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.

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.

Understanding students' academic profiles and skillsets is crucial for personalized guidance in higher education. In transnational education (TNE) programmes, large student cohorts and time zone differences often complicate the allocation process of final year projects. To address these challenges, a project recommendation framework was developed. Using latent semantic analysis (LSA), students' academic profiles are summarized into skillsets, which are then matched with project requirements. This framework was deployed in a TNE programme between Queen Mary University of London (QMUL) and Beijing University of Posts and Telecommunications (BUPT). Quantitative results show that 80% of students using the framework secured a project on the first day of the allocation process, compared to 64% in a previous cohort without the tool, effectively shortening the allocation timeline. Qualitative feedback indicates high student satisfaction, emphasizing the tool's ease of use and relevance, as well as its ability to help students identify projects aligned with their academic profiles and interests. These findings reflect the framework's potential to streamline project allocation, reduce administrative workload, and enhance student support in project allocation. Moreover, the student skillsets generated by the framework can support broader applications, including employability analysis, academic profiling, and strategic decision-making to enhance institutional processes and student outcomes.

We propose HyperIV, a novel approach for real-time implied volatility smoothing that eliminates the need for traditional calibration procedures. Our method employs a hypernetwork to generate parameters for a compact neural network that constructs complete volatility surfaces within 2 milliseconds, using only 9 market observations. Moreover, the generated surfaces are guaranteed to be free of static arbitrage. Extensive experiments across 8 index options demonstrate that HyperIV achieves superior accuracy compared to existing methods while maintaining computational efficiency. The model also exhibits strong cross-asset generalization capabilities, indicating broader applicability across different market instruments. These key features -- rapid adaptation to market conditions, guaranteed absence of arbitrage, and minimal data requirements -- make HyperIV particularly valuable for real-time trading applications. We make code available at https://github.com/qmfin/hyperiv.

The capstone project is a crucial element of a degree programme and plays a vital role in the growth of learners, as it enables them to enhance their problem-solving skills and improve their employability prospects. In addition to this, the project provides the learners with an opportunity to demonstrate and showcase their critical thinking abilities and creativity. However, due to the year-long independent nature of these projects, learners can disengage due to a lack of motivation or self-regulated skills throughout the project. To address this problem, we formulated a data-driven intervention approach that conducts learner engagement analytics to identify and support disengaged learners, ensuring they maximise the benefits of completing a capstone project. The motivation was also to provide these learners with the necessary resources and support to get them back on track. This approach was implemented in the capstone projects conducted by learners at Queen Mary University of London within the School of Electronic Engineering and Computer Science. Based on the data of the three cohorts in 2020–21, 2021–22 and 2022–23, our analysis shows that the proposed data-driven intervention approach for capstone projects can effectively identify less-engaged learners and targeted interventions are shown to improve the overall performance of these less-engaged learners on capstone projects.

This research full paper proposes a novel data-driven approach for programme review that leverages module assessment data in an undergraduate engineering degree programme and graph theory concepts. The approach involves constructing a curriculum correlation graph, where nodes represent modules and edge weights are determined by correlation coefficients between assessment results of all modules in the engineering programme. Based on the curriculum correlation graph, graph-theoretic techniques and metrics, such as the minimum spanning tree, clustering coefficients and centrality measures, are employed to perform quantitative analyses, which evaluate the coherence of the programme's curriculum delivery. Furthermore, the approach facilitates a quantitative evaluation of the alignment between the programme's intended curriculum structure, as encapsulated in the designed curriculum graph, and its actual delivery, represented by the curriculum correlation graph. By comparing centrality measures between these two graphs, the approach highlights areas where the programme's curriculum delivery may deviate from its original design expectations, allowing targeted interventions to address potential misalignments. The proposed approach is applied to a UK-China transnational education undergraduate engineering degree programme. The analysis results demonstrate the effectiveness of the proposed data-driven approach in providing comprehensive and quantitative insights into the programme's curriculum design and delivery. By leveraging the power of graph theory and data analysis techniques, this approach offers a valuable tool for programme review, enabling programme teams in higher education institutions to identify both strengths and potential discrepancies in the alignment between a programme's curriculum delivery and its original design expectations, so that informed decision and targeted efforts can be made towards continuous improvement and enhancement of the academic degree programme.

