 Mae'n gwneud o'r cyffredinol yma. Mae'n gwneud o'r cyffredinol yma, Llywodraeth Wadrffordd. Hei, dyfodol. Fawr hynny'n gweithio. Mae'r cymorth, Eif, mae'r lleiwyr yn y hwnnw i'r fflaenig yw Llywodraeth Wadrffordd. Mae'r ddweud o'r gweithio ar y cyffredinol yma, mae'r ddweud o'r cyffredinol, mae'r Richard Hayes, mae'r hefyd o'r strategi ddiwedd yw WIT o pobl ar yn y cyffredinol o'r ddweud o'r strategi ddiwedd a Fflaenig. Rydyn ni'n godiwn nhw. Mae'r newid ym Hermes Llywodraeth abydd yw Llywodraeth, mae'r lleiwyr er mwyn i lleol yn y Llywodraeth Wadrffordd a phanol i Llywodraeth Wadrffordd. Mae ar y cyffredinol i Llywodraeth Wadrffordd yn y cyffredinol i weld byddai ychydig yn y Cymru. Mae'n mynd i ni'n thynd i'n dweud o'r cyffredinol yma. y problemau yw'r dysgu mewn cymryd i ymryd yn ymryd yw ein maen nhw'n ac mae'n gydag ymgwyl yn ymwysgrifeth a'i gynhyrch ar gyfer ymwysgrifeth ar hyn yn gyfroeddiaeth ymwysgrifeth yn ysgolwyd. Felly mae'n gwneud yn ddod oherwydd ymwysgrifeth. Mae'r problemau hefyd yn ymryd am y解f, ac mae'r problemau hefyd yn y cymryd yn yw'r cymryd, mae'r problemau hefyd yn ysgolwyd. It's not just a problem that's limited to WIT. This is a problem across all higher education institutes in Ireland. Also, defeated into this, ever since Richard became head of strategy in WIT, there's been very much a move to being data aware in terms of how we deliver our classes, how we can maximise the effectiveness and use of resources and so on. So going towards that, the strategic office has been developing and producing annual reports, so we have an idea as to what we are doing. So far, the strategic office has developed reports for the workload allocation, basically describing the timetabling duties and the research responsibilities of various members of staff. In terms of retention, the strategic office has been developing reports, retrospective reports, on the first-year non-progression since 2013. These reports take a significant amount of work, in part because we're drawing from WIT, like pretty much all HIEs, have a number of disparate systems for storing their data and for storing their various functions. So in compiling these annual reports, they take a lot of effort in drawing stuff from Moodle, which is our main source of online content, drawing from our library access, from our admin system from Banner, our timetabling system, Silvis Plus, and drawing all this data in order to be able to produce a report, takes significant amount of effort. We want to build on that. So the part where this project comes in is that we want to automate a lot of the stuff that Mark currently is doing by hand, extend the project so it extends to all first-years, not just students that we've identified as non-progressing, also to be done in real time, hence the need for the data managing to be automated. As it is, it's a significant component of Mark's year. And also to widen the sources we're drawing the data from. As an example of the level of effort in terms of dealing with different data sources, we're currently with the strategic office developing reports on room utilization. And the main work in there is reconciling the information we have on our separate databases, our timetabling in particular, and our module catalogue banner. So this project is a continuation of some of the current initiatives that the strategic office has developed over the last four years. And it's hopefully to get us to a stage at some point this side of the screen where we have a really good idea of what we're doing and how we're doing it, and then we can see how we can actually improve it. In terms, for example, of the non-progression rates, we've been in that report since 2013, and we have a very clear picture now in terms of what percentage of our students are non-progressing and where it is a breakdown in terms of the various departments. And unfortunately, in computing and mathematics, it's not so good, which is common across the country. So that's basically where the project came from. So what are we posing we'll do? We want to develop a predictive model which will allow us to, as early as possible, identify students that are at risk of not progressing. In the first instance, we're going to focus on our department, the Department of Computing and Mathematics. But ultimately, we'd see this model being extended across the Institute. In terms of doing this, it'll have two components. The first component is the actual model allowing us to do predictions. It'll use a mixture of both pre- and post-registration data in order to help us identify students as early as possible. And tailored with that, we'll have a suite of interventions to help the students to identify that are at risk and help them to get over the hurdle. Because we are focusing on students that we've identified that are at risk, these interventions can be much more tailored. So they're effectively at individual student level as opposed to actually a group, a cohort level. This complements a lot of the existing initiatives we have in the college. We have a retention office, we have a student health and learning office, we have a maths learning centre, which are all really excellent facilities. What happens is our students tend to only engage in those global facilities when they've already half made their mind that they can't handle the material and they're going to leave. And so it's often they're doing it just to flag that they're exiting as opposed to anything else. So in terms of the deliverance, where we said we addressed three of the delta