Okay, thank you very much Elizabeth. Good morning everybody. I see there's 64 people in attendance, which is very good. We are expecting 90 or just over 90 in all. But I'm glad that at least there's also already well over 50% of the people we are expecting in this Institute. Okay, I'll start off by saying my name is Dile Lerato Le Kena. Not Kekana, not Le Kana, but Le Kena. That's my surname. I think I'm going to start finding people who get my surname wrong, especially those whom I have very close working relationships with like Elizabeth. So Elizabeth, I'm going to start sending a bill for you getting my name wrong so many times. Anyway, I'm Dile Lerato Le Kena. I'm the Vice President of the SAE. I won't say much about what the SAE was about. As Elizabeth hinted earlier on, our president will come and tell us more about the SAE. Not only for those who don't know, but even for those who know, because there are very many iterations of what the SAE is and does. So he will clear the air and tell us exactly what we are about and what we do. So my role this morning is very simple is to welcome all of you, like literally all of you. Our delegates, our presenters, our facilitators of the sessions, the hosts, the beautiful choir that gave us a very beautiful national anthem. Thank you so much for that, Elizabeth. We missed such on this online institutes and workshops that we are now attending in our so-called new normal. So that was a breath of fresh air. Thank you very much for that. My special welcome to all of you, like literally all of you. There are many things that you could have been doing, but you chose to attend the Lena Analytics Institute in 2022. We are very grateful for that, and we do not take it for granted. So thank you very much from the bottom of our hearts as the SAE. Today is the first day of a two-day engagement with the Lena Analytics Institute. I'm here on the special thanks, I would like to take this opportunity to really thank the University of the Western Cape for taking this initiative, which is not an easy job at all of boasting this kind of an institute. It is a very, very difficult thing to put together. And often when we look at the program and look at everything, we see a very nice, well-rounded thing. We don't know what went behind the scenes in terms of preparing for such an event. So we really would like to thank the University of Western Cape. We really would like to thank Elizabeth Boy and her team in putting up this together. I missed all sorts of obstacles, including being sick and that speaks volumes about the passion for higher education in South Africa, about this passion for student success. We are truly grateful for your effort, Elizabeth and Tim. That said, I would like to now go to specific, actually take time to thank Mr. Larry Papos, who will be giving the welcome address, as Elizabeth hinted earlier on, on behalf of the institution. We also appreciate that effort. We appreciate that effort having taken time out of your busy schedule to come and address us here. It's not taken for granted also. And then I will go into the content of today. We thank the presenters in advance, the presenters who took time of their very, very busy schedules to put up content, to pack it, to think about the audience, to do all sorts of things. It's not easy. We thank you for that. We really appreciate it. It also speaks volumes about your passion for higher education, about student success, about the success of humankind in general. Because this is about, this is exactly what it's about, it's about humankind, because the students don't end up here. We are just preparing them to be productive citizens in the future. And when I go to the specifics of the content that has been prepared out, I would like to start with the keynote address that will be offered by Prof. Skuhte, there is a student behind the data. I like it, and it's very much in line with the theme of the conference, which is going back to the basics. Often when we see the data, we are looking at student enrollment, we are looking at all sorts of things, attendance patterns, all sorts of things. Sometimes we tend to forget that there is an actual human being in the form of a student behind the data. So if we remind ourselves of that, which I'm glad about this, we will always remember that we are talking about human beings here. We are talking about progress of the humankind here, not just about the numbers and that we are looking at in the form of data. So I'm looking forward in anticipation to the actual content and delivery of the address. And then there will be workshop one which speaks about the essentials of learning analytics is also speaking to going back to the, to the basics, which is very key because we always have to remember as practitioners. I know we said the target is for novice as well as non-novice. So as non-novice, we have an extra challenge of unlearning, learning to unlearn in order to relearn, because things are dynamic and they keep on changing from time to time. There's also an interesting workshop titled, leveraging learning analytics dashboards to support decisions, decisions, decisions, decisions. We are essentially about decision support in higher education. So this is also a very, very important workshop, which we also look forward to the content thereof. And lastly, we will be given a workshop called lift experiences of implementing learning analytics at scale. Lift experiences are so much useful also. We always have to look back in order to look forward. Know what happens so that we can plan for the somewhat things that we may not be aware of that are coming in the future. So it's super important also to look back in order to look forward. When I come to the learning analytics institute itself, there is a ton of data that we're always collecting from either digital footprints of students, either physical footprints of the students. So learning analytics is actually a very good opportunities for us as higher education practitioners to try and improve the quality of learning and teaching, to try and boost retention both from the student side and the staff side and to enable students to take control of their studies also to mention just a few ways in which we can use learning analytics. So we do look forward to this learning analytics institute. We have full trust in the organizers of the event. We have full trust in the presenters of the event and we have full trust in the delegates. So we can really implore upon all of us to please, please, please, please clear our tables. It's going to be a long day and doing this online can be challenging sometimes you have tempted to do this and that even when it's physical but it's more so difficult when you are online. So I would like to really implore upon us to take time and make a deliberate effort to stay away from the temptations of doing other things because the content here of that's coming is going to be amazing. So really make an effort to stay aware of those distractions and concentrates going to be a long way but let's just hang in there it will be worth it at the end of the day. That being said, I'd like to call to take this opportunity now to call upon my president, Dr. Colisey Masango. Dr. Colisey Masango is going to tell us about the Southern African Association of Institution for institutional research. Yeah, so yeah, let's, there he is already I see so thank you very much my president we are two minutes ahead of time. So I think it's okay as you have a lot of things to tell the people here over to you. Thank you. Thank you very much. Good morning colleagues. Can you please confirm if you can see my presentation. Yes, we can. All right. Thank you very much. I don't believe as you have indicated we are about two minutes ahead or so. But what I'm going to do is I'm just going to go right ahead with my overview of the essay. Colleagues of indicated good morning and thank you very much dealer for that warm welcome. I also just want to echo the same words of gratitude to Elizabeth as well as the WC team for hosting this year's analytics. I mean you've clearly indicated it's it's a really a pep program. I believe that all of us we are indeed going to learn quite a lot of things in the next two days. So that was a time so my task has been just to refresh your memory in terms of who we are as the essay. So that's what then I'm going to do in the next 10 minutes or so. So for those of you who do not know essay was established in 1994. The year if most of you can remember where in South Africa we held our first democratic elections. So in 2022 this year, we are actually 29 years and we are having plans and discussions in terms of celebrating our third year celebration in next year. So that is in 2023 and 2024 in a very special way. So what then we will do is we are putting together a very special program specifically for that. There's quite a number of things that we are planning to implement during that event. So we'll keep you updated in terms of what is that going to be happening. So in addition to us, you know, turning 29 this year, most of you remember that the idea actually came when some of our founders attended the USA. And then they took some of the ideas in terms of how they can be able to to structure our own South African Association looking specifically into institutional research. You would know that in Africa actually essay is the only association that is solely focusing on institutional research. So I think the fact that also we've been in existence for almost eight years. I think it also speaks to to the idea and the funding they use and the relevance of the association in terms of serving our higher education sector. So looking at our vision, we are aiming to promote institutional research and related professions. So when we're talking about I R and related professions, we talking about institutional effectiveness, institutional planning, management, information systems, quality assurance, all of those other in our related profession. So when we are referring to IR and we are not excluding those but all of those they're included in there. Also, what we aim to promote as an association is training and development of our members. And I think this is part of that training session that you are going to benefit from. We also aim to promote the use and exchange of information in the context of an effective institutional research in each institution of higher education.  