaluation windows, tasks, sensor configurations, and validation strategies. Heterogeneity in validation protocols and demographic samples limited direct cross-study comparisons. This review synthesizes how implementation choices, including model architecture, feature-engineering strategy, sensor configuration, and validation protocol, shape GRF prediction across axes. The variability observed underscores the need for standardized methodologies, larger and more diverse cohorts, consistent performance metrics, and benchmarking on public datasets to enable fair cross-study comparisons and accelerate clinical translation.
Access status: Open Access ,
Femininities, School Balls, and New Materialism
(Springer Nature Switzerland, 2024-12-24) Ingram, Toni
[From Introduction] Schools are central players in the construction of femininities, actively shaping and regulating girls’ understandings, bodies, and behavior (Paechter, 2006). Schooling environments are a complicated terrain where girls are required to navigate multiple, often contradictory, discourses of femininity and girlhood. These negotiations and tensions frequently play out at a visceral bodily level where discourses of girlpower (Aapola et al., 2005) sit alongside ideas of ‘appropriate’ femininity and “ladylike” behavior (Allan, 2009), shaping how girls are expected to look, dress, and behave. Girls’ bodies are prime subjects of school rules, critique, admiration, and surveillance, where expectations and judgment largely cohere to dominant gender and sexual norms. Yet at the same time, school girl bodies function as a powerful means for the expression of personal style, identity, confidence, and desire (Pomerantz, 2008); potentialities that can generate a sense of resistance to dominant expectations and feminine norms. School balls and proms are fertile sites for these complex negotiations. Discourses of gender and sexuality are intimately entangled with the materiality of girls’ bodies, clothing, and embodied practices – all of which are integral components within the landscape of sexuality education. By focusing specifically on school balls, it is possible to trace key theoretical approaches for conceptualizing femininities and the materialization of girls’ bodies. In doing so, what these approaches offer for understanding agency and resistance within the sphere of research about sexualities at school is considered.
Access status: Open Access ,
Navigating the Ethical and Societal Impacts of Generative AI in Higher Computing Education
(Association for Computing Machinery, 2026-02-12) Mak, Janice; Nakatumba-Nabende, Joyce; Clear, Tony; Clear, Alison; Abluwi, Ibrahim; Andrei, Oana; Angeli, Lorenzo; MacNeil, Stephen; Oyelere, Solomon Sunday; Hale Rattigan, Matthew; Sheard, Judy; Zhu, Tingting
Generative AI (GenAI) presents societal and ethical challenges related to equity, academic integrity, bias, and data provenance. In this paper, we outline the goals, methodology and deliverables of the project, considering the ethical and societal impacts of GenAI in higher computing education. A systematic literature review that addresses a wide set of issues and topics covering the rapidly emerging technology of GenAI from the perspective of its ethical and societal impacts is presented. This paper then presents an evaluation of a broad international review of a set of university adoption, guidelines, and policies related to the use of GenAI and the implications for computing education. The Ethical and Societal Impacts-Framework (ESI-Framework), derived from the literature and policy review and evaluation, outlines the ethical and societal impacts of GenAI in computing education. This work synthesizes existing research and considers the implications for computing higher education. Educators, computing professionals and policy makers facing dilemmas in the integration of Generative AI in their contexts, may use this framework to guide decision-making in the age of GenAI.
Access status: Open Access ,
POI Location-Aware Services Package Recommendation for Random Groups
(Springer Science and Business Media LLC, 2026-10-15) Shang, Ling; Song, Xiaoyu; Liu, Zhizong; Zhang, Kai; Sheng, Quan Z; Yu, Jian
Recently, with the rapid growth of Point of Interest (POIs) and increasing of social activities, more and more individuals tend to join parties or trips with groups formed randomly (named random groups, RGs). Although some works have proposed wonderful POI recommendation methods for RGs, they failed to consider how to recommend service packages for RGs after they accept the recommended POIs, which significantly degrades user experience. To address this issue, this work proposes a POI location-aware service package recommendation model for random groups (termed PLSPR-RG). First of all, PLSPR-RG generates fitted feature representations of random groups with an enhanced feature learning method. Then, it selects users with similar characteristics to the random group and utilizes service usage data of similar users as the training dataset for PLSPR-RG. Subsequently, PLSPR-RG constructs a service labeled bipartite graph of similar users, then it employs an edge-enhanced bipartite graph neural network with multi-view contrastive learning (EBGNN-CL) to learn service feature representations that capturing both positive and negative interaction preferences of similar users. Furthermore, PLSPR-RG builds a directed service transition graph based on the service interaction records of similar users and adopts the SRGNN network to learn service feature representations containing latent user preferences, as well as user preference representations. Finally, based on the learned service representations and the fitted features of random groups, PLSPR-RG generates a ranked list of recommended services, thereby completing POI location-aware service package recommendation for random groups. Extensive experiments have been conducted on three public datasets (Foursquare, Gowalla and Yelp dataset), which have proved that our proposed model outperforms the state-of-the-art models.