Web frameworks have significantly changed how developers create web applications for the Internet. Thanks to pre-defined libraries, these frameworks not only accelerate development time but also reduce the amount of code developers need to write. However, to get the most out of the frameworks and libraries, developers need to have a deep understanding of core web programming languages. This allows them to write efficient code, troubleshoot effectively, and push the boundaries of what the frameworks can achieve. The same principle applies to Generative Artificial Intelligence (AI) tools, as they have the potential to enhance a developer's toolkit. However, they will only be useful if the developer has sound fundamental knowledge to verify the output from these tools. Educators in higher education face a similar predicament with the widespread use of Generative AI tools by learners. Many learners rely on these tools as a go-to solution without being able to verify or fully comprehend the output, leading to shallow understanding. The work in this paper outlines an approach used in a first-year web programming module within the School of Electronic Engineering and Computer Science at Queen Mary University of London, where learners were encouraged to use Generative AI tools to stimulate critical thinking when conducting assessments. Specifically, GitHub CoPilot was used as a pair programmer, and ChatGPT served as a peer reviewer. In this context, the peer reviewer's role was to help the learner reflect on the tool's output. The aim of this study was to explore the design of active learning activities that incorporate Generative AI tools for web programming to foster critical thinking practices among learners. To evaluate our approach, we employed a critical thinking self-evaluation questionnaire instrument, where learners' opinions and customs were surveyed before and after both of the assignments.

In today's higher education landscape, governments expect higher education institutions to offer learners ‘value’ in ensuring they successfully complete their degree programmes and secure graduate employment. However, a significant challenge is that learners often fail to see the connections between what they learn and what prospective employers require. In addition, learners can struggle to self-reflect on their skills, leading to underselling themselves in job applications. In response, the work in this paper presents the ‘mySkills’ framework, which not only enables learners to see connections between core skills and prospective employment opportunities but can also equip them with a professional identity and a record of accomplishments and experiences that they can showcase to potential employers. One of the aims of ‘mySkills' is to support learners in establishing their personal brand and taking ownership of their career development. In this paper, we report our findings of the second year (2022/23) of this framework, where the ‘mySkills’ tasks are now summative assessments in anchor modules. We compare the engagement levels of two cohorts who have been enrolled in the programmes at the School of Electronic Engineering and Computer Science at Queen Mary University of London. Our findings show that this framework can incentivise learners towards active engagement in employability initiatives. There is certainly potential for institutions to leverage the ‘mySkills’ framework as a mechanism to promote career readiness among their learners.

The capstone project is a crucial element of a degree programme and plays a vital role in the growth of learners, as it enables them to enhance their problem-solving skills and improve their employability prospects. In addition to this, the project provides the learners with an opportunity to demonstrate and showcase their critical thinking abilities and creativity. However, due to the year-long independent nature of these projects, learners can disengage due to a lack of motivation or self-regulated skills throughout the project. To address this problem, we formulated a data-driven intervention approach that conducts learner engagement analytics to identify and support disengaged learners, ensuring they maximise the benefits of completing a capstone project. The motivation was also to provide these learners with the necessary resources and support to get them back on track. This approach was implemented in the capstone projects conducted by learners at Queen Mary University of London within the School of Electronic Engineering and Computer Science. Based on the data of the three cohorts in 2020–21, 2021–22 and 2022–23, our analysis shows that the proposed data-driven intervention approach for capstone projects can effectively identify less-engaged learners and targeted interventions are shown to improve the overall performance of these less-engaged learners on capstone projects.

Software development has evolved significantly. In the past, developers were required to have comprehensive understanding of programming languages, algorithms, and computer architecture. However, with the emergence of the Internet, software libraries, frameworks, and forums became widely available, which utilize reusable software components that can reduce development time and costs. The advent of Generative Artificial Intelligence (AI) tools, such as ChatGPT, GitHub Copilot, and Amazon CodeWhisperer, has further enhanced the developer's toolkit, as these tools can be used for a wide variety of tasks such as code generation, documentation, commenting and reviewing. As programming is often slow and requires trial and error, novice programmers can be tempted to apply the first solution found on the Internet or proposed by an AI tool without much critical reflection or notion of responsibility. Hence, the advances of AI have raised both excitement and concerns among Information Technology (IT)/Computer Science (CS) students and educators. Yet, AI tools are here to stay, and students must learn to use them responsibly. The aim of this paper is to investigate how to design learning activities that introduce Generative AI tools (GitHub Copilot and ChatGPT) for programming while promoting critical thinking practices among students in an introductory programming course in the first semester. Students' opinions and customs were surveyed before and after the AI-based programming assignment. The results indicate that students' awareness of the possibilities and limitations of AI, as well as practices of critical thinking in programming increased. This is encouraging as critical thinking is an integral part of best programming practices.

