This innovative practice paper presents a work-inprogress on the design of a 'play-kit' to introduce incoming first-year university students to diverse thinking styles through playful interactions, addressing the need for adaptable problemsolving skills development required to tackle increasingly complex global socio-technical challenges. Our initial design stage involves creating a prototype physical workbook to stimulate computational thinking skills through play. We will adapt lessons from existing computational thinking material, originally designed as a classroom-based tool for primary school students. We customize lessons for university students, and re-work them so that they become self-directed learning activities. Our workbook emphasizes essential computational components - decomposition, algorithms, pattern recognition, logic, representation, and abstraction. In time, the project will offer both physical and online 'Learning to Think' play-kits to widen accessibility and suit a diversity of learning styles.

Generative Artificial Intelligence (AI) and Large Language Models (LLMs) such as ChatGPT are revolutionizing the landscape of learning and teaching. They excel in understanding and creating natural language texts, thereby captivating students with their quick and well-crafted responses. While some perceive AI simply as a tool to reduce workload, our study appreciates these technologies for their ability to beautifully augment human capabilities. In this study, we tasked ChatGPT with designing a relational database for an online food delivery system, similar to an early university computer science assignment. This paper explains the attention mechanism, which is a crucial component in LLMs, enabling them to focus on specific parts of the presented input (prompt) and enhances their ability to ‘understand’ context. Through a series of iterative prompt refinements, we evaluate ChatGPT's effectiveness in developing this database, with a goal to enhance the accuracy and relevance of its responses. Our findings reveal both the benefits and limitations of using LLMs in education, highlighting their potential to significantly enrich the learning experience.

This paper presents a detailed comparative analysis of three approaches to capstone projects in a Software Development Master's program, reflecting on the evolution of software development education at Master's level. Historically centered around a comprehensive individual dissertation, the program recently introduced mini-projects as an alternative, leading to a critical decision between three educational routes. The first approach adheres to the traditional model, with individual dissertations that offer depth and rigor but increase the supervision workload academic staff. The second exclusively adopts mini-projects in state-of-the-art areas such as Data Analysis and Cloud Computing. The mini-project route promotes collaborative and diverse learning experiences. The third, a hybrid approach, provides students the flexibility to choose between the dissertation and mini-projects, accommodating diverse educational and professional goals. Utilizing data on student enrollments and academic performance, the study evaluates the implications of each approach on students, faculty, program outcomes, and employability. This analysis is pivotal in guiding the program's future direction, ensuring alignment with industry demands and effective preparation of students for their professional careers. We find that offering both traditional dissertation and mini-project options is the most advantageous strategy. This dual approach caters to a broader spectrum of student needs and preferences, balancing in-depth research with exposure to varied software development topics. Although this requires additional resources and management, it emerges as the preferred educational route, addressing the contemporary demands of the software development industry. These insights are crucial for shaping the future of capstone projects in Software Development Master's programs,

This paper explores the complexities of implementing a student-centered approach within a software engineering conversion degree. It addresses the challenges and opportunities presented by students with diverse backgrounds and expectations, as well as varying levels of prior experience. Through a comprehensive study, surprising levels of previous experience among students were revealed, despite their non-computing undergraduate degrees. However, this diversity in experience is accompanied by differing degrees of confidence in their programming knowledge. The paper underscores the necessity to shift from traditional, uniform educational methods to a personalized, student-centered model that accommodates individual expectations and the ever-evolving demands of the software industry. In today's landscape of software engineering education, mere memorization of programming syntax and facts falls short. It is imperative that students become active problem solvers, capable of applying fundamental principles in real-world contexts. The principles of learner-centered education, including individualized learning, active and problem-based learning, collaborative learning, and continuous feedback, are discussed as vital components of a student-centered approach. The research methodology involved surveying students to understand their prior experiences, and career aspirations within the context of student-centered education. Results demonstrated that many students had prior experience, with a substantial percentage having completed short online courses in programming. However, the majority expressed neutrality or a lack of confidence in their software engineering knowledge. On the other hand, most students were confident in their ability to succeed in the course. In this paper, we advocate a shift towards personalized, student-centered education in software engineering, highlighting the importance of understanding students' needs and expectations to create more effective and engaging learning experiences.

This innovative practice full paper describes an indepth analysis of the pedagogical implications of incorporating generative artificial intelligence (genAI) tools, specifically Chat-GPT, into a data science course for postgraduate masters computing students. This research is grounded in the implementation of ChatGPT in a data analysis course, aiming to evaluate its effectiveness in fostering students' analytical and decision-making capabilities. The study employs a qualitative methodology to assess the educational outcomes of integrating ChatGPT, focusing on its impact on student engagement, learning efficiency, and the development of critical thinking skills in the context of data science. Through a combination of interviews, and analysis of students' project outcomes, we gather insights into the challenges and opportunities presented using genAI in the data science course. A notable innovation of our approach is the introduction of a dual-report assessment method, which not only evaluates the students' project results but also their proficiency in prompt engineering - a crucial skill for effective interaction with genAI tools. Our findings suggest that while students demonstrate enhanced data analysis skills, they also face difficulties in accurately framing queries to yield useful results from genAI, highlighting an essential area for further curriculum development. Further-more, the work delves into the pedagogical strategies that can optimize the benefits of genAI tools in education. It emphasizes the importance of a structured framework that guides students in the ethical use of genAI, encourages critical reflection on AI-generated content, and fosters a deeper understanding of the underlying algorithms and their implications for data science. The implications of this research extend beyond the classroom, offering valuable insights for instructors, curriculum developers, and policymakers on integrating AI technologies into educational practices. By providing a comprehensive overview of the benefits and challenges associated with the use of ChatGPT in data science education, this paper contributes to the ongoing dialogue on preparing students for a future where genAI might a significant role. In conclusion, this work highlights the potential of genAI to revolutionize data science education by enhancing analytical skills and decision-making capabilities. Continued exploration of effective strategies for integrating AI tools into learning environments, such as data science, is required to ensure that students are equipped with the knowledge and skills necessary to navigate the complexities of genAI for future employment.

