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.

Personalised learning seeks to provide a tailored and highly effective learning experience, to maximize the unique potential of individual learners. However, despite the potential benefits, the implementation of personalised learning has not significantly materialized within the current structure of higher education institutions. In this paper, we propose a Guided Personalised Learning (GPL) model, specifically designed to facilitate effective interactions between educators and learners. The GPL model empowers learners to develop their tailored learning plans, while enabling educators to adapt their teaching and embrace student-centred pedagogy to address the diverse learning needs of students in the same classroom. We developed prototypes for the practical implementation of the GPL model in two undergraduate engineering courses and conducted initial evaluations of their effectiveness.

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.

Context-awareness is essential to the development of adaptive information systems. Environmental noise can provide a rich source of information about the current context. We describe our approach for automatically sensing and recognising noise from typical environments of daily life, such as office, car and city street. In this paper we present our hidden Markov model based noise classifier. We describe the architecture of the system, compare classification results from the system with human listening tests, and discuss open issues in environmental noise classification for mobile computing. 1.