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 brain-computer interface (BCI) and the tracking of eye gaze provide modalities for human-machine communication and control. In this article, we provide the evaluation of a collaborative BCI and eye gaze approach, known as a hybrid BCI. The combined inputs interact with a virtual environment to provide actuation according to a four-way menu system. The following two approaches are evaluated: first, steady-state visual evoked potential (SSVEP) BCI with on-screen stimulation; second, hybrid BCI, which combined eye gaze and SSVEP for navigation and selection. A study comprises participants without known brain injury (non-BI, N = 30) and participants with known brain injury (BI, N = 14). A total of 29 out of 30 non-BI participants can successfully control the hybrid BCI, while nine out of the 14 BI participants are able to achieve control, as evidenced by task completion. The hybrid BCI provides a mean accuracy of 99.84% in the cohort of non-BI participants and 99.14% in the cohort of BI participants. Information transfer rates are 24.41 bpm in non-BI participants and 15.87 bpm in BI participants. The research goal is to quantify usage of SSVEP and ET approaches in cohorts of non-BI and BI participants. The hybrid is the preferred interaction modality for most participants for both cohorts. When compared to non-BI participants, it is encouraging that nine out of 14 participants with known BI can use the hBCI technology with equivalent accuracy and efficiency, albeit with slower transfer rates.

Pupillary behaviour and dilation have been considered in the literature as an effective input for the measurement of cognitive workload and stress. In this work, we explore the correlation between pupil dilation and features extracted from low quality video frames that have been captured using a normal webcam during a set of computer-based tasks. The methodology presented herein attempts to develop an alternative, cost effective technique for the representation of pupil dilation in order to track pupillary behaviour from images instead of employing specialised, high-cost eye-tracking devices, which typically require specialist expertise during setup and calibration. A description of the data collection protocol and subsequent data analysis is presented. The results obtained indicate that there is a moderate correlation achieved through the use of a linear regression model, which employs fiducial point features as independent variables, and pupil size measured by an infrared-based eye-tracker as the dependent variable. Furthermore, an example of the pupil size variation within a game-based task context is shown, whereby one can easily relate the engagement and the amount of mental processing during gameplay.

One of the challenges in virtual environments is the difficulty users have in interacting with these increasingly complex systems. Ultimately, endowing machines with the ability to perceive users emotions will enable a more intuitive and reliable interaction. Consequently, using the electroencephalogram as a bio-signal sensor, the affective state of a user can be modelled and subsequently utilised in order to achieve a system that can recognise and react to the user’s emotions. This paper investigates features extracted from electroencephalogram signals for the purpose of affective state modelling based on Russell’s Circumplex Model. Investigations are presented that aim to provide the foundation for future work in modelling user affect to enhance interaction experience in virtual environments. The DEAP dataset was used within this work, along with a Support Vector Machine and Random Forest, which yielded reasonable classification accuracies for Valence and Arousal using feature vectors based on statistical measurements and band power from the α , β , δ , and 𝜃 waves and High Order Crossing of the EEG signal.

This paper addresses the potential of the Brain Computer Interface (BCI) for self-quantification through recording and analysis of brain activity. From the electroencephalographic (EEG) signal it is possible to quantify and investigate brain activity, allowing, for example, a measure of engagement with tasks to be derived, states of relaxation or anxiety to be determined, or levels of alertness to be assessed. This can be of particular use in areas such as immersive education, where an objective measure of task engagement would be of value. As such it may be possible to measure engagement but also to identify people who may not be able to engage fully, such as people with dyslexia.

In the current work, data gleaned from an assistive technology (reminding technology), which has been evaluated with people with Dementia over a period of several years was retrospectively studied to extract the factors that contributed to successful adoption. The aim was to develop a prediction model with the capability of prospectively assessing whether the assistive technology would be suitable for persons with Dementia (and their carer), based on user characteristics, needs and perceptions. Such a prediction tool has the ability to empower a formal carer to assess, through a very limited amount of questions, whether the technology will be adopted and used.

Assistive technology has the potential to enhance the level of independence of people with dementia, thereby increasing the possibility of supporting home-based care. In general, people with dementia are reluctant to change; therefore, it is important that suitable assistive technologies are selected for them. Consequently, the development of predictive models that are able to determine a person's potential to adopt a particular technology is desirable. In this paper, a predictive adoption model for a mobile phone-based video streaming system, developed for people with dementia, is presented. Taking into consideration characteristics related to a person's ability, living arrangements, and preferences, this paper discusses the development of predictive models, which were based on a number of carefully selected data mining algorithms for classification. For each, the learning on different relevant features for technology adoption has been tested, in conjunction with handling the imbalance of available data for output classes. Given our focus on providing predictive tools that could be used and interpreted by healthcare professionals, models with ease-of-use, intuitive understanding, and clear decision making processes are preferred. Predictive models have, therefore, been evaluated on a multi-criterion basis: in terms of their prediction performance, robustness, bias with regard to two types of errors and usability. Overall, the model derived from incorporating a k-Nearest-Neighbour algorithm using seven features was found to be the optimal classifier of assistive technology adoption for people with dementia (prediction accuracy 0.84 ± 0.0242).

Pervasive Healthcare Systems (PHS) constitute a research field that examines a wide range of technologies for the development of healthcare applications. Due to the data-centric nature of such applications, a number of challenges exist, notably combining the data generated from heterogeneous Distributed Sensor/Actuator Networks (DSANs) and maintaining application software as underlying technologies evolve. Coupling the technological features of DSANs with healthcare application software results in every sensor upgrade requiring a corresponding software upgrade, leading to constrained interoperability and degraded efficiency for the PHS. Consequently, a requirement exists to decouple technological features of DSANs operating within pervasive healthcare spaces from the corresponding healthcare applications. In this paper, the Sen-sorMed software library is introduced. SensorMed aims to address the issues related to the provision of homogeneous access to heterogeneous DSANs in an open and interoperable manner. Details of the architecture, highlighting interactions that promote the interoperability and portability of SensorMed, are presented.

With the challenge of healthcare for the increasing number of elderly people and the prevalence of chronic disease, research has been carried out on the development of assistive technologies and devices. This paper proposes a framework of context-aware physiological analysis for remote and efficient healthcare. With the relationship between the physiological function and daily activities, the online detection of abnormal situation needs to be carried out given such rich context information. Two core modules in the framework are discussed in details by proposing hierarchical online activity recognition and dynamic Cumulative Sum Control Chart (CUSUM) methods for process control. Corresponding experiments have been set up to collect both ECG data and upper-body accelerations from two healthy participants. This framework also has great potential to be used for long term health drift detection by comparison of the physiological function patterns given the activity across different periods of time.