This paper investigates the impact of self-revision quizzes on student engagement and reflective learning in a Transnational Education (TNE) programme module. Designed around Kolb's Experiential Learning Cycle, the quizzes em-phasise four stages: concrete experience, reflective observation, abstract conceptualisation, and active experimentation, encour-aging students to identify knowledge gaps and apply feedback iteratively. Reflective learning supports metacognition and self-assessment, helping students enhance engagement and deepen their understanding of complex topics. Introduced in 2020/21, the self-revision quizzes provided immediate feedback with brief validation for correct answers and detailed explanations for incorrect ones, guiding students back to relevant teaching materials. Questions were based on recurring queries in QMPlus (Queen Mary's Virtual Learning Environment) and in-class discussions, targeting challenging areas of the module. Designed as formative assessments, the quizzes allowed multiple attempts to promote continuous revision. Over four years (2020/21 to 2023/24), quiz timing and reminders were adjusted to maximise participation. Results show that engagement varied between 25% and 57% per year, with the highest engagement linked to well-timed quizzes before assessments and multiple reminders. Feedback from the 2023/24 cohort revealed 55% of respondents found the quizzes very helpful for clarifying concepts, while 39% found them somewhat helpful but acknowledged the need for additional practice. Moreover, students who engaged with the quizzes consistently performed better in both final exams and the coursework. This study highlights the potential of self-revision quizzes to enhance engagement and prepare students for assessments such as exams, particularly in TNE contexts. It contributes to formative assessment research by showcasing how reflective learning tools can drive continuous learning. Plans are underway to integrate Generative AI for tailored feedback and quiz automation, reducing academic workload and expanding applicability to other modules.

The Joint Programme/Joint Education Institute (JP/JEI) Summer School initiative, developed in collaboration with Amazon Web Services (AWS) Education to Workforce (E2W), uniquely addresses global cloud computing skill gaps by redesigning and implementing a holistic curriculum. The summer school program aims to enhance both technical and soft skills among students to meet current and future industry demands. To meet the fast-changing skills gaps in the cloud industry, we consistently update the syllabus every year to include developments in solutions architecture, software development, and data engineering at the associate level of AWS certification qualifications. It incorporates insights from in-depth labour market surveys conducted in three countries and leverages expertise from AWS professionals and industry partners. In addition, graduates receive a digital HEAR (Higher Education Achievement Report) transcript, showcasing the transferable lifelong skills acquired beyond their conventional academic coursework, thereby amplifying their employability. Since its inception in 2019, the program has garnered 90% positive feedback from students and employers. In developing the E2W initiative, we created a model based on five strategic steps namely leadership support to bring on board the JP/JEI leaders; curriculum alignment with the in-demand industry skills gaps; faculty development to enhance the skills development programme of JP/JEI staff that will be involved in delivering the training; career support that involved experts from the career service department and employer engagement in which we involved employers of such in-demand industry skills in cloud computing. We then implemented the E2W framework in four stages within six months. The implementation stages are based on strategic planning process, programme building, course content creation and launching of the summer school training. Having armed the students with industry-based skills with academic and professional qualifications, we organised a career fair in collaboration with AWS, which attracted 17 companies and employers with 375 students attending, out of which 40% were invited for job interviews. The graduates that got jobs through this programme are now participating in the training to encourage and mentor our students to take this training opportunity to enhance their employability skills. This program presents a valuable model for workforce development in the cloud computing industry through innovative education-industry collaboration.

This paper presents an AI-assisted approach that leverages Multimodal Large Language Models (MLLMs) to automate the generation of Multiple-Choice Questions (MCQs) for modules in engineering education. The system introduces a LOs extraction to MCQs generation pipeline, which extracts Learning Outcomes (LOs) from provided lecture notes and generates relevant MCQs with solutions and explanations based on the extracted LOs. By harnessing MLLMs' capabilities in vision and text comprehension, coupled with carefully crafted prompts from human educators, the tool efficiently produces context-relevant MCQs that can streamline teaching material development. The effectiveness of this AI-powered MCQ generation pipeline is investigated through experiments across a number of engineering modules with evaluations on the quality of the generated MCQs by human educators. The analysis of the evaluation results shows the AI tool's ability to generate MCQs that are well-aligned with LOs and exhibit strong contextual relevance, demonstrating the potential of AI-assisted approaches to enhance the efficiency of creating high-quality MCQs in engineering education. However, the variability in quality ratings across different aspects underscores the continued need for human expertise and oversight in the assessment design process. The findings provide useful insights into the capabilities and limitations of state-of-the-art multimodal language models in supporting assessment development in engineering education.

Multi-access Edge Computing (MEC) is an emerging paradigm designed to provide storage, computing and communication capabilities in the proximity of end-user devices. This approach facilitates the deployment of real-time on mobile devices with limited capabilities. To realize the MEC goals, it is essential to effectively manage and offload computing tasks to both edge and cloud-based resources. However, the dynamic nature, uncertainty and mobility within edge computing environments pose significant challenges to resource management. Furthermore, the inherent software and hardware heterogeneity, coupled with the distributed nature of architecture, complicates the development of efficient task offloading strategies that can adeptly manage resources across both edge and cloud platforms. In this paper, we propose a cluster-based task edge orchestrator, where edge servers are grouped based on service demands, resource ability and other factors to improve the overall service. Our proposed method leverages the K-Medoids clustering algorithm to dynamically form clusters of suitable edge servers for offloading computing tasks with minimum response time. To validate our proposed solution, we have orchestrated a comprehensive series of tests using EdgeCloudSim. Results show that our approach outperforms its competitor in terms of average service time by around 8 %.

