 Mae hwn y pwer yn hynny, a'i ddyn nhw have three short papers for you in this session. There'll be fifteen minutes presentation and five minutes questions for each paper. We are being streamed in here, which I think was already mentioned at the start of the day. Remember that we do have the VVox app for any discussions and presentations that are happening in this room. So if at any point you want to make a comment or post a question, please do so and we'll try to pick those up when we get to the questions for each of the sessions. Felly, yn y bydd yw y tro i gydag, rwy'n fwyfoddu i'r ysgolwyd i Siân Sê. O'r hyn oedd yn fawr o'r ffordd, oherwydd mae'n bwysig ychydig iawn o'r wlad gyda'r ffordd. A'u bod yn wedi'i gweithio'r ysgolwyd yma, o'r cyfnodau cydweithio'r ymddangosol ym ddechrau. Rydym ni'n meddwl i'r cyfnodau o'r cyfnodau. Rydyn ni'n meddwl i'n meddwl i'r cyfnodau, o'r ysgolwyd i'r ffordd. Hello everyone, I'm from the School for Informatics at the University of Edinburgh, this is the prettiest look I've ever presented in my work at and I hope you'll enjoy the next hour being here. So this talk is about stakeholder expectations and concerns regarding the use of learning analytics in higher education and is based on the outputs of a large-scale European project called SHILA, sy'n dweud o'r ffordd byddwch gyda hwyl hwyl uchrygiadol i'n bod yn ystod o'r anoledau arweithio'r hynny. A wnaeth y byddwch yn gofyn o'r ysgol o'r hynny yw'n gweithio hwyl hwyl uchrygiadol i'n gweithio'r anoledau yn cael eu sythesbynau yn ymddangosol i'r hynny. I'm presenting this work on behalf of this whole team, and I would just like to acknowledge the great inputs to this work. So this project is made up of different partners in Europe. So what is the learning analytics? The learning analytics is about collecting and writing and reporting data about learners who constantly interact with this technology as they learn today. And they have been producing a massive amount of data every minute when I'm talking about a massive amount of data that is being generated already. So we could make use of this data. We could integrate it with various sources of data such as student characteristics and then generate some insights which could help us make better data informed decisions. So it could be useful for students to adjust their learning strategies and for teachers as well to adjust their learning design, their course design, and for managers as well to better allocate resources. And if you're interested in learning more about learning analytics first in three minutes, I would like to show this, the advertisement short video I made. It's called Learning Analytics in a nutshell. You'll be able to find it on YouTube. Okay, so to be able to understand what different groups of stakeholders think about learning analytics, and we have a lot to make the methods we talk to are various groups of stakeholders using a number of methods. We have carried out a survey for schools, interviews, and book cards at night. And I'll let you focus on managers, teachers, and students in terms of expectations and concerns regarding learning analytics. So expectations. From our institutional survey, we found that one of the multiple choice questions where we asked them to tell us about their motivations to use learning analytics, we found that the top motivations here, the top five ones are to improve student learning performance, to improve teaching excellence, to improve student satisfaction, improve student retention, and to explore what learning analytics can do for the institution, for the teachers and for students. So we could see that the top four items here are very familiar key performance indicators for institutions. And we could also see that from the fifth item here that at the time of the survey, which was towards the end of 2016, learning analytics was still fairly a new idea to many institutions. So they were still just exploring this idea, trying to figure out in what way they could benefit from learning analytics. For teachers, we have observed the three areas of interest. At the student level, we found that teachers were particularly interested in using learning analytics to help students develop their self-regulated learning skills, and also to give them better access to their own learning progress so that students would be able to make better learning decisions. They weren't necessarily overly anxious or overly optimistic about their progress. And our teachers were quite interested in using learning analytics to identify students' weakness by providing support to them. They would also like to know how students are engaging with the learning contents that they have prepared and so as to identify the needs to adjust the course design and materials that they have prepared for students. At the programme level, especially for teachers who were responsible for managing programme, academic programmes, they were particularly interested in getting an overview of how well the programme is doing and what way they can improve the overall quality of the educational programme. For example, students would observe the four areas of interest, personalise the support, pay everything back, and get the navigation of extending resources and also opportunities for self-regulated learning. So here are just the two quotes as examples to illustrate what they meant by personalise the support. The first student was talking about widening access that universities are all striving for, but yet students themselves don't feel that universities have really done well in terms of providing the same kind of access at the course level. So making sure that every student is on the same page, nobody is being left behind. And the second quote provides a very good example of what they meant by somebody being left behind because the teacher didn't know that the student hasn't learned to use microscope and yet they just started working on microscope. And this quote regarding feedback came from a student who was in the first year at university and he was saying that the big change between tertiary education and tertiary education is that the contact with teachers have become much less. So they would really appreciate it if learning analytics could allow them to get more feedback about where they are and where they should be improving on. In terms of resource access, we particularly hear from students who have learning difficulties and learning disabilities that just waiting through the information about various supports available at university itself is a very challenging task. So if learning analytics could provide them more targeted recommendations about these support available, then they would really appreciate that. From our survey with students which got brought out to six institutions and reached about 3,000 students, we also found that among the items related to the expectations of learning analytics services, the top three items are all related to self-regulated learning. So in this survey, we asked the students to rank their ideal expectations and the predicted expectations for various items. The ideal expectation is regarding what they expect to see ideally, what they would like to see, whereas predicted expectations of what they expect to see in reality. And we could see that for both scales, the top three items are about receiving complete profile of their learning, making their own decisions based on analytics results and knowing how their progress compares to a safe learning goal. But we also noticed that among all the samples that we have received, we have got, the Open University in the Netherlands did not really give very high score to this item about receiving complete profile about their learning. So this is an interesting one. We do not know why, but what we do know is that the average age of this student population is much older because the students at Open University