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.

Post COVID-19 pandemic, the education system has significantly shifted towards blended and hybrid learning approaches. In most cases, course delivery and materials are now being hosted online through various Learning Management Systems (LMS). In the recent years, researchers in the education paradigm have proposed methodologies to find key performance indicators from the LMS data and used Machine Learning (ML) models to identify students who require an early intervention to improve their academic performance. Although these ML-based solutions show high accuracy, these solutions are often restricted to a single course/module belonging to a specific department or university. Moreover, they evaluate a fixed set of ML models to determine the best performer. To address this, we propose a dynamic Student Academic Performance Predictor (SAPP) tool which can work for different types of modules having diverse student records. The tool can predict poorly performing students to make early intervention and improve their academic performance. As proof of concept, the tool has been designed as a Python application which can take LMS data from different modules and predict a list of students requiring early intervention. The application uses the best performing ML model out of 18 ML models. Initial results using LMS data collected from two different modules running at a UK university give us early indication that the SAPP tool can accurately predict student performance for various modules with diverse range of students.

Computer science including data analytics is a widely popular field, boasting promising career opportunities in the future. Proficiency in programming stands as a fundamental requirement for success in this domain. However, students entering MSc programs in data analytics often possess varying levels of programming background, which can impact their performance in assignments. Recognising and addressing these differences through tailored instruction can improve students’ outcomes. This paper explores the importance of considering students' programming backgrounds in the data analytics field and highlights strategies to enhance their performance based on prior knowledge. This study was carried out on two different modules in two different pathways. We have chosen two distinct cohorts and pathways to ensure unbiased conclusions in our study. The initial research was applied to the Database and Programming Fundamentals module for an MSc data analytics cohort, and then we utilized a Deep Learning module for final year computer science undergraduates as a validation cohort. As a conclusion, this study successfully demonstrated a significant increase in student assignment performance through the implementation of tailored instruction based on students' programming backgrounds. Despite receiving positive student feedback and observing excellent and improved performances, it is crucial to acknowledge instances of unsatisfactory student performance as well. Both studies were conducted by the School of Electronics, Electrical Engineering, and Computer Science (EEECS) at Queen's University Belfast (QUB) during the academic year 2021/2022.

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 explores the potential for new work-based apprenticeship degrees to encourage more women into computing degrees and the IT sector. In the UK, women are currently under-represented on computing courses. Meanwhile the IT industry requires more computing graduates, in general, and specifically more highly skilled women to create appropriate products and systems. The UK has recently introduced apprenticeship computing degrees, where the apprentice is a work-based employee. In some models, apprentices spend 20% of their time on Higher Education studies and also gain credits through work-based learning; in others, apprentices spend blocks of time in Higher Education and the workplace. These degrees offer a new and innovative route to studying computing at university. Largely funded by employers, apprentices are salaried, and their fees are paid, paving the way for more people to study for a degree. The work context enables apprentices to keep their jobs (if relevant) or to move into IT roles and start a computing degree without necessarily having computing qualifications; the degrees have no upper age limit. Extending the work-based approach of US cooperative education and student work placement models, apprenticeship degrees have been introduced to increase skills levels through a close partnership between universities and employers. This is particularly important in IT, where the sector is expanding, and employers are looking for both good technical and personal skills. With this model, employers are collaboratively involved in the design of the degrees and apprentices graduate with extensive work experience.We posed the following research question: Are there differences in the paths into computing apprenticeship degrees between women and men? A survey was conducted with apprentices beginning a degree in Fall 2019: Cyber Security, Data Science, IT Management for Business, or Software Engineering/Development. Participants were asked about their routes into the apprenticeship and the IT sector. Apprentices at five universities in Scotland and one in Northern Ireland completed the survey, on paper or online (n=85; 23 female, 59 male).The results revealed a less severe gender imbalance than with comparative on-campus degrees (28% female), but this varied greatly across the subjects, from Data Science, where 55% of respondents identified as female and IT Management for Business (40% female), to Software Development (27% female) and Cyber Security (only 11% female). Apprentices were more likely to have started the degree at least a few years after leaving school and this was especially true for women. More female respondents had also been with their current employer for over five years. However, women were slightly more likely to have joined their employers in order to start the apprenticeship.This initial work identifies opportunities to recruit women onto computing degree apprenticeships, for example by targeting women who have started careers. It also highlights that there are challenges in recruiting women into certain subject areas, especially Cyber Security, but also Software Development. Exploring our respondents' motivations for choosing their subjects illuminates the gender balance challenge and indicates how degree apprenticeships can encourage more women into the IT sector.