AI-empowered Cognitive Radio (i.e., AI-enabled radios) is a paradigm shift to achieve the highest level of Self-Awareness in future wireless communications. This work proposes a joint automatic modulation conversion and classification (AMCC) framework, which allows an AI-enabled wireless node to predict signals' dynamics of different modulation schemes and explain how it can be transported (converted) with minimal effort and forwarded with higher spectral efficiency. To achieve this goal, we propose a Generalized Filtering framework integrated by Transport Planning to learn the way of converting low-order modulations to high-order modulations, which has also been validated by performing the automatic modulation classification. Simulation results demonstrate the effective performance of our novel framework on converting and classifying multiple modulation formats.

In multi-access edge computing (MEC), most existing task software caching works focus on statically caching data at the network edge, which may hardly preserve high reusability due to the time-varying user requests in practice. To this end, this work considers dynamic task software caching at the MEC server to assist users’ task execution. Specifically, we formulate a joint task software caching update (TSCU) and computation offloading (COMO) problem to minimize users’ energy consumption while guaranteeing delay constraints, where the limited cache size and computation capability of the MEC server, as well as the time-varying task demand of users are investigated. This problem is proved to be non-deterministic polynomial-time hard, so we transform it into two sub-problems according to their temporal correlations, i.e., the real-time COMO problem and the Markov decision process-based TSCU problem. We first model the COMO problem as a multi-user game and propose a decentralized algorithm to address its Nash equilibrium solution. We then propose a double deep Q-network (DDQN)-based method to solve the TSCU policy. To reduce the computation complexity and convergence time, we provide a new design for the deep neural network (DNN) in DDQN, named state coding and action aggregation (SCAA). In SCAA-DNN, we introduce a dropout mechanism in the input layer to code users’ activity states. Additionally, at the output layer, we devise a two-layer architecture to dynamically aggregate caching actions, which is able to solve the huge state-action space problem. Simulation results show that the proposed solution outperforms existing schemes, saving over 12% energy, and converges with fewer training episodes.

Unmanned Aerial Vehicles (UAVs) attracted both industry and research community owing to their fascinating features like mobility, deployment flexibility and strong Line of Sight (LoS) links. The integration of Cognitive Radio (CR) can greatly help UAVs to overcome several issues especially spectrum scarcity. However, the dynamic radio environment in CR and the strong dependence of safe communications from LoS channels integrity in UAV communications make the Cognitive- UAV-Radio vulnerable to jamming attacks. This work aims to study the integration of CR and UAVs introducing a Self- Awareness (SA) framework from the physical layer security perspective. Under the SA framework, a Dynamic Bayesian Network (DBN) model is proposed as a representation of the radio environment and a modified Markov Jump Particle Filter (MJPF) is employed for prediction and state estimation purposes. A novel jammer detection framework is proposed that allows the UAV to perform abnormality evaluation at different hierarchical levels. The jammer is shown to be located effectively in both time and frequency domains. Experimental results show the effectiveness of the proposed framework in terms of detection probability and accuracy.

Wireless backhauling is recognised as a main contender for connecting small cells to the core network during the rise of 5G, especially in the absence of last mile fibre optic links. However, wireless backhauls present serious competition for the finite radio resources traditionally employed for radio access and are increasingly in demand due to exponential data growth. This work proposes a reinforcement learning approach that dynamically adjusts the sharing of radio resources between backhaul and radio access depending on the subtleties of the users' requirements and the network conditions. The metrics governing the reinforcement learning techniques are both usercentric and network-centric. They aim to maximise the network throughput while satisfying the differing requirements of users and corresponding applications. Our results indicate significant gains on the network performance (up to 23% throughput improvement) and the users' satisfaction (up to 15% improvement with respect to latency) as compared to static spectrum sharing methods for wireless backhaul.

Recently, base station (BS) sleeping has emerged as a viable conservation strategy for energy efficient communication networks. Switching-off particular BS during low-traffic periods requires the load to be sufficiently low so user performance is not compromised. There remain however, network energy saving opportunities during medium-to-high traffic periods if BSs operate in scalable fashion, which involves deploying multiple BSs with different power modes, i.e., macro/microcells, which are co-located in each cell. In this paper, a new scalable multimode BS switching (MMBS) cellular model is presented where depending on the traffic load, each BS operates in multimode: active, low-power and sleep, so the model dimensions network capacity by dynamically switching modes to minimise energy consumption. Results corroborate that the MMBS model reduces energy consumption by more than 50% during low-traffic and up to 9% during high-traffic conditions, thereby significantly improving the energy efficiency compared with the always-on and existing BS sleeping approaches.