This innovative practice full paper describes a home-based summer family learning programme, STEM Play & Learn, designed to support kindergarten-aged children and their families by engaging them in Science, Technology, Engineering and Mathematics (STEM) activities. 'Summer Learning Loss' is the phenomenon where children experience a decrease in academic knowledge and skills when they take an extended break from education, particularly during the summer holidays. Children from lower socio-economic status backgrounds are disproportionately affected by summer learning loss. This research investigates the efficacy of a novel STEM Summer Family Learning Programme for 4-6-year-old children in a socio-economically disadvantaged community. The novelty of our programme centres around the practice and pedagogy delivered in the child's home with parental engagement at its core. The programme is delivered once a week over six weeks by Home Visitors. Child-directed open-ended play-based STEM activities are modelled to encourage children to develop higher-order thinking skills. Parents and children are encouraged to explore these educational STEM Play and Learn activities further between visits. Through a community action research methodology, this research employs a cyclical process of observing, reflecting, acting, evaluating, and modifying. Results of parent and child evaluation data show promise in improving the children's developmental skills and positively influencing the home learning environment. Results also indicate there was an increase in the parents' confidence in teaching their children STEM at home. The findings contribute insights into how an innovative STEM family learning programme for early years educators and parents enhances educational outcomes for kindergarten-aged children and mitigates summer learning loss.

Academic Support 360 Framework is a strategy to support the professional development of faculty. Such a strategy can be applied to solving full-time and adjunct faculty issues that relate to pedagogy, systems and processes in Higher Education. The faculty that this framework supports includes professors, lecturers, programme/course directors and classroom assistants. Adjunct faculty are part-time lecturers who complete many of the same tasks as a full-time professor/lecturer whilst also working in industry. For both new full-time and adjunct faculty, understanding pedagogy, systems and processes is a challenge. New faculty may require support around teaching effectively, creating assessments, using the Learning Management System, entering grades into the student management system and so on. For adjunct faculty, the challenge is compounded by their time commitments to both their professional and academic careers. Programme/Course Directors need support in understanding college processes such as course validation, exam boards, reading broadsheets, student requests to defer modules or move courses and more. Classroom assistants require induction, training and support on the Learning Management System, in-class student queries and payroll systems. This research proposes an Academic Support 360 Framework that provides the knowledge, skills and competence for full-time and adjunct faculty to apply pedagogy, use systems and follow processes. The framework is comprised of Induction, Mentoring, Online Resources and Just-in-Time training. This framework provides continual support to faculty which commences before the start of a semester, continues throughout the teaching weeks and is available during the grading process after the end of the semester. The framework shows promise as demonstrated by the increase in the number of users, induction sessions, attendance at weekly Q&A sessions, video views, queries in teams' channels and induction events. This research is useful to academic management that would like to induct and train full-time and adjunct faculty in systems, processes and teaching and learning.

Zoom Fatigue is a form of mental fatigue that occurs in online users with increased use of video conferencing. Mental fatigue can be detected using eye movements. However, detecting eye movements in online users is a challenge. This research proposes a Machine Learning based Eye Tracking Framework (MLETF) to detect zoom fatigue in individuals by analysing the data collected by eye tracking device and other influencing variables (such as sleepiness, personality, etc.). An experiment was conducted with 31 participants, where they wore an eye tracker device while watching a lecture on Mobile Application Development. The online users were given by two questionnaires, one with the summary and test from the content of the video and the second a personality questionnaire. The classification performance analysis of the supervised learning algorithms showed Ada-Boost was the most suitable algorithm to detect Zoom fatigue in individuals with accuracy of 86%. The result of this research demonstrates the feasibility of applying wearable eye-tracking technology to identify zoom fatigue with online users of video conferencing.