This innovative practice full paper describes the development and implementation of a cross-discipline techno-preneurship course, highlighting best practice, the benefits of the course perceived by students and further considerations. The course is offered at a large prestigious UK university in a department where electronic, electrical engineering and computer science courses are taught, and is mandatory for the electrical and electronic engineering students and elective for computer engineering students, who are in their third year of study as part of combined bachelor's and master's degree programs. It emphasizes teamwork, problem identification, problem solving, and creativity. This course uniquely integrates engineering skills with entrepreneurship, using a project-based learning approach requiring students to work in teams to develop a pitch, business plan, technical feasibility study, and a working prototype. These have been shown to be the most predominant methods to assess technopreneurship courses. However, the course is set apart by the focus on real-world world problems and fostering connections between students and the local start-up ecosystem. A key strength of the course is to improve students professional skills, which has been shown to be desired by employers in industry. In this work, we outline the course structure, intended learning outcomes, assessment, schedule of teaching, and present findings gained from teaching the course. Therefore, it is easily replicable by other practitioners. We detail how this builds upon previous practices to further the aims of the course to increase links with the local start-up ecosystem and improve students professional skills. The results of an online questionnaire proposed by the University optionally completed by students at the end of the course in the 2022/23 and 2023/24 academic years, revealed that students do perceive the benefit of the course as a way to develop their professional skills, such as public speaking, teamwork, and writing skills. Furthermore, the students appreciated the course structure and felt well-informed about the assessment. The results also revealed that the students rated the course highly for overall quality. The work produced by the teams confirmed our hypothesis that the hardware and/or software nature of their prototype/product appears to be disconnected from the makeup of students from the two different program backgrounds enrolled on the course. For instance, a team of only electronic engineering students still had a highly important software component that was vital for their final product. However, even more interestingly, the success of each team would appear to be based upon the team dynamics, which was monitored by the academic instructor in chard of the course throughout the entirety of the course and was a part of the assessment. Teams that demonstrated good levels of teamwork, overall tended to do better in the course than teams, which did not.

This paper investigates the impact of providing free access to campus sports facilities on the mental health, physical fitness, lifestyle activities and academic outcomes of postgraduate students pursuing a Computer Masters degree at a UK University. Utilising controlled barrier access tracking to campus sport facilities and periodic surveys, data was collected to examine changes in mental health indicators, physical activity levels, and social interactions throughout the academic year. Results reveal a complex interplay between access to sports facilities, mental well-being, and academic performance. While there is an increase in mental health issues during the course, the utilisation of sports facilities correlates with reduced Exceptional Circumstance requests (application for an extension to a coursework deadline or exam deferral) related to stress and anxiety. Additionally, increased attendance and engagement in the course were recorded, as was marginally better academic outcomes. However, students that stated they had frequent mental health concerns statistically performed worse in assessments. Furthermore, while there was a concerning increase in mental health issues among students during the course, there was a decrease in other reported external stressors such as body image, physical health, and loneliness. It is significant that the frequency of students feeling regularly concerned about their mental health in this study was 41%, which compares favorably with the comparative university average of 79%. The findings underscore the importance of providing students with access to physical activity resources and support systems to enhance overall well-being and academic success. Further research is needed to optimise student support mechanisms and promote holistic student development within the university context.

The dearth of females in computing continues to be of considerable concern worldwide. Much of the previous research in the area concludes that young females hold negative attitudes and experiences toward computing and consequently choose not to study or work in the area. Many initiatives have been put in place in recent years to attempt to address the gender divide. In light of these activities this research seeks to provide a timely measure of the potential future computing career ambitions of secondary school pupils and provides a comparison with a group of similar aged members of a voluntarily attended computer coding group (Code Club). It finds that female intent to pursue a computing career remains worryingly low yet attitudes towards computing are generally positive. It also provides an insight in gaming experience and relates the significance of this and potential computing exposure to computing career choice.

The proliferation in the use of video lecture capture in universities worldwide presents an opportunity to analyse video watching patterns in an attempt to quantify and qualify how students engage and learn with the videos. It also presents an opportunity to investigate if there are similar student learning patterns during the equivalent physical lecture. The goal of this action based research project was to capture and quantitatively analyse the viewing behaviours and patterns of a series of video lecture captures across several university Java programming modules. It sought to study if a quantitative analysis of viewing behaviours of Lecture Capture videos coupled with a qualitative evaluation from the students and lecturers could be correlated to provide generalised patterns that could then be used to understand the learning experience of students during videos and potentially face to face lectures and, thereby, present opportunities to reflectively enhance lecturer performance and the students' overall learning experience. The report establishes a baseline understanding of the analytics of videos of several commonly used pedagogical teaching methods used in the delivery of programming courses. It reflects on possible concurrences within live lecture delivery with the potential to inform and improve lecturing performance.