Approaches to learning : perceptions about Chinese international undergraduates in Australian Universities

Chinese students constitute the largest cohort of international undergraduates in Australian universities, comprising 37.3% in 2019. However, there is a scarcity of research examining perceptions of how Chinese international students (CIS) learn in Australian universities, from the broader context of the students themselves, their Australian teachers and Australian domestic student (ADS) counterparts. Drawing on the 3P (Presage-Process-Product) framework by Biggs, Kember, and Leung (2001), this thesis explored the perceptions of CIS, and their lecturers and classmates regarding their approaches to learning in Australian universities. Utilising a mixed methods approach (Creswell, 2014), surveys were conducted with 156 CIS and 212 ADS incorporating a validated survey by Biggs et al. (2001) called the R-SPQ-2F. Interviews were also conducted with 10 CIS and 10 Australian academics from two Australian universities, one regional and the other metropolitan. The findings demonstrated that perceptions of CIS were characterised by a unique learning structure that differed from ADS in a number of ways, particularly in relation to group learning, the use of understanding and memorisation strategies, and classroom engagement. It was noted that these disparities did not support the generally held view of CIS as mainly surface oriented learners who preferred rote-learning techniques (Grimshaw, 2007). While adopting similar levels to ADS of deep approach strategies in their learning, CIS also used more surface and achieving approaches than ADS, and tended to incorporate memorising with understanding in their learning process. However, it was also evident that the approaches used by CIS in Australia were often more complex than what was easily observed. For instance, their reticence in class was not necessarily indicative of passive learning, but instead, suggestive of the complexity of context that needs to encompass the ‘whole being’ of these students, i.e., their personality, culture, and most of all, the dynamics of their perceived approaches to their learning. This study also investigated negotiations that occurred between CIS and their Australian lecturers. While CIS’ learning approaches were greatly shaped and determined by academics’ instructional decisions involving curriculum, teaching patterns and assessment procedures, it was also found that academics’ instructional activities were reshaped and counter-determined by CIS’ learning approaches. As a result, a Co-constructed Model of Learning and Teaching (CMLT) for CIS in Australian universities, based on the 3P framework (Biggs et al., 2001), was developed to assist future education experiences for international students. This study is significant in that it has given voice to Chinese students, enabling a greater understanding of their experiences in Australian universities to emerge, in conjunction with and supplemented by insights provided by their Australian student counterparts and educators. It has enabled both international and domestic students the opportunity to reflect on possible cultural impacts on learning, hopefully improving their capacities to act as effective global citizens. It has also afforded an opportunity for academics to reflect on their beliefs and practices in relation to teaching diverse student cohorts, which will hopefully deepen their understanding of the complexities that come with the increasing globalisation of education.Doctor of Philosoph

Parameter Estimation for a Sinusoidal Signal with a Time-Varying Amplitude

This paper addresses the parameter estimation problem of a non-stationary sinusoidal signal with a timevarying amplitude, which is given by a known function of time multiplied by an unknown constant coefficient. A robust estimation algorithm is proposed for identifying the unknown frequency and the amplitude coefficient in real-time. The estimation algorithm is constructed based on the Volterra integral operator with suitably designed kernels and sliding mode adaptation laws. It is shown that the parameter estimation error converges to zero within an arbitrarily small finite time, and the robustness against bounded additive disturbances is certified by bounded-input-bounded-output arguments. The effectiveness of the estimation technique is evaluated and compared with other existing tools through numerical simulations

Deep or surface learning? Perceptions of Chinese international and local students in Australian universities

Despite the COVID-19 pandemic, Chinese international students (CIS) still constitute the largest international population in Australian higher education. Yet limited research has examined the lived learning experience of CIS and local students in Australian universities. Underpinned by Biggs, Kember and Leung’s (2001) 3P model of learning, this article explores the perceptions of CIS regarding their approaches to learning in Australian universities, as compared with Australian domestic students (ADS). Surveys incorporating the Revised Study Process Questionnaire (R-SPQ-2F) were conducted with 156 CIS and 212 ADS from two Australian universities. The findings demonstrated that perceived disparities existed between the two cohorts in terms of their approaches to learning. These disparities, however, did not support the well-documented view of CIS as mainly surface oriented learners but rather as more rounded learners than ADS in their learning approaches. This study gave voice to CIS to reflect on their learning in Australian universities, in conjunction with and supplemented by insights provided by their Australian student counterparts. It also enabled a greater understanding of CIS learning in Western universities, particularly in Australian universities. © 2022, Western Australian Institute for Educational Research Inc.. All rights reserved