So looking at our statement of papers, we were here to advance research and analysis leading to the production of improved management information, also looking at the development and application of appropriate methodologies and techniques from many disciplines, and also to encourage the collection, interpretation, exchange, and dissemination of information with respect to higher education and its institution. So most of you will know that, especially here in South Africa, we do not have a specific academic program that is focusing on institutional research.  So our papers here as the association is to provide that, you know, kind of community of practice where we can learn to assist each other. And also, what we are here to do is also to promote the culture of data-driven decision making in higher education and as I still indicated in one of the workshops we are going to be talking specifically about this one. So this one, colleagues, as you know, it is becoming very important in terms of making sure that the work that we do, the data that we collect, it informs accurately what executive management they need to do in terms of policy implementation and action. And lastly, our papers is to further the professional development and training of individuals engaged in IR-related fields. As I've indicated earlier, you will see that going forward, as the SAA Exco, we are planning quite a number of initiatives that will specifically deal with, you know, professionalization of IR training, as well as making sure that we have a platform where we can be able to, you know, continuously engage with one another in terms of what you are doing and what is happening in other various institutions. Also, just to refresh your memory, in terms of the current Exco members, remember our team started in 2020 and it's going to end this year. So at our conference, annual conference in November this year, we are going to have our elections. I'm hoping that people who are already, you know, thinking about how they can contribute meaningfully, especially at the level of Exco. You can now start, you know, thinking about how you can make that contribution, but also start preparing, you know, your portfolio so that when the call comes for people to submit their bios, you know, you'll be ready by then. So in terms of the current Exco members, these are the people that we have here. It's myself, Dele Likana, the Vice President, we have Karim Straydom, who actually is the engine of the SAA. I think without him, SAA could not have been this far. And then we have Matoale, we have Lisa, we have Naveena Ashton, Alison, and we have Angel of this year, who is the conference chair for this year's annual conference. And then in terms of the latest figures looking at our membership, in total, we have about 239 members and then most of them, they're based in a cavity and followed by Karim Natal. And colleagues, you know that in terms of the membership role, so it is based on the annual registration, so that's why you find that sometimes the figures, you know, they fluctuate. But I think for us for now, that's not the problem or a concern, a major concern. What we are focusing on is, you know, packaging our services in such a way that all of our members, they can find value and meaning in terms of what the SAA is providing. And then just to provide you a summary of our annual events, the first one that we usually have is the learner analytics workshop. This is the one that we are currently having. So the main purpose of that one is to develop the capabilities of recessions and higher education practitioners in the field of learner analytics and institutional research. And then the second one that we are going to have this year is the IR Institute around July 2022. So there the aim is to grow the capabilities of IR practitioners and researchers in addressing the complexities of the higher education landscape by sharing intelligence, instruments, practices and new developments in IR. The second one is going to be the HEMIS. I think this is one of our largest events is going to take place around August 2022. We are discussing all HEMIS related issues. We engage with officials from the head CHE and they're looking at what is currently, you know, happening in terms of the statutory reporting and all of that. So this is one of our flagship as I've indicated one of our flagship events and we're looking forward to this year's HEMIS Institute. And then the last one before the annual conference is the Quality Forum, which this year is going to be around September and October. The dates are going to be communicated with all of the SAEM members as soon as we have confirmation. So the Quality Forum, as we know it mainly focuses on the practice of quality assurance in higher education and looking at how we can, you know, advance the practices and benchmarking within the area of quality assurance. And then the final one that we have is the SAE conference, which features presentations by delegates representing all sectors of higher education and accommodates exhibition stalls. And this year we are going to have that conference in November. And I'm sure that colleagues, you have completed the survey which was communicated to you with regards to whether do we prefer to have a face-to-face conference this year, a hybrid or an online one, because that feedback is actually going to help us in terms of finalizing our logistics. Similarly, we do have our social media platforms. We have Twitter, Facebook, as well as our website. So please do go there, like our pages, make comments, especially with regards to the events that we are having, the learning analytics to the end tomorrow. Please do communicate that information and let's also use that platform as our interactive way of communicating. Elizabeth, thank you very much. Thank you, policy and delay for the welcome and also for the information that you have just shared. Now I'm going to hand over to Mr. Popas to give us a welcome from the UWC side. Mr. Popas is the institutional planner and the executive advisor to the director. Mr. Popas, over to you. Good morning. I'm at the busy place, but good morning everyone. And thanks for the opportunity to address you. Once again, good morning and welcome to the 2022 South African USA learning analytics workshop with a team learning analytics, getting back to basis. This is a wonderful opportunity to pause and reflect on emerging practices in recent years. And to assess the underlying foundations which gave rise to several innovations at our universities, including learning analytics and learning. I have the numbers. I'm really pleased to see the increased interest from practitioners to engage in this important theme. So allow me to welcome you all to UWC as the host of the workshop and also to extend a very special welcome to our international participants. I'm from the University of Oslo. I want to extend the special appreciation to the organizers, because I'm told that yes, this year's essay and learning analytics workshop is different from the others that it's not just technical, but it also focuses on national practices and use cases, such as taking our back to the basic. I trust the sharing of national and international experiences and insights will inspire us all as participants to serve as respective champions when we go back to our respective institutions and to advance the course. In the context of huge inequities and huge unequal access to education, we all as our education institutions hold education in high regard. In the words of Nelson Mandela, and I quote, it is not beyond our power to create a world in which all children have access to education. Those who do not believe this have small imagination. I trust that we think big and that we regard the importance of learner and learning analytics as important tools to understand the causes and the impact that we have on our students and their outcome. The advances in advances in technology, compounded by the pandemic in recent years have compelled many institutions to innovate to customize the approaches to education. In doing so, often had to do make do approaches, which may serve as Instagram stopgap, and often lack appropriate underlying foundations, such as thought infrastructure, appropriate