tend to be professionals. They are already working, so they are more mature students. So it could be that to them they tend to have a better awareness of where they are regarding their own progress, and perhaps because of that they don't think they need these constant updates. But what we do know, especially what is important about this finding is that the implementation of learning analytics can never be one size fit all, and that students in different contexts do have different needs. What about concerns about learning analytics? Well, among senior managers we have observed the four areas of concerns, including returns on investment, whether the investment is worthwhile or not, and resources, whether institutions have enough resources to drive the use of learning analytics, including the financial resource, technological infrastructure, and enough people who could work on learning analytics. And also whether the university has this data culture, whether people are willing to use data, to make decisions based on data, and finally whether there are enough skills to drive learning analytics. So he is just a quote illustrating this manager's concern and uncertainty about whether learning analytics can really do them any good or bring any changes at all. As for teachers, concerns are around three areas, students, teachers and learning analytics, and I will just highlight teachers and learning analytics, as I will also be talking about student-related concerns later. So the top concerns that teachers have about themselves, or number one workload, and number two, potential judgment on their teaching performance that learning analytics has been used as a managerial tool. As for learning analytics-related concerns, teachers have reasonable skepticism about whether, to what extent learning analytics can capture the differences among individual learners, and also the fact that learning is quite difficult to observe and difficult to define in different disciplines, and the way we collect, we analyze data could all affect our interpretation of learning. So to what extent learning analytics can present us a faithful picture of learning is a question that teachers asked. So he is just another quote that teacher was talking about not wanting learning analytics to make students performing way that satisfy algorithms. As for students, their concerns are also there are a few areas of concerns, including the shared concerns about to what extent learning analytics can give them a precise picture of learning. But really the primary concerns among students are related to privacy and ethics, and I would like to talk particularly about access and anonymity because purpose and security are more about the expectations of what the institutions should do. So three themes came up from our conversations with students when they talked about access and anonymity. The first one is the fear of surveillance. I think we have all heard earlier from the keynote this negative feeling about being watched. And also there is a fear of being labeled, which leads to stereo types and then unfair treatment and marking. And finally, there is a very strong distrust in third parties, especially when talking about university sharing their data with external parties. And this is due to the fact that students do not know what would happen once data travels out. And also there's a fear of becoming the target of commercial emails, spam emails. So what we have seen is that there are very different priorities and concerns among these three key stakeholder groups. So what we have been able to do is that we have developed a framework that we call SHILA framework, which is based on the data that we have collected from direct engagement with 89 institutions across 26 European countries. We have developed a very comprehensive set of key action points related to learning analytics adoption and some primary challenges that institutions are facing today and also different stakeholders are facing today. And also a list of questions that we encourage policy makers to answer when they are developing strategy or policy for learning analytics. And we try to take them step by step to consider key dimensions to really use a holistic approach to learning analytics. So I'm going to just give you some examples here from this long list of statements that we have created in the framework. So for example, in terms of mapping political context, we ask decision makers to consider what are the reasons for adopting learning analytics. In terms of the dimension of identify key stakeholders, we ask will there be mechanisms to address inequality. And moving on to identifying decide changes, we ask how will the purpose of learning analytics be communicated. In terms of strategy, how will the results of learning analytics be interpreted within the context. Moving on to internal capacity, what training will be provided to scale up data literacy. And in terms of monitoring, what are the limitations of learning analytics, what can learning analytics can do and what can they not do. So this is just to show you how they all connected to each other and that this is an iterative cycle. So just quickly show you that we have developed a web tool based on this framework. And this is an interface. It's also openly accessible. You can move the, drop the statements and put in your own statements and also label them by the relevance to different stakeholders. And if I create your own policy framework. So that's the key output of this project. I'm happy to take questions. Thank you. Questions. First of all, check if there are any in the audience in the left-hand side. I'll keep your hand up. Just so colleagues can find you. Thank you. And if you can just introduce yourself and say who you are and then your question, that would be great. Hi, my name is Richard The Black Air Clarkson from the University of Leeds. We just started in learning analytics systems. This is really interesting. I had a question about the idea of conflict between different stakeholders. It seems like perhaps it's not as big a problem as some people might fear, but there's going to be conflicts of interest. So in particular things like student privacy with regards to having a system that delivers useful data. Do you have a general approach to resolving that kind of thing? So I'm thinking about examples where a student says, OK, I understand you need to track my attendance, my grades and so on. But I don't want you to have anything to do with my physical location or gender, for example. Or is it something that would have to be dealt with on a case by case basis? So in general, we are trying to encourage a dialogic approach to adopting learning analytics, which is to bring different stakeholders together to talk about how they would like to use learning analytics and really to find a balance. And I hear your question, which is, I don't think I have an absolute answer to that because that really depends on context. And I think at the end of the day, it's down to this balance that we can reach within our own context. So for example, perhaps could we offer opt-in and opt-out options for certain uses of data or collections of data, particularly when it comes down to interventions. And this is also what JISC has been encouraging people to do as well, that we may not necessarily need consent to collect certain types of data when it is for legitimate purposes and of public interest. However, when it comes to interventions, it is quite crucial that we do have students' consent. So I think that would be a principle there. Thank you very much. It's just on time, so what I'll ask everyone to do is give Yishan another round of applause, please. There were another couple of questions that were similar up on the wall. Future developments include a text and data mining service, working with satellite data and machine learning, and smart campus technology.