criteria, our predictive model will help us to have more evidence-based analysis in terms of retention. And our initiatives that we're going to develop will help us improve our learning and teaching practices within our discipline of mathematics and computing. And these initiatives will be in line with all this three-layered approach. We'll alert the students, engage them and inform them. In terms of the water framework, we would see that the model that we would develop while it would be particular to WIT's special case and this model would have a water lack of applicability. In terms of the actual breakdown of time management of the project, we see this in four phases. Phase one is very much a data analytics phase. We have good buy-in from the various partners within WIT in terms of getting their data. We've been doing this since 2013 in terms of building up the first year of non-retention reports, as I mentioned earlier. But so far we're currently manipulating that data in order that we can actually generate reports and reconciling the data from the various data sources is a huge manual task that is done a year on year because of doing stability in various databases that we use. So we want to automate that process. Without doing that, we can't make the process in real time. So that's very much the very first phase. The second phase then that happens between May and September is involved the development of the initiatives. We see this being done by talking to the first year of course students that currently we currently have who are having difficulty and by engaging with them and we hope to develop various initiatives that we can then apply to the following year cohort. Phase three happens in September in 2019 is actually the roll-out of the model where the model goes live and we were aiming for that by week four we'll begin to identify students that are beginning to have issues and we hope to interact as quickly as possible from then on before the issues become solidified which is very much a problem we would actually see. So at that stage interventions will be rolled out and there is a continuous monitoring process there in terms of monitoring how well our interventions are actually working and how well are we matching our students' expectations of how they actually affect them. The last stage in September in 19 to 20 involves a retrospective review of how our students have progressed and how the actual model actually has actually worked. In terms of student impact, the big impact we hope is we've less students, not progressing. Students don't progress, there's a huge financial cost to them but also there's a huge emotional cost to them and often it isn't for academic reasons why they don't progress. WIT and its vicinity has one of the largest concentrations of desk schools in the country and we have a lot of students who find it very difficult to make the transition from second level to third level. This has happened since we were in semesterised. Since our students now have 12 weeks and then bang, they have to start doing exams and they find it very hard to make this transition so they just get used to it and then the exams start kicking in. And we very much find when they start having problems at Christmas exams then they very much disengage in the second semester so by the end of the first year they've weighed too many modules they're repeating in autumn and they never get a break then so they never catch up and it becomes harder and harder as it goes on. So we very much would like to see if we can improve our progression rates or even just lighten the load in terms of our increased number of credits students will get even if they don't progress so it facilitates them to move to other courses that might be more suitable. In terms of the wider impact for the Institute, one of the action items of our WIT strategic plan is to improve our retention rates by 30%. We don't expect this model to get that. We can't numerically in terms of the numbers because we're only doing one department but we see it as one component in the overall Institute suite of initiatives. And while the first run of this project is within our own department, we would see these things being scaled across the Institute pretty easily. The actual model itself would be scalable. The interventions are subject specific so we would expect in terms of other disciplines that they would develop their own corresponding set of interventions. And this model is also aligned with national initiatives and particularly Objective 4 of the HAA system performance framework. And again, because we would see that this would help students that we mentioned before very much from the schools that have difficulties making this transition from the second level to third level. And in terms of project sustainability, this is just one brick in the whole wall or one component of a whole suite of tools we have within WIT. This complements the existing offices, like our retention office and our student learning, as I mentioned earlier on, and the various other services. But currently we have a gap. And currently we have a whole lot of data on our students and we have a whole lot of data on our courses. And we're not using this data that we have in order to help us to maximise the students' potential to progress. And this project fits it. We're hoping to use the information that we actually have and use the information we have in order to give our students the best chance of progressing as we possibly can. Okay? Thanks very much. Cheers.