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.

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.

Conventional lectures without engagement are not always quite effective in facilitating learning, and students often lose their learning interest in these eventless sessions. A solution to this could be creating an active learning environment by incorporating cooperative and interactive activities into conventional lectures. Live/Online Student Response Systems (OSRS) are excellent tools to serve that purpose. In this paper, a collaborative learning approach for enhanced learning experience and performance by using OSRS from different perspectives has been explored. The aim is to illustrate that this method can provide a complementary pedagogical approach to the traditional didactic lecture format and offer greater class engagement, improved learning outcomes, higher attendance rates, and higher student satisfaction when applied in different delivery setups in pre-covid, during covid, and post-covid classroom environments.