context, appropriate delivery modes, and appropriate support of the learning environment. This is a new tool for student success that has been vastly under-researched and underutilized in learning analytics. In other words, the use of data generated within the educational context to inform, to improve, and improve our approaches to learning and to teaching. It can no longer be said that the tools available are too limited, too complex, or prohibitively expensive. The challenge often lies in our understanding of why it is important to understand learning and to lend support to the widespread adoption and the uses of learning analytics. I would like to, as part of this introduction and welcome, just to leave you with four questions to mull over as you embark on this journey over the next few years. In terms of learning analytics, have we adequately articulated and clarified the vision, the value, and the purposes of learning analytics to our peers and our institutional leaders in order to harness and gain support. Secondly, are we collecting, are we curating, and using the appropriate data to inform our approaches to understand learning as opposed to just understanding the learning. Thirdly, do we have the requisite institutional capacities, the infrastructure, and the support to lend adequate support to learning analytics. And finally, are we equally observant to the potential intrusive collection and the uses of data and particularly learning analytics data to uphold the integrity of our institutional research. I trust that you have taken these into account as you will embark on discussions over the next two days. In conclusion, I trust that this workshop will provide the opportunity for us to share and to learn from each other, and to help with the development and the adoption of technological affordances for student success. Welcome again to the SAE, learning analytics workshop hosted by the university, and to better understand the foundations of learning analytics. We hope that you do not only build networks, knowledge and capacity, but that you enjoy the experience over the next two days. Welcome again and thank you. Thank you, Mr Papus, for the welcome. I know that you have a very busy schedule, so I'm going to also release you to attend to your other priorities. And thank you for leaving us with those questions. I think in the next two days, when we ventilate and when we learn from others, we are going to see how we can adequately answer those four key questions that you have asked to look at as well. For now, before I introduce a prof to speak to us about students, I am going to give you again an overview of who UWC is about, but this time around, I'm not going to use the numbers. I'm going to also share with you the video about who UWC is. I hope you enjoy and learn more about UWC. There's a university at the tip of Africa that's right at the top of its game. It's a place with a proud history and legacy pulled upwards by a future that will not be tamed. It brims over with powerful ideas that again and again lead to potent action and real change. Since when? Six decades long, it's been home to groundbreaking, changemakers, planet shakers, and it's still going strong. It's the home of forward-thinking, higher learning, homegrown pride and a story still unfurling, independent thought which sparks innovation, mindful global citizens, and their perpetual liberation. Today, this is my home, my community, my springboard, my zone, like iron sharpening iron. Here, my potential is mind, my mind is refined, and my future redefined. It's our time to learn from our past and our histories to be the leaders of tomorrow's victories. It's our time to stand up and step into our light, to rise with Africa and all the world by our side. And on that note, those were our beautiful students, our third year and second year and master's students. I'm going to hand over to Prof. Scooter to talk to us about the student behind the data. Over to you, Prof. And before Prof starts, because Prof is going to give us just a keynote. It's not a keynote address, but he's just going to give us a little bit of information and thought-provoking thought as well as Mr. Popes also did the same. With Prof. Scooter, because we have enough time, he will allow questions and answers at the end of the session, so I welcome you to use the chat forum, or also at the end, when he is done presenting, you can raise your hand to ask your questions or comment, either on the introductions done previously or comment on Prof. Scooter's. Talk for this morning. On that note, Prof, over to you. We are at your... Thank you, Elizabeth. Ladies and gentlemen, thank you very much for the opportunity to be involved in this meeting today. It is an amazing experience for me to be exposed to learning analytics in general, but to colleagues that we could sit around the table today and from all over the world and have some deliberations, share some views. I'm going to share my slides, so just in a moment, please bear with me that I could get my slide set going. Thank you very much, Mr. Popes, for the interesting introduction that we've had today. I think it is very important what he said. And you really succeeded in setting the scene for this two-day webinar and seminar, or what I would like to call it, colloquial discussion. When I was asked to get involved in delivering some notes to you today, I thought that of the work that we do at UWC, my involvement in it and the unit I am in, and it is against the background of my training. I'm a trained sociologist and have been involved in academia and in educational research for many years also. But therefore, ladies and gentlemen, it is not strange that you would hear from me that there is a student behind the data. So my presentation is based on the information accumulated during the process that I was employed at UWC. I was still employed in the MapWorks project while I was involved. And the Making Your Mark project now program to assist students in the university in developing a predictive analytical tool for studiousness. And we've got a lot of lessons that we learned from that, and that I want to share with you. I want to talk to you today about what is this thing called a student. I'm going to take you through a journey, how we went back to basics, exactly like Mr. Pogba said, how we went back to basics and the views that sort of were exposed when we started to share it. Ladies and gentlemen, just a few introductory notes for you.  I don't look at me. You have my slides to look at. Therefore, the slides would be more complete than usual. Let me start with some introductory observations. The idea is that it could be included in your colloquial discussions as we progress through these two days. And it will be sometimes provocative statements I would make, simply because we went back to the drawing board. Not reinventing the wheel, trying to discover how the wheel was invented. It seems that all tertiary education institutions and that is basically all of them. Do some data analytics simply for or mainly for marketing through to enhance their teaching and learning capacity and ability and and to pick up the areas where there are problems. But ultimately, to enhance the throughput rate of their students. These three outputs. All depend on the input of learner analytics. It plays a crucial role in understanding what we are doing. And very important to take note is that there is no one best practice. Let go from one university to the other and this part of the world to the other part of the world and say, yes, we have a best practice. No. There, there's only room for improved practices. I think we must mistake note of the statement that this is why we are here. This is why we are here to raise the bar of our respective institutions where we are involved in to take from what other people say to tailor make it for our institutions. There is no one single best practice. And also want to want to make note of from some of my observations is the UWC is doing phenomenal. And this we are, I do believe from from my experience at the cutting edge of this, of this phenomenon of an analytic data, collecting learning, having access to learning academic data, and so forth. And it's super Malala network partnership. And, and the faculty is all of them play into this. And it is amazing work that we do. You will hear from my colleagues that we share what we do, take from it what you can, and we want to take from yours what you do and your institutions, and take from that, what is applicable to power to our environment. All researchers and scientists as researchers and scientists we are, we are purely aware of the fact that the more we know the more we become aware of what we don't know. This is why we are here today in this room. When we go back to basics. We're talking about this thing called a student. What is this thing called a student in a university environment. And worldwide, all university campuses showed both commonalities and unique differences and the challenge for us is to dissect both of these concepts and make it applicable to our environments. Commonalities are usually academically related. It is often not necessarily not negotiable academic criteria that is the curriculum that is the standard that we set across the world for academia to have qualifications that could be respected and accepted