We study the Student Project Allocation problem with lecturer preferences over students (spa-s), which involves the assignment of students to projects based on student preferences over projects, lecturer preferences over students, and capacity constraints on both projects and lecturers. The goal is to find a stable matching that ensures no student and lecturer can mutually benefit by deviating from a given assignment to form an alternative arrangement involving some project. We explore the structural properties of spa-s and characterise the set of stable matchings for an arbitrary spa-s instance. We prove that, similar to the classical Stable Marriage problem (sm) and the Hospital Residents problem (hr), the set of all stable matchings in spa-s forms a distributive lattice. In this lattice, the student-optimal and lecturer-optimal stable matchings represent the minimum and maximum elements, respectively. Our results extend known structural characterisations from bipartite models to the more complex spa-s setting, and provide a basis for the development of efficient algorithms to address several open problems in spa-s and its extensions.

As computing education grows rapidly across the globe, there is an increasing need to broaden participation and engage all students in computing, particularly those from underrepresented groups and developing countries. A programming workshop that uses various interventions to broaden participation was set up to empower African university students with computer programming skills to address this need. Out of 487 applications, 172 participants from 11 African countries were selected to participate in the workshop. This paper aims to explore the participants' experiences, including their motivation for attending the workshop, their programming skills confidence, what they found most useful for their learning, and the challenges they faced. Employing a mixed-methods design, our quantitative and qualitative results indicate that participants' motivations were more intrinsic. Furthermore, the results indicate that participants' confidence increased after the workshop. They found the hands-on sessions with the tutors to be most beneficial to their learning. We also observed that many participants struggled with access to basic ICT resources during the workshop, even though they were provided with the internet. Our findings highlight that participants are interested in learning programming; therefore, to support them, sustainable collaborative partnerships are necessary to provide relevant teaching interventions and resources.

We study the Student-Project Allocation problem with lecturer preferences over Projects (spa-p). In this context it is known that stable matchings can have different sizes and the problem of finding a maximum size stable matching is NP-hard. There are two known approximation algorithms for max-spa-p, with performance guarantees 2 and 32. We show that max-spa-p is polynomial-time solvable if there is only one lecturer involved, and NP-hard to approximate within some constant c>1 if there are two lecturers involved. We also show that this problem remains NP-hard if each preference list is of length at most 3, with an arbitrary number of lecturers. We then describe an Integer Programming (IP) model to enable max-spa-p to be solved optimally in the general case. Following this, we present results arising from an empirical evaluation that investigates how the solutions produced by the approximation algorithms compare to optimal solutions obtained from the IP model, with respect to the size of the stable matchings constructed, on instances that are both randomly-generated and derived from real datasets.