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.

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.

Much previous research has indicated that where a student sits in a university lecture theatre has a correlation with their final grade. Frequently those students that sit regularly in the front rows have been reported to achieve the highest grades. However most of the research restricted student seat movement, which is both unnatural and may have adversely influenced the research results. A previously reported unique unrestricted seat tracking investigation by the authors of this paper used a web and mobile software tracking application (PinPoint) to investigate student seating related performances in a 12 week Java programming university module. The PinPoint investigation concluded that the best assessment results were achieved by the students in the front rows and that assessment scores degraded the further students sat from the front. Additionally while the most engaged students were found to regularly sit at the front the same was not true for the most academically able or those with the greatest prior programming experience. This paper presents a further analysis of the PinPoint data, focusing on assessment performances within similar groups (academic ability, engagement and prior programming experiences) and additionally presents results of a temporal movement study and a qualitative analysis of the group and individual student seating decisions. It concludes that a comparison of student assessment performances within each of the peer groups, in every instance, found that the front row students outperformed their peers sitting further back. This strongly suggests that there is a benefit to sitting at the front regardless of academic ability, engagement or prior subject knowledge. It also points to other untested factors that may be positively influencing the front row performances.

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.

In this paper, we present a new approach to visual speech recognition which improves contextual modelling by combining Inter-Frame Dependent and Hidden Markov Models. This approach captures contextual information in visual speech that may be lost using a Hidden Markov Model alone. We apply contextual modelling to a large speaker independent isolated digit recognition task, and compare our approach to two commonly adopted feature based techniques for incorporating speech dynamics. Results are presented from baseline feature based systems and the combined modelling technique. We illustrate that both of these techniques achieve similar levels of performance when used independently. However significant improvements in performance can be achieved through a combination of the two. In particular we report an improvement in excess of 17% relative Word Error Rate in comparison to our best baseline system.

This paper describes the anatomy of a pilot surveillance system with a speech-based interface for content-based retrieval of video data. The proposed system relies on an ontology-based information sharing architecture and lets components of the system communicate among each other through TCP/IP communication channels. The aim of developing the pilot system was to explore dependencies between image analysis, event detection, video annotation, and speech- based retrieval of the video content in the context of a broader spoken dialogue system.

Existing dialogue systems typically use only one dialogue strategy: finite-state based, frame-based or agent-based. This paper describes research done on a dialogue manager (DM) that uses all three dialogue strategies. This DM can determine the most suitable dialogue strategy to be used according to circumstances, and change to a different strategy whenever required. The research puts emphasis on how such a multistrategy DM improves the naturalness of human-computer interaction. Determining best use of different dialogue strategies, rather than tailoring dialogue management to a particular business domain, motivates the research. 1.

This paper presents an examination of the Queen’s Communicator, a cross-domain, mixed initiative spoken dialogue system, in terms of the system’s extensibility and reusability. The paper explores how an object-oriented design methodology can be used to define both high-level discourse objects and appropriate forms of relationship between discourse objects. The paper concludes that the application of an object-based approach to dialogue system design makes possible the use of a number of different mechanisms that facilitate the extension and reuse of discourse expertise.

This paper investigates the effect of modeling subband correlation for noisy speech recognition. Subband feature streams are assumed to be independent in many subband-based speech recognition systems. However, speech recognition experimental results suggest this assumption is unrealistic. In this paper, a method is proposed to incorporate correlation into subband speech feature streams. In the proposed method, all possible combinations of subbands are created and each combination is treated as a single frequency-band by calculating a single feature vector for it. The resulting feature vectors, therefore, capture information about every band in the combination, as well as the dependency across the bands. Although using the new features results in a higher computational complexity, our experimental results show that they effectively capture the correlation between the subbands while making minimal assumptions about the structure of the correlation. Experiments are conducted on the TIDigits database. The results demonstrate improved accuracy for clean speech recognition and improved robustness in the presence of both stationary and nonstationary band-selective noise, in comparison to a system assuming subband independence.

