 Welcome back to learning analytics course. What is data mining? Data mining refers to the process of transforming raw data into useful information. There will be a lot of data as I mentioned in the first video. The users have started creating a lot of data in social media or in the entertainment industry or health industry. How to use this lot of data and transform the data into useful information so that the company can give you recommendations or provide meaningful advertisements to you. So, how to do that? It is actually the data mining. Data mining is done by analyzing the data using pattern mining or process mining or predicting something or doing correlations, regressions or developing some algorithms to predict which product you will buy next or which move you would like to watch next. For example, this data mining is used in marketing for e-commerce. It supports you which product to buy or it provides advertisements like the targeted advertisement based on your search behavior, your behavior in the browser on the content email can be used to provide your targeted advertisements. Or in a credit risk management, for example, whether based on the user's previous past behavior of how much loan he had or whether the user has paid loan on time, all this data can be used to assess the credit rating and then we whether we should give out loan to the user or not. The banks can take decisions using the data analysis or data mining. And also the data mining can be useful to analyze how students interact in a discussion forum. For example, in a MOOC course, there will be like thousands of users registered and a lot of them will be discussing in the MOOC forum. Can we use these discussions in the MOOC classrooms, the data from the discussion forums to give a meaningful feedback to the learners. This also can be done by data mining, by mining the useful patterns in the discussion forums. So what is educational data mining? EDM or educational data mining is you can put it very simply it is applying data mining algorithms on education from data from education domain or learners data. For example, data mining and you apply data mining on education data it can be called as EDM, very simple as it. So EDM actually refers to the process designed for analysis of data from the learners environment to better understand students in the learning environment in which they learn. So here it is not talking about measuring, collecting data and reporting instead. It talks about process designed for the analysis of data from the environment. The learning environment can be classroom or a MOOC or a technology enhanced learning environment. To understand the students in the particular learning environment and to provide a adaptable content or provide a better feedback or in so that they can learn better. So you would see that EDM as almost kind of a subset of learning analytics. For example, developing a learner model that includes students cognitive states to understand whether student will able to perform the test correctly, whether student is able to complete this course. These kind of information can go into a EDM for a, using the learners data or which pedagogical support is most effective. For example, if a teacher teaches a particular course using two different teaching strategies, which pedagogical