and recognized across institutions. And this is what we talked about of all education campuses is usually students related mainly related to unique environmental where they are situated, which part of the world they are situated physical physical availability of facilities, buildings, etc. These are structural and psychosocial factors that could have a positive or negative impact on our knowledge transfer. In combination, these factors, these four factors, make a university campus, make a tertiary, tertiary educational institution. Three of this is a human behind it. In all three of these factors, you will see this history, history reflected in buildings and the UWC is not unique in the sense in South Africa. All campuses in South Africa are reflections of the history of South Africa, good and bad, positive and negative. And we will have to take from that what is good and what is positive to take us forward into the future. All of these unique elements would have an impact on our throughput rate. But what is this thing called a student. You know, a student get access to any university once he's got a senior certificate with a certain level of performance at school. Nice to hear. Nice to say that it's not so easy that when I apply for a university access to another country, I would have to. It's not necessarily recognized I would probably have to pass an exam to get access to make sure that I'm on standard in South Africa. This is the case. At UWC we offer approximately 2010 models I was informed. Educational subject models is offered to students. The reality is, the student that entered the gates of our university. I've had exposure to a maximum of a probably five to seven subjects of you want to add models that relate to the school education of probably one or two or even none might have had a bearing on their first year subjects when I studied psychology at the university in South Africa. I haven't had that subject at school at all. If I studied social work, I haven't had that subject at all. So what is the reality. The reality is that students that enter the gates of our universities all over the world. We're not prepared by the secondary educational environment for university training for tertiary training. They would be prepared for the job market, they would be prepared to be solid citizens and to earn a living. That is the primary purpose. It happens to be so that we add some qualification to say yes, if you have this extra performance and I have this extra performance at school. We will allow you into tertiary education. But theoretically, all students that managed to have access to tertiary education according to rules or regulations in a country basically start the fresh. That is the reality. They all start from the same page. This thing called the student that we have on our campus comes from a spectrum of backgrounds. It's a spectrum of cultural, family and educational contexts that enter our gates. In South Africa, there's no standardized element other than I mentioned school academic performance and financial resources that access a filter to get access to the university. Some universities abroad, the first world countries sometimes will find that the financial resources would be covered by the government or by the states. You have to access financial resources, whether it's NSFES or personal funding or whatever it may be. So ladies and gentlemen, these two factors are actually the only providers built into our access to universities. Not even your criminal record will deter you from entering our gates at university. It is clear from this that students need a mirror to fathom our personal preparedness. Because of this diverse backgrounds that we that we come from diverse histories that we that we come from and diverse historical institutions gates that we enter. What seems to us here in the group that I was doing a research with is that there is a Center for Student Support Services in the unit for the office for academic support. It seems that first year students in particular, sometimes it carries over to the secretary of third year that they don't know what they don't know. And this is a very important element in our in this new unique egg you and I would like to call it a new social environment is that it's an academic psychosocial blend that they enter in world they're going to live in for some time. The reality check on throughput rate. It is such an important element. And it's the cause of ours of debate of creating definitions what it is. But in the end ladies and gentlemen throughput rate in South Africa is directly related to the funding formula. If you have a poor throughput rate or funding from the government is less. It seems to me that we focus our to enhance our throughput rate the focus is usually mainly on first year students to prepare. We have programmed some projects to get first year students on the page to prepare them to say this is what you are answerable but the point is throughput rate is often the first year students inability to pass. It carries through to the second and third year students also. It's a delayed graduation. If I, if I miss one subject, a core subject that would delay my graduation in my second or third or final year. So I'm a throughput rate. Negative impetus negative element in our statistics. So clearly ladies and gentlemen learn analytics is key to identify areas for intervention where this preparedness or my I call it re socialization for students. This environment can be improved. We have to press the right buttons. If we want to be successful. Just some comments for our colloquial discussions during these two years or two days of sharing bills. Interesting that the dominant forms of assessment at universities would always be test assignments exams. The academic use. If you have enough marks, your marks is high enough, you pass. If not, you fail. And that's it. But it does not provide this holistic picture of all the factors that contribute to the successful student. What contribute to those students. That were not successful. What made the contributions to those students that were not successful. We need to know more. What do I say, we need to sharpen the tools. Ladies and gentlemen. The data analysts have to sharpen our tools to read the student better. The majority of the students probably 80%. According to some information, you will see just now. Just struggle to get through the year. And pass. They don't seek for attention, special attention, special assistance, special help. They just pass the cruise through the university. Me, a case in point. I just struggled to pass. I struggled to manage my time. I struggled to do enough just to pass and where I failed. I just had to recap as a war. I have to go back. And that's it. What does it say? The majority of the students are the neglected missing middle. The majority of the students are probably. Underperforming. The majority of the students. Through the rate. Would be determined. Also by a focus on the struggles. The students that don't cope. The students have problems. We have to pay attention to that. Paying attention only to that segment. Is not going to make a difference. A serious difference. In the throughput rate. The number of campuses show different physical and cultural. The historical environment as mentioned. And some of these elements could have a direct way in direct student academic performance impact on it. And we have. To sharpen the tools. To look into this. To this. To those of us working at all. To discuss of all of cold of information. That we have access to. So that we could make sense of it. It seems to be that we have a broad consensus. Amongst psychologists. Education is. Of what a student. Really needs to be. To do. To be academically. Successful. were then, it became clear and it is obvious not for us, I mean, it's true for Murray-Kate, it's true for Oslo, it's true for all over the world, that some struggles students experience could have been avoided if they had the early warning signs of where they're heading towards, because we don't know what we don't know. So if the CEE-TRABLE-S group at the UWC, we embark on a paradigm shift, I'm so privileged to be associated with that, and that paradigm shift sort of moved from a deficit interventionist approach to a more proactive, possibility-focused approach, and that includes the students once in partnership with the university administration, and this is two important elements that give some meaning to our learning analytics data. So what is it that I experienced? First of all, knowing too much and having too detailed information from our learning analytics is just as unmanageable as knowing that too scant little information, or little information, and that is that is something that that is important to take note of. We could go into detail and give a lot of information, but if we cannot interpret it, it's only data. We have to turn data into information. So yes, knowing too much or knowing too little, neither has any practical value for our purposes in servicing our academic environment and student success. So yes, in search of this balance is the challenge that we undertook at UWC, and that we would like to share with new people during these two days. It's that journey that took us back to the drawing board, and this is important to take note because that journey took us