Academic staff provide research supervision based on the one on one approach or the apprenticeship model. Current student recruitment policies are enrolling larger numbers of students on taught master’s programmes. The current research supervision approach is not sustainable with growing numbers of students and conducting research supervision that is scalable is a challenge. Increasing the number of supervisors leads to difficulties with the consistency in the quality of the supervision. This research proposes a research supervision framework that scales with increasing numbers of re-search students and ensures consistency in the quality of research supervision among faculty. The framework combines teaching practices, timetabled group supervision, co supervision, coaching and scaffolding. The research supervision framework was applied in timetabled group research supervision sessions in May to August2020 with 15 students, September 2020 to January 2021 with 10students and May to Augu st 2021 with 12 students. Results demonstrate an increase in the quality of research as demonstrated by the publication of 4 book chapters, 3 peer reviewed international conference papers and 3 invention disclosures. These publications occur during a period of growth in student numbers by approximately 1000% from 2012 to 2020. This research is of interest to both Deans and faculty. Deans will gain insight in how to ensure quality of supervision with growing student numbers on taught master’s programmes. Faculty will gain insight in how to effectively supervise students in order to increase their academic publications using alternative supervision approaches.

This Research-to-Practice Full Paper looks at the transition from second level education to higher education and the challenges this presents in terms of students getting to know a new learning environment, identifying supports to assist with their learning and even getting to know new friends. This challenge is even more complicated with the move to an online learning environment in response to the COVID19 emergency restrictions. This research introduces a higher education transition framework (called S³F) that provides support and intervention activities to manage students transition from second level education to higher education, to reduce the impact of the online environment on students learning experience and to help to improve student mental health. The S³F framework uses ongoing student Feedback to inform activities across three pillars: Learning Environment Support, Academic Subject Support and Social Support. The research presented in this paper was conducted over the 2020/2021 academic year when 1styear undergraduate Computing students from National College of Ireland, School of Computing participated in an innovative induction programme that consisted of a number of activities and support actions for the entire duration of the academic year that were part of the S³F framework. Students were surveyed during each induction session for live feedback to adapt the activities for the following sessions and to inform staff of other interventions required. Students initially have expressed feelings of nervousness at the start of the first semester however this changed to feelings of excitement midway through the induction programme. Results of the case study demonstrates that the activities and innovative actions introduced as part of S³F framework had a positive impact on student’s transition to higher education, especially around mental health, seen in the retention figures for those students. This paper discusses the results only in terms of students mental health This research is of benefit to higher education management and course directors involved in first year orientation that would like to reduce the impact of the online environment on student’s transition from second level to higher education.

Dropout and failure rates are a major challenge with online learning. Virtual Learning Environments (VLE) as used in universities have difficulty in monitoring student engagement during the courses with increased rates of students dropping out. The aim of this research is to develop a data-driven clustering model aimed at identifying low student engagement during the early stages of the course cycle. This approach, is used to demonstrate how cluster analysis can be used to group the students who are having similar online behaviour patterns in the VLEs. A freely accessible Open University Learning Analytics (OULA) dataset that consists of more than 30,000 students and 7 courses is used to build clustering model based on a set of unique features, extracted from the student’s engagement platform and academic performance. This research has been carried out using three unsupervised clustering algorithms, namely Gaussian Mixture, Hierarchical and K-prototype. Models efficiency is measured using a clustering evaluation metric to find the best fit model. Results demonstrate that the K-Prototype model clustered the low-engagement students more accurately than the other proposed models and generated highly partitioned clusters. This research can be used to help instructors monitor student online engagement and provide additional supports to reduce the dropout rate.

Due to the fast development in computer science new modules and specializations have to be developed, and the Computer Science (CS) curriculum needs to be reshaped in order to include 21stcentury skills such as problem-solving, creativity, innovation, communication and collaboration. This paper proposes a generic model for designing learning experiences for CS curriculum. The model builds on existing credit systems such as European Credit Transfer and Accumulation System (ECTS), to determine if a CS programme can be reshaped to a given period of time. Furthermore the model is capable of determining the student workload distribution across the different types of learning activities for a module. The distribution is computed based on information such as the number of credits for the module, the number of weeks in a semester, as well as the percentage hours of lectures, labs, independent study and other learning activities. Preliminary data collection and analysis was conducted in order to determine the percentages on 75 computer science modules taught at 14 universities from Ireland and UK.