The paper investigates the effect of modeling sub-band correlation for noisy speech recognition. Sub-band data streams are assumed to be independent in many sub-band based speech recognition systems. However, the structure and operation of the human vocal tract suggests this assumption is unrealistic. A novel method is proposed to incorporate correlation into sub-band speech feature streams. All possible combinations of sub-bands are created and each combination is treated as a single frequency band by calculating a single feature vector for it. The resulting feature vectors capture information about every band in the combination as well as the dependency across the bands. Experiments conducted on the TIDigits database demonstrate significantly improved robustness in comparison to an independent sub-band system in the presence of both stationary and non-stationary noise.

Advanced spoken dialogue systems incorporate functionalities such as mixed-initiative and cross-domain dialogues. In this paper an object-based approach to cross domain dialogue modelling is described in which service agents representing primary transaction types and support agents representing tasks such as eliciting payment details are selected as appropriate by a domain spotter. The domain spotter also deals with user-led focus shifts and selects the agent that is best placed to respond to the user’s utterances.

This paper presents some of the main features of a prototype spoken dialogue manager (DM) that has been incorporated into the DARPA Communicator architecture. Developed in Java, the object components that constitute the DM separate generic from domain-specific dialogue behaviour in the interests of maintainability and extensibility. Confirmation strategies encapsulated in a high-level DiscourseManager determine the system’s behaviour across transactional domains, while rules of thumb encapsulated in a suite of domain experts enable the system to guide the user towards completion of particular transactions. We describe the nature of the generic confirmation strategy and the domain experts’ specialised dialogue behaviour. We describe how rules of thumb fire given certain combinations of user-supplied values – or in the light of the system’s own interaction with its database.

Real-world noise may be a mixture of known or trainable noise and unknown unexpected noise. This paper investigates the combination of the conventional noise-reduction techniques with the probabilistic union model to deal with this type of mixed noise for robust speech recognition. In particular, we have developed a multi-environment system to remove the known or trainable acoustic mismatch across different environments. The novelty of this system, in contrast to other multi-environment models, is that the acoustic model for each environment is built upon the probabilistic union model, so that this system is also capable of accommodating further unknown unexpected noise within a specific environment. We have tested the new system for connected digit recognition in different environments, each involving an environment-specific noise and some unknown untrained noise. The results indicate that the new system offers significantly improved performance for the environments involving unknown additional noise, in comparison to a baseline multi-environment system.

Although the discrete cosine transform (DCT) is widely used for feature extraction in pattern recognition, it is shown that it converges slowly for most theoretically smooth functions. A modification of the DCT is described, based on a change of variable, which changes it to a new transform, called the discrete Chebyshev transform (DChT), which converges very rapidly for the same smooth functions. Although this rapid convergence is largely destroyed by the noise in real experimental data, the discrete Chebyshev transform is still generally better than the DCT when the sampling of the data can be selected at nonequidistant points. The improvement over the DCT gives a theoretical explanation for improved speech recognition obtained using Mel feature cepstral coefficients. These choose the sampling frequencies of a DCT to correspond to the human perception of pitch. It is shown that this sampling is similar to the sampling used in the discrete Chebyshev transform.

This paper provides a summary of our recent work on robust speech recognition based on a new statistical approach - the probabilistic union model. In particular, we considered speech recognition involving partial corruption in frequency bands, in time duration, and further in feature components. In all these situations, we assumed no prior knowledge about the corrupting noise, e.g. its band location, occurring time and statistical distribution. The new model characterizes these partial, unknown corruptions based on the union of random events. For the evaluation, we have conducted isolated-word recognition tasks by using both a speaker-independent E-set database and the TiDigits database, each being corrupted by various types of additive noise with unknown, time-varying statistics. The results indicate that the probabilistic union model offers robustness to partial corruption in speech utterances, requiring little or no knowledge about the noise characteristics.

Most current speech recognition systems are built upon a single type of model, e.g. an HMM or certain type of segment based model, and furthermore typically employs only one type of acoustic feature e.g. MFCCs and their variants. This entails that the system may not be robust should the modeling assumptions be violated. Recent research efforts have investigated the use of multi-scale/multi-band acoustic features for robust speech recognition. This paper described a multi-model approach as an alternative and complement to the multi-feature approaches. The multi-model approach seeks a combination of different types of acoustic models, thereby integrating the capabilities of each individual model for capturing discriminative information. An example system built upon the combination of the standard HMM technique with a segment-based modeling technique was implemented. Experiments for both isolated-word and continuous speech recognition have shown improved performances over each of the individual models considered in isolation.