back to the figures. We've had access to figures, but it seems that even a person like Albert Einstein have had something to say to us. Not everything that counts can be counted, and not everything that can be counted counts. During our journey back to basics, there was an individual actually, unknown individual to me, but a photo I discovered that had a profound impact on our thinking. During the advent of the COVID times, there was this picture of this individual somewhere in the middle of Africa, and I thought, Africa's got news for the world. This man deserves our respect. To put on a mask during the early days of the COVID, this was the solution, and you could see it's 100% organic. It's 100% protecting safe. It's a 100% fit. We've had research done on what the fit should be that you don't that you could see through glasses when you put the mask on, etc. It's 100% recyclable, but you hear what it is. It is blended, touched modern technology in the real sense of the word. Proud to be associated with this individual and going back to basics. Allow me to share some observations and also to give you some point, as possible, point during my research in the Making Your Mark program at UWC. We dissected 28 international predictive analytical instruments that can be used as a soundboard reflection as a mirror to students so that they could fathom where we are, and I on track, and have I adapted to this new academic environment, university environment. But what was clear when I discovered, when I dissected these 28 instruments, the students' voice was absent. You could see it clearly here. It is a typical top-down, academically inclined, designed instruments. 35% of the clusters in all of these 28 instruments focused on creative thinking, creative mind, those things that is theoretically associated with tertiary academic environment. It is about self-motivation and social integration, and then it tapers down to some other stuff. We took from this what we could, but clearly the bulk of these instruments showed to me the voice of the student is missing. So what we did during that time was that when we embarked on this research to test one of these instruments, we added three questions to the evaluation of it, and these three questions were, if you look at successful students in your faculty, what do you think is the one most important ability there? That is looking at the people around you, my fellow peer students. The second question is, okay, having said this, having seen this, what is it that you need, and then what do you think your faculty should do? These are the results we found. Students saw, recognized what they success, the successful students, they recognized successful students in as being hardworking. They know how to manage their time. They have perseverance, they are determined to complete, they are motivated, which is probably the same thing, both of them could be in the same category. Hardworking and time management could probably also be in the same category. Self-discipline, hardworking, same thing. You see all these personal traits, and then they pop up speaking, communication, ability, and discussions, having to share reviews. But we also asked the students, what is it that I need? Now I've said this, I see this, this is the makeup of a successful fellow student. What I need, I need better time management. I need more commitment, hardworking, less procrastination, more focus, that's probably time management. Self-discipline, and so forth. We also asked what the faculty should do for us. The faculty should give us writing skills, speaking and communication skills. The faculty should help us with study methods, learning skills, and so on. And you could see what this critical thinking thing is further down of what came from the international environment. I'm rushing for time. The standard protocol, ladies and gentlemen, of intervention addresses three levels, and there's the primary, the whole class in the secondary, more group work, and then you have the individual work. It seems to be that about 80% of the students are dressed in the overall, that's the international figure in the group work environment. You know, the big group work environment. Then we have students that are dressed, and then we have the individuals. But at UWC, we were able to dissect it further. And I have two or three slides left. I was talking about the machine middle. We were able in our evaluations when we designed an institution alert index. It seems to be that from transfer, that is from 2018-2019 and the third transition that we did our research on, 8.3%. That means about three out of everything students have had, sorry, had no problem. And one out of everything students would have a problem in this status. The machine middle is probably here. But if you look at this, what happened here is once this group became bigger over time, and because of interventions, this group became smaller, but this group also became smaller. So who are the neglected people? We have to work on different elements, ladies and gentlemen. We have to work in improving the ability of the poor student. But we also have to nurture the good student to excellence, and our data should cover the spectrum. And our information is putting the student behind it, should cover the spectrum. Therefore, ladies and gentlemen, I can inform both, we need information that can inform both the faculty, the institution, and the whole movement of current status and trends in the student's experience. Let us all use this collegial opportunity, ladies and gentlemen, of these two days to share our knowledge, to improve our practices, not in search of the best practice. We will never get there to improve our practices so that we could serve the student population and could serve the institutions that have all unique traits. Ladies and gentlemen, I thank you for your attention. Wow, Prof.  The more we know, the more we come to a realization that we don't know. Thank you for a thought-provoking presentation. And you do have a question because I'm looking at the time as well in the chat. I'm going to get it right this time because she used her name, Lerato. So Lerato has posted a comment, not a comment, but actually some questions, some couple of questions. I'm not sure if you're able to see the chat. She says, Hi, Prof. Very thought-provoking talk. Please elaborate for me on the following. How do you profile your students to know where, to know who they are? That's number one. Number two, how do you handle changing profile over the years? And the last one, how do you deal with the minority groups to make sure that their experience is still memorable in a positive way? Oh, thank you for that. I'm very hesitant to study to a student. How do you know who you are?  And we should not take that away from anybody. I am who I am. I operate in a different environment. In fact, the university campus environment is according to all criteria of society, an abnormal society. It's an abnormal environment. I can prove it to you. The average IQ of the person that enters the gates of the university is higher than the average IQ of the population. The average facility for education is higher than that or any in the population, in the communities. The average training of the presenters of your lecturers is higher than that you've ever experienced. And we have facilities for recreation. Everything is there. It's a different environment. But it's also different. You have nobody that wakes you up in the morning to say you must go to school. And this is where you have to adapt. This is where we have to say, I have to change my attack. I have to change my way I operate in this environment. There's a saying in sociology, we talk about you have to switch containers. You enter the gates of a university campus. You have to switch containers. And we have to give students that ability.  But be versatile. Be able to switch containers. I don't think I should go into too much detail because of the time. You muted Elizabeth. There are a couple of comments in the chat. One is saying very informative and practical. This is from Magdalene. And the other one comes from Melikaia. I'm not going to say whether he or she but they wrote, my thesis is on the gamie or gamification of higher education content. Do you have any thought on gamification as a tactic for increased engagement? I do have. It is a total new environment. You are trading new ground. And the spin-offs of that, we still don't know. You must remember, ladies gentlemen, we have to, when we raise children, we often give children guns to play, police, robbers, police and robbers and so forth and so forth. I also played with guns in my life. I have never had the urge to shoot anybody. So it is so important to take note of how far the implications would be. And we've done some research on television just to trigger your thinking a little bit more on television movies. What is the dangerous part of movies? And it seems to be that the dangerous part is the effect of my actions, not my actions. We are inundated with movies that shoot people. But when you see the effect of that, it becomes disastrous. So gaming, it's amazing how vicious these games nowadays are. How vicious the content is of games people play and so forth. But it seems to be it does not necessarily rubble. We need a lot of more information on that. It