Fixed-Time Convergent Distributed Observer Design of Linear Systems: A Kernel-Based Approach

The robust distributed state estimation for a class of continuous-time linear time-invariant systems is achieved by a novel kernel-based distributed observer, which, for the first time, ensures fixed-time convergence properties. The communication network between the agents is prescribed by a directed graph in which each node involves a fixed-time convergent estimator. The local observer estimates and broadcasts the observable states among neighbours so that the full state vector can be recovered at each node and the estimation error reaches zero after a predefined fixed time in the absence of perturbation. This represents a new distributed estimation framework that enables faster convergence speed and further reduced information exchange compared to a conventional Luenberger-like approach. The ubiquitous timevarying communication delay across the network is suitably compensated by a prediction scheme. Moreover, the robustness of the algorithm in the presence of bounded measurement and process noise is characterised. Numerical simulations and comparisons demonstrate the effectiveness of the observer and its advantages over the existing methods

Robust Adaptive Learning-based Path Tracking Control of Autonomous Vehicles under Uncertain Driving Environments

This paper investigates the path tracking control problem of autonomous vehicles subject to modelling uncertainties and external disturbances. The problem is approached by employing a 2-degree of freedom vehicle model, which is reformulated into a newly defined parametric form with the system uncertainties being lumped into an unknown parametric vector. On top of the parametric system representation, a novel robust adaptive learning control (RALC) approach is then developed, which estimates the system uncertainties through iterative learning while treating the external disturbances by adopting a robust term. It is shown that the proposed approach is able to improve the lateral tracking performance gradually through learning from previous control experiences, despite only partial knowledge of the vehicle dynamics being available. It is noteworthy that a novel technique targeting at the non-square input distribution matrix is employed so as to deal with the under-actuation property of the vehicle dynamics, which extends the adaptive learning control theory from square systems to non-square systems. Moreover, the convergence properties of the RALC algorithm are analysed under the framework of Lyapunov-like theory by virtue of the composite energy function and the λ-norm. The effectiveness of the proposed control scheme is verified by representative simulation examples and comparisons with existing methods

Distributed Model Predictive Control for Heterogeneous Vehicle Platoon with Inter-Vehicular Spacing Constraints

This paper proposes a distributed control scheme for a platoon of heterogeneous vehicles based on the mechanism of model predictive control (MPC). The platoon composes of a group of vehicles interacting with each other via inter-vehicular spacing constraints, to avoid collision and reduce communication latency, and aims to make multiple vehicles driving on the same lane safely with a close range and the same velocity. Each vehicle is subject to both state constraints and input constraints, communicates only with neighboring vehicles, and may not know a priori desired setpoint. We divide the computation of control inputs into several local optimization problems based on each vehicle’s local information. To compute the control input of each vehicle based on local information, a distributed computing method must be adopted and thus the coupled constraint is required to be decoupled. This is achieved by introducing the reference state trajectories from neighboring vehicles for each vehicle and by employing the interactive structure of computing local problems of vehicles with odd indices and even indices. It is shown that the feasibility of MPC optimization problems is achieved at all time steps based on tailored terminal inequality constraints, and the asymptotic stability of each vehicle to the desired trajectory is guaranteed even under a single iteration between vehicles at each time. Finally, a comparison simulation is conducted to demonstrate the effectiveness of the proposed distributed MPC method for heterogeneous vehicle control with respect to normal and extreme scenarios

Integrated Thermal and Energy Management of Connected Hybrid Electric Vehicles Using Deep Reinforcement Learning

The climate-adaptive energy management system holds promising potential for harnessing the concealed energy-saving capabilities of connected plug-in hybrid electric vehicles. This research focuses on exploring the synergistic effects of artificial intelligence control and traffic preview to enhance the performance of the energy management system (EMS). A high-fidelity model of a multi-mode connected PHEV is calibrated using experimental data as a foundation. Subsequently, a model-free multistate deep reinforcement learning (DRL) algorithm is proposed to develop the integrated thermal and energy management (ITEM) system, incorporating features of engine smart warm-up and engine-assisted heating for cold climate conditions. The optimality and adaptability of the proposed system is evaluated through both offline tests and online hardware-in-the-loop tests, encompassing a homologation driving cycle and a real-world driving cycle in China with real-time traffic data. The results demonstrate that ITEM achieves a close to dynamic programming fuel economy performance with a margin of 93.7%, while reducing fuel consumption ranging from 2.2% to 9.6% as ambient temperature decreases from 15°C to -15°C in comparison to state-of-the-art DRL-based EMS solutions