Computer Science (CS) programmes in higher education institutions worldwide have seen unprecedented growth in learners, which has presented educators with several challenges. These include teaching large classes while simultaneously measuring learner engagement. CS programmes tend to have large first-year programming classes, as this is a core subject for all learners, which can lead to an environment where learners start to disengage due to feeling anonymous and lacking support. As we enter the post-pandemic era, institutions have started to adopt a hybrid approach to teaching and learning, which paves the way for educators to analyse data from learning management systems and on-campus learning activities (lectures, seminars, labs) to measure learner engagement and identify learners who are struggling and require further support. The work in this paper describes the adaptation of an online pedagogic framework during the hybrid delivery of a first-year web programming module, which includes a hybrid practical lab coordination system to conduct learner engagement analytics to support learners.

Learner retention and employability are important metrics within the higher education sector, which places an onus on higher education institutions to ensure learners not only complete their degree programmes but also get employed in graduate-level roles after graduation. Given the competency nature of Computer Science (CS) and Electronic Engineering (EE) programmes, it is easier for these CS/EE learners (in comparison to other majors throughout the university) to demonstrate the skills required by employers within the digital sector. However, a challenge exists in that it can be difficult for learners to see the connections between the core skills taught in their modules and occupational areas. The work in this paper describes ‘mySkills’, which is a reflective framework that allows learners to track and reflect on their skills-level progression throughout their degree programme. This framework was introduced in the academic year 2021/22 as a pilot within the School of Electronic Engineering and Computer Science.

Over the last decade, there has been a gradual increase in the number of learners on Computer Science-based programmes, which in turn has led to a situation where educators have been teaching large classes. This is a challenge, as it can be difficult for educators to provide personalised support for each learner. The pandemic has only exasperated this further, given the online delivery of courses. The work in this paper describes the implementation of a pedagogic framework that was deployed during the delivery of a flrst-year web programming module. The motivation behind the development of this framework was driven by the need to facilitate an online learning environment for a large class, which adapted existing pedagogic approaches such as problem and project-based learning with the view to enabling learners to develop their problem-solving skills. In addition to this, an online lab co-ordination system was formulated to measure engagement and provide support to the learners.

Over the years, there have been many factors that have influenced the landscape of higher education within the UK. These factors include the rise in tuition fees, the introduction of the Teaching Excellence Framework (TEF) and the formation of Office for Students. Although student performance plays a vital part, another significantly influential key performance indicator that impacts these factors is student experience, which is influenced by positive or negative feedback and engagement. Despite student engagement forming a key part of the learning environment, it is still perceived as one of the weakest aspects when it comes to enhancing the student experience. In this paper, we present the implementation of an innovative, holistic teaching & learning framework for the final year project module trialed within the Department of Engineering and Computing at the University of East London. This project module had been running in different forms since the inception of the undergraduate programmes within the department, however it generally yielded poor and inconsistent evaluation, student experience and engagement. The framework was introduced during the academic year 2015/16, where its evaluation has shown a positive impact on student engagement, performance and experience, compared to the previous year.

Mining useful knowledge from data readily available in today's information systems has been a common challenge in recent years as more and more events are being recorded, and there is need to improve and support many organisational processes in a competitive and rapidly changing environments. The work in this paper shows using a case study of Learning Process - how data from various process domains can be extracted, semantically prepared, and transformed into mining executable formats to support the discovery, monitoring and enhancement of real-time processes. In so doing, it enables the prediction of individual patterns/behaviour through further semantic analysis of the discovered models. Our aim is to extract streams of event logs from a learning execution environment and describe formats that allows for mining and improved process analysis of the captured data. The approach involves augmenting the informative value of the resulting model derived from mining event data about the process by semantically annotating the process elements with concepts they represent in real time using process descriptions languages, and linking them to an ontology specifically designed for representing learning processes to allow for the analysis of the extracted event logs based on concepts rather than the event tags of the process. The semantic analysis allows the meaning of the learning object properties and model to be enhanced through the use of property characteristics and classification of discoverable entities, to generate inference knowledge which are then used to determine useful learning patterns by means of the proposed Semantic Learning Process Mining (SLPM) formalization - described technically as Semantic-Fuzzy Miner. As a result, the approach provides us with the capability to infer new and discover hidden relationships/attributes the process instances share amongst themselves within the knowledge base, and the ability to identify and address the problem of determining the presence of different learning patterns or behaviour. Inference knowledge discovered due to semantic enrichment of the process model is advantageous especially in solving some didactic issues and answering some questions with regards to different Learners behaviour within the context of process mining and semantic model analysis. To this end, we show that information derived from process mining algorithms can be improved by adding semantic knowledge to the resulting model.