could be a logic link. Whether it's proven by data, I don't know. But gaming in general, I'm not talking about, I have a sense of what you are referring to. Education is not only a game. It's creating a mindset of knowledge, something different. You can use gaming as an instrument, I suppose. Go for it. We need more information. Thank you, Prof. Melika, if you have more questions or you need more clarity, you can raise your hand and I will recognize you so you can speak to Prof directly as well. Kofi, you also posted in the chat, you can unmute and ask Prof the same question that you posted on the chat. Thank you. I thank you very much, Elisabeth, and thank you very much, Prof Scott, for a wonderful presentation. My name is Krohima Rumula and I'm from UNISA. Prof, I wanted to just ask you a young question with regards to the comments that you made around best practice. And certainly as somebody who has been practicing in information systems, business intelligence and analytics, for the bigger part of my humble career, those comments really resonate. And I remember the days when we used to have a very inferior infrastructure. And it was often funny when we are sitting for these international exams because the men in which they asked these questions and how we would respond, we would respond in the context of how we would have to do it in South Africa with the limitations that we have. And what was always shared as best practice then was really not best practice for us and even in some cases was not feasible. So I do support the views and the ideas that there is no best practice. However, Prof, my question is that in the context of what you have shared with us, and we are talking about learning analytics in general, there is always a scenario where others are doing better than others. And there is often a learning process which may include a benchmark or an alignment of some sort where we learn from others. Is it really a bad idea to work towards a certain position where that position can be defined as a kind of, when I say minimum requirement, I mean not exactly, but there's a sort of a benchmark of some sort to say in the context of trying to understand what this thing of a student is and how do we assist them. These are the list of measures or indicators and this is what they mean. And if you are able to kind of work out these sort of measures then these are the kind of insights that you can get. I just wanted to check, Prof, as to what are your views regarding that, especially mapping it back to the comment that you made around best practice. Thank you. Thank you for that question. It is the case that we have been working on an instrument that could assist with that. We call it the student thermometer is now in the final phases of our testing. We did manage, I do believe the group, we work in an excellent group in C++ and this group managed to draw down back to basics. You see that guy with the mask? We went back to real basic what makes, the question is what makes a successful student. We draw it down to six criteria, clusters of information, which is what I would like to believe is inter-institutional. There are, for instance, just to give you one and then I will conclude, we talk about communication skills. You have to have reading, writing, listening and speaking. If you cannot do that, those four, you have a problem. I don't say to one level, but what I do say, if you don't have the ability to speak in class with colleagues, with students, fellow students, with lecturers, to come across and project your views, you've got a problem and then we have to adjust that. If you cannot write properly what you think on paper, you're not going to pass. Our previous question was from a doctoral student. If you cannot put your thoughts on paper, they're not going to pass you. So these are provisors, the very bare basics. We draw down to that. Yes, we are in the process of putting them on the table. Early learning, early, what's it, alert for students, studiousness, the early warning instrument for students, studiousness. And I think we will probably be able to share some good information during our next deliberations coming up of our conferences. But yes, I think we are there. I have six of them, but I didn't decide not to share that with you at this point. It's still in the final developing phase. Thank you, Prof. Apologist. My phone decided to go on. Are there any final questions? Anyone who have this burning question that they want to ask, but they were not sure whether to ask now is your chance to ask that question. Tuma, the floor is yours. Prof, you only have two minutes to respond to Tuma. Tuma, you only have one minute or two minutes to ask your question. Thank you. Okay, thank you, Elizabeth. I'm not sure if I'm audible enough for everyone to hear. Okay, thanks, Prof. My name is Tumamba, I'm from Multasa-Sulu University. Mine is a quick one, Prof. You mentioned something very much interesting about access to higher education institutions and student profile and background. Unfortunately, you correctly specified that the only determinant that guarantees one access is academic performance from high school. There is nothing else that is being determined. I want to check from your understanding or from your wisdom. Do you think we need to get to a point where we enrich those determinants with a student profile? For example, we look to where the students are coming from and the environment from where they were learning. We use that particular information and get to an understanding that it is justified that their marks will not be similar to those coming from, at least backgrounds that are better off. At what stage do we use that particular information to enrich our determinants of giving them access to higher education? I'm not sure if that does... Yes, yes, thank you. I have some thought-provoking ideas for you and I'll do it in short. The problem with the current problem is that the average mark of a tertiary education, of a secondary education, school mark, gives me access to the university. That means if I am a language guru gifted person in languages and I fail my maths, I won't get access to university. The same is for the contrary. If I am a gifted mathematician and I fail my language, I don't get access to university. So what do the universities do? We give the average person and above average person. I think we should think of in future giving access to university courses, study directors, what on earth do you want? If I want to become a language expert, an author, what do I want to do with maths? I don't need it. I'm not going to attend any maths classes. If I want to become a... I'm just thinking aloud now, become a nurse and so forth, then you need some maths. I don't need languages. If I want to become an engineer, I don't need languages. Now, not to that extent, at least. I know what I say we could debate it. But I think we should move in the direction of course related access so that we could get the gifted person in that direction and not only the average person and we have to show the door for that gifted person. Just think about that for future. That means you won't be able to switch courses. I give you access to the language department and that's it. You can't switch from there to law. Right, you get my idea. I think that is probably where we should go and I think it will address a lot of our, may I call it, low-key performers at universities. We already cut out the excellent guy before he entered university or the gifted person. Okay, thank you. Stanford, unfortunately, we have run out of time, but you are more than welcome to put your comment on the chat and Prof will gladly also probably respond to all the comments that are posted on the chat. Okay, we are almost heading towards, let me see if we do have enough of time because we going to have a break and then we move into the workshops immediately. So I want to allow more time so that you can go and refill your coffees. Okay, so let's get to the announcements and the house rules going into the workshops. So I would like to firstly actually also to say thank you to all the speakers this morning and also to Prof for a thought-provoking note for us to take away and also when we go through the workshop today then all this information that you shared with us we will also reflect on as we go through the workshop and learn from others and also I encourage everyone to also participate and ask questions and also make reference to what Prof has also shared with us this morning. We're going to go into one breakout session. There won't be any breakout sessions in between depending on the facilitator. We have shared what the facilitators are going to achieve with each and every individual workshop. So there will be polls if you are struggling to see the poll if the facilitator has posted a poll and then say let's participate please put your comment on the chat so that we can assist you with addressing that challenge that you have. The workshops are going to be interactive and we expect that everyone participate in those workshops and share because the people we've invited to these workshops they are leaders in their own rights in the institutions that they work in most of them they work with the LENAR analytics institute with the LENAR learning analytics in their workplaces as well so they do have a