The study of microstructure of materials is of great significance in the field of materials science. The interdisciplinary cooperation of materials science and computer science makes it more accurate and efficient to explore the relationship between material microstructure and material properties. Machine learning has potential in exploring the relationship between microstructure and properties of materials. This paper proposes adding the morphology features of grains into the construction of grain knowledge graph to enrich the grain information in the graph. First, an autoencoder extracts the grain morphology features and adds them to the grain knowledge graph. Then, the graph convolutional network is used to extract the features of the graph, and the fully connected network is used to predict the properties of the material. Experiments are performed on actual EBSD scanning data. The experimental results show that the proposed method has noticeable improvement over the competing methods.

The new generation of video coding standard, Versatile Video Coding (H.266/VVC), brings tremendous computational complexity by incorporating the quad-tree with nested multi-type tree (QTMT) partition structure. We propose an adaptive low loss fast algorithm to tackle this disadvantage by using the online Support Vector Machine (SVM) classifier. Firstly, we perform a pre-scene-cut detection before encoding the whole sequence to split it into several scenes, which divide frames into training-frame and predicting-frame. Then, the training-frame is used to construct the data set for SVM parameters training. Specifically, we extract partition-related features, i.e., gradient, entropy, and difference of neighbor area depth to train the SVM classifier. Lastly, the partition decision in predicting-frame is accelerated by the SVM classifier model in the same scene with the training-frame. Besides, we control our algorithm to maintain a low Bjontegaard Delta Bit Rate (BDBR) index by applying the SVM classifiers in the most suitable size 32x32. The experimental results show that our algorithm achieves about 15.76% encoding time saving on average with a negligible quality loss-0.23% BDBR increase under all-intra configuration.

In this paper, a QoS differentiation adaptive retransmission limits ARQ (QDARL-ARQ) is proposed to improve the efficiency of retransmission in conventional SR-ARQ for the IEEE 802.16e BWA systems. With a simple algorithm implemented based on the conventional SR-ARQ, QDARL-ARQ scheme is able to dynamically adjust the retransmission limits for services with different characteristics by considering their QoS requirements as well as the current system states simultaneously. This scheme aims to achieve lower packet error rate with restrained end-to-end delay in the time-variable and error prone wireless environment in comparison with conventional SR-ARQ. Several performance metrics of QDARL-ARQ are compared with conventional SR-ARQ in both single service scenarios and multiple services scenarios. The performance improvement due to QDARL-ARQ is evaluated through the IEEE 802.16e system level simulation, and the results clearly show that it can improve the performance of mean end-to-end delay, packet error rate and throughput, especially the retransmission efficiency. It can also be found that the conventional SR-ARQ is in fact a special instance of the QDARL-ARQ designed here.

Relaying transmission is a candidate way to combat wireless channel fading and enlarge the coverage, and efficient radio resource allocation is essential to provide quality-of-service (QoS) for wireless networks. In this paper, an adaptive multiuser radio resource allocation model is proposed for the downlink of OFDM-relay system, which exploits performance gain in both frequency domain and time domain. According to the different transmission modes, two QoS-oriented scheduling algorithms based on the feedback of the channel state information (CSI) of two hops are investigated. One is enhanced proportional fairness (EPF) algorithm, and the other is improved priority (IPRI) algorithm. Both of them can achieve high system throughput and better resource fairness due to the adaptive allocation, especially in the QoS-guarantee aspect for cell edgy users compared with conventional scheduling schemes. The priority strategy is a novel scheme, because of considering resource fairness with artificial starve (AS) state in IPRI, which yields higher spectral efficiency and achieve better data rate requirements for the users.