 Hello and welcome back to the learning analytics course. This is the last learning dialogue in this course. I thank everyone who registered for this course and I hope you enjoyed this course. In this course we saw that what is LA, what is solar community, what is EDM community, how this LA has come up from these two communities. Also we talked about all the types of analytics, learning analytics like diagnostics, descriptive just what is that. And we discussed with detail about what are data to be collected in MOOC, what are data to be collected in teleenvironment and how to collect data in the classroom environment. We did not see much detail about how to visualize this data, the visualization like once based on what is your purpose. If you are collecting data from class of 50 or 60, you cannot plot all the 60 points in the data. Instead you might need to compute the mean or average or the performance in a different buckets like a student's performance from 20 to 30, 30 to 40 something like that. So, it depends on your requirement of the research question. And we talked about ethics and privacy. It is very important before collecting any data. We need to inform the users that we are collecting data and the user also have some rights that they can participate in the course and they can say they do not, they do not want to participate in the data collection process. During the interaction, they decide not to do anything. And we talked about predictive analytics like linear regression. I talked about only one predictive analytics model, linear regression just to say how this predictive analytics model look like. If you are interested, you have to learn more about that. You have to go and learn about different predictive models exist in the machine learning tools like VECA or RapidMiner. And also we discussed the demonstration of the VECA tool. I hope you enjoyed taking this course and you will enjoy doing this project, small mini project for this course. And you will able to take the exams very well. The aim of this course, when I started this course, the aim of this course is to just to introduce what is learning analytics, not to teach a very deep concept in learning analytics or not to teach a multimodal learning analytics, something like that. This aim is to introduce the terms we use in learning analytics and how can you apply the data you collect in the learning analytics and you can interact with someone who is working on learning analytics in their language. That is the goal of this course. I hope I achieved that. So, what is NEST? Please read books, more books. As I mentioned in the first lecture, there is no standard book, but go and read the research papers in Helike conference, an EDM conference. When you read a papers now, you might able to understand the research and you see what data collected, what is the tool they used, what is algorithm applied, read those things and you get more interested in it. And please learn about non-linear classifiers. As I mentioned, linear regression has a linear assumption between these two data. It may not be true always. There are a lot of very good classifiers, non-linear classifiers, ML based classifiers, probability based classifiers. Please learn them. And we saw linear regression because it is very easy to start with. And collect data, please follow the ethics principle and privacy of the learners. Collect data in whatever environment you are working on, classroom environment or the tally or the MOOC or any other programming lab, please collect data then do the analysis. When you collect data, you need to come up with the list of variables, independent variables that will impact the dependent variable. The dependent variable in our example is performance, but in a course project is dependent variables whether the student will drop out or not. So, the dependent variable is based on your research question. Please try different tools. We saw VECA, but try different tools like rapid miner is very good tool, orange. These tools actually gives a free license for the academic use. If you have an email id which ends with academic.in, ac.in, you will get this tool for free. We plan to offer a next course, a tool weeks course with the more deep concepts and rigorous on learning antics called learning antics tools. We will discuss more about the tools we use, not about the data collection, instead we call talk about what tools we will use and different tools not just VECA for the diagnostic antics is there any tools, is there patent mining, is there a tool called rapid manner it can be used, what are the other algorithms, we will discuss that in detail in the tool weeks course. Thank you for taking the course again. Thank you.