vast majority of knowledge that they want to share with us in South Africa we can learn from them and we need to ask them as many questions as possible so please interact don't be shy don't be quiet like me I'm shy so we want you to participate and ask questions and learn and share ideas also share what your institutions are also working on in terms in relation to the LENAR analytics or learning analytics as well. In terms of the program you all have the program at the end of the second day Karen will send you the feedback form session that the feedback form that you need to complete it is very very important for us to have your views and your opinions in order to improve from here going forward as well so if you're not coming back tomorrow please make sure that you check your emails tomorrow at two o'clock to see if you have received the feedback form but probably she will send it early in the morning please make sure that you submit that we always would love to hear from you the reason why we also wanted to go back to the basic it started with the conference and the conference we identified that in South Africa not all universities are at the same place when it comes to analytics some are advanced some are at a lower place usually the LENAR analytics workshop or the LENAR analytics institute for SAE it's always technical and we always have a minimum number of people because it is a technical and also it's a specialized specialist area so we normally go into the technical looking at the tools looking at techniques and predictive models and all that but since the conference last year of SAE we came to a realization that if we want to bring everyone along on this analytics journey we need to go back to the basics we need to go back to understanding the frameworks to understanding the principles to understanding the processes the methodologies the techniques and not make this workshop too technical so that everybody can understand can also participate and can also at my end use the LENAR and LENAR analytics in their workplace tomorrow we're going to launch what we call the LENAR analytics special interest group where it will look at sharing best practices when it comes to LENAR analytics training and development when it comes to LENAR analytics and LENAR analytics where we're going to share international and national trends with each other so that we bring everyone along with no institutions will be left behind as we move through this journey and it is very important that those who have interest in the LENAR and LENAR analytics to join that special interest group it's open to everyone we're going to plan how we're going to communicate how we're going to do things including also how we're going to run the workshops going forward how we're going to run webinars or share resources with one another in order for us to improve that is tomorrow please I encourage everyone to participate in that special interest group launch we have created also the website for it we will launch it to tomorrow as well for now thank you we're going to have a five minute break and then we'll go to the workshops and at 10.35 Andrew will will start with the first workshop let's see if I have it right so the first workshop that is run by Dr Yishan Tsai it's on LENAR analytics essential and Andrew will be facilitating that workshop I will see you at 10.35 in the meantime enjoy UWC choir as they render again another interesting music sorry you can go and refill your coffees and come back for now enjoy the music


Hello everyone, the title of this talk is a fast and accurate guessing entropy estimation algorithm for full-key recovery. This work is done by Professor Adam Ding, Yun Si-fei, and me from Northeastern University. This talk contains three parts. First, we talk about the motivation. We review the definition of GE and explain why it is an important metric for a set channel evaluation. Next, we will talk about the state-of-art estimators of GE, which are the empirical GE and GM. For estimating the full-key GE, the empirical GE is too uncertain, and the GM is biased. Then we explain our new GEA estimator. This estimator is based on the theoretical distribution of the score vector, and we simplify the calculation using a relationship between GE and pairwise success rates. Lastly, we compare GEA with empirical GE on empirical data. We did not compare with GM in full-key case anymore since it is biased. We will show that GE gives useful confidence intervals for the GE of a 16 bytes AS full-key in practical time. First, let's start with the introduction and motivation. What is guessing entropy or GE? Why it is an important metric for a set channel evaluation and how to estimate it? When the attacker launches multiple set channel attacks using random data set with equal size Q, GE is the average of rank of the true key in these attacks. Considering this concept, given a random data set in a set channel attack, a higher GE value means more wrong keys are likely to be checked before the attacker reaching the correct key. As a result, a higher average computation cost is needed for a successful set channel attack. That is why GE is commonly used to evaluate the resistance against set channel attack. Before I will propose the GEA algorithm, GE are estimated empirically. In this case, the evaluator needs to collect a fairly large and independent data sets with equal size. Attacks n times then estimates GE by the sample mean of the crack key ranks from all these data sets. Notice that GE changes with the size Q, which is the number of traces used in one attack. Usually, more traces results in better attacks so that GE is smaller. To decide GE at a specific Q, one data set of Q do not give reliable answer. And we need to average over n data sets for each value of Q. This estimation is actually pretty good when we try to evaluate key with short lengths. For example, you can check the result in this figure when we estimate GE empirically for 1 byte AS sub key. Because in this case, the ranks are computed by simply comparing scores of all key candidates in a brute force way. As a result, ranks are easy to compute. And since the rank only varies in a short range, the variance of the rank is also very small. With a large number of ranks computed in a short time and a small inherent variance, we can get an empirical estimation with very narrow confidence interval for GE. However, if we want to estimate GE in the same way for a large size key, two challenges need to be solved. First one is how to calculate or estimate the rank of crack key in multi bytes case. This problem is solved in the previous literature. More specifically, after the first ranking algorithm invented by Nikola's variance of line etc in Eurocrypt 2013. Many follow-on ranking algorithms further improve the efficiency and accuracy of the rank estimation, but still compute the rank in a single data set still takes some time for multi bytes key. The second challenge is that the empirical GE estimation is often too uncertain. In one of our experiments, to estimate GE of 16 bytes AS4 key, we compute 2000 true key ranks for each Q value, which is the number of choices for a single attack. We can see from the figure that the empirical GE only provides useful and accurate bounds in the law scale when attacks either always fail or always succeed. In these trivial cases, it is obviously that the device is safe or unsafe against the site channel attack. However, for most cases, the confidence intervals are too wide for us to render a conclusion about the safety of the device. Let us focus on the case of Q equal to 50,000. The confidence interval is labeled in the red here. The confidence interval has a lower bound of 1 and upper bound of 2 to the power of 44.2. Generally, 2 to the power of 20 is considered within an attacker's computational power to enumerate the key candidates. Thus, present a realistic threat. Usually, 2 to the power of 40 is considered too big to enumerate, that is safe against the attack. This confidence interval says that GE is somewhat between 1 and 2 to the power of 44.2, including values representing both realistic and unrealistic attacks. Hence, the evaluator cannot tell if the target device is safe or not against such attacks. The reason of such uncertainty is that the variance of the rank is very large. The distribution of the true key rank is also highly skilled. These issues were pointed out by Daniel Martin etc. in Asia Crip 2016. We will illustrate this phenomenon on our experimental data later. In order to reduce the variance of empirical estimation, the evaluator needs to compute a much bigger number of ranks. However, both the collection cost and the computation cost of doing so will be prohibitive. For the collection cost, the evaluator needs to collect n times q leakage measurements for the empirical GE. Also, computing one rank in multi-bice case still takes some effort using a state-of-art ranking algorithm. We cannot in practice do this n times for very large n values. In the picture we show on the earlier slides, computing 2,000 ranks for each q value costs roughly 9.6 hours on our workstation. So it is hard to increase n much bigger. Charlie and Pabskill also introduced a very fast estimation of GE called GM in chess 2017. GM is very fast by substituting the rank probabilities in GE formula with the ice-largest posterior probability from one dataset. However, there are two issues with this estimator. First, such probability substitution is biased. Causing GM to be a biased estimator of GE. You can check our appendix for the detailed discussion on this. And second, GM is dataset dependent. Well, theoretically, GE should be independent from specific dataset. As a result, GM needs to be averaged over multiple datasets to empirically eliminate such dependency. Due to this biasness, we will not compare GM with our GE-A in multibice case in the experiments later. Motivated by the absence of efficient and accurate multibice GE estimation method. In this work, we proposed a new GE estimation algorithm, GE-A, to fill in this part. Instead of averaged over actual ranks, GE-A algorithm provides fast and accurate GE estimation based on theoretical distributions of the ranking score vectors. This idea is inspired by the theoretical multivariate Gaussian distribution discovered in a previous literature. In the next section, we will further talk about how we utilize the relationship of GE with pairwise success rates to further reduce the GE calculation from multivariate Gaussian probabilities to the sum of univariate Gaussian probabilities. This allows us to accurately estimate GE within practical computational time. Now let's look into more details, and let's see the relation between the GE and pairwise success rate. Actually, GE has a linear relation with the sum of pairwise success rate. Why? Since we can write the rank of two key into the sum of one minus identity functions of score comparison between the crack key and the run key candidate. Where the value of the identity function is one, when the two key beats the run key and zero otherwise. Then after taking the expectation from the outside, the identity functions are turned into pairwise success rate of the two key against one run key. That is how we write GE in terms of pairwise success rate. The advantage of writing GE into sum of these pairwise success rate is that we do not need to compute the joint probabilities across different key pairs anymore. And the pairwise success rate itself is also extremely easy to compute. To illustrate this advantage, we first need to bring in the comparison score. As defined in a previous literature, comparison score is the difference of score between two key and one run key. The comparison score over one dataset is the sum of comparison score of each trace. Under the fair consumption that the comparison score of each trace is identically independently distributed. Then the comparison score over one dataset will asymptotically follows a univariate Gaussian distribution. Actually, in previous works, RIVINE and FAIT etc. have found that the whole comparison score vector involving all run key guesses follows a multivariate Gaussian distribution, which is a more general case. We emphasize here that each pairwise success rate involves only one dimension of that multivariate Gaussian distribution. So the relationship we discussed on the last slide allows us to calculate GE from univariate Gaussian distributions, which only requires two unknown parameters, the mean and the variance of comparison score. Then estimating GE is turned into profiling the mean and the variance for all comparison scores. After going through theoretical derivation, we can write GE as the sum of CDFs of univariate Gaussian distribution. In practice, we compute the sample mean and sample variance of each comparison score over the entire evaluation set. Then the GE-A algorithm gives the GE estimation by replacing the mean and the variance by their sample version using the same formula. As you may notice, in a new formula, Q is involved as an independent parameter, which means after profiling on the evaluation set, GE-A can give GE estimation for any Q values. Well, empirical method requires that Q should be far less than the size of the evaluation set. Until now, GE-A algorithm requires to compute CDFs under all other wrong key guesses. However, in AS-Folky evaluation, the size of the whole key space is too large to enumerate over. In order to solve this issue, we first break each full key candidate into one byte subkey. Next, we randomly select M samples over the key space and compute the sample version of GE-A estimation upon them. In the next two slides, we will explain these two steps in detail. Consider a multi-byte full key candidate. The side channel attack can be conducted byte by byte according to the divide and conquer principle. As a result, the comparison score under one wrong key guess equals to the sum of comparison score of its one byte subkeys. Then the mean and variance estimation of univariate Gaussian distribution under one full key candidate becomes the sum of a single byte estimation. Since there are only 256 different distributions in the one byte case, in the first step, we profile all one byte's distribution over the evaluation dataset. Then, given any full key candidate, we can quickly compute the sample mean and variance for the corresponding distribution. In the second step, instead of enumerating over all wrong key guesses, we create a sample set S by sampling M candidates from the key space uniformly. Then the sampled version of GE-A estimation becomes the sample mean of scaled probability over the sample set S, where the scalar is the size of full key space. GE estimates GE in a somewhat similar way as the empirical GE estimation. But GE-A samples over the space of scaled probabilities while empirical GE samples over the rank space. As a result, the difference of two estimation methods in accuracy may be caused by both the number of samples collected in the fixed lines of time as well as the variance of sample distribution. To illustrate the advantage of GE-A over empirical GE estimation, let's first compare their sample distributions. As shown in the figure, the variance of rank is much larger than the scale probability. What is more, the distribution of the former is much more skilled than the distribution of the latter. Make it even harder to estimate the mean of rank than scaled probability. This graph illustrates the three-byte case as the number of bytes increases, the difference of variance and skillness grows much bigger. Also, computation of scaled probabilities only involves the CDF of univariate Gaussian distribution once. Thus, it is very fast and almost constant in time when the number of bytes increases. In contrast, the empirical GE's computation cost will linearly grow with the number of bytes. In the case of AS128 full key, the computational cost is massive even with state-of-art ranking algorithms. In our work, we use FSE ranking algorithm to estimate each full key rank. An experiment shows that the GE is seven orders of magnitude faster than empirical GE estimation to raise the same accuracy for the full key. In this section, we apply both the GE-A algorithm and the empirical GE estimation on two datasets. The first dataset is the ASCAD dataset. ASCAD dataset is one of the power measurements dataset that works as the benchmark for a DN-based set channel attack. We conduct the single byte GE estimation on this dataset to show the accuracy of GE-A estimation as well as the generality of GE-A algorithm to DN-based attack. The second dataset is the SG21M containing one million unmasked AS power measurement collected from an SG2 board at our research lab. We conduct the full key GE estimation on this dataset in order to show that GE algorithm is currently the only practical solution for full key GE evaluation. In the first experiment, we used pre-trained CN and MLP included in the same project trained by first-order masked AES power measurements with 50 maximum desynchronizations. Only the CN model is able to recover the correct key. By computing both the GE estimation as well as the empirical GE estimation, we find that two estimations agree with each other in both successful and failed attacks, which confirms that GE-A is an unbiased estimator of GE and it can be further applied for DN-based attack. We can observe that the confidence interval for our GE-A is also much tighter than the confidence interval for empirical GE even in this one byte case. In the second experiment, we conduct a traditional template attack to break 16 bytes last run key byte by byte based on one measurement point with highest correlation with hamming weight label. By sampling 6 times 10 to the 7th scale probabilities for each key value in 1.8 hours, GE-A is able to provide GE estimation with very narrow confidence interval where empirical method cannot. We can see that Q equal to 50,000 the GE-A confidence interval is now much narrower and we can judge the safety of the device here. In conclusion, we propose a new GE estimation algorithm based on the theoretical distribution of ranking score vectors. We discover the relationship of GE with pairwise success rate and use the sum of univariate Gaussian probabilities to estimate GE. In real implementation, we apply GE-A to both traditional template attack and DN-based set channel attack and give the only practical full key GE evaluation tool. Result shows that GE-A is much more accurate and efficient than all current GE estimations and it can predict the GE for sizes larger than the size of experimental data sets. That is all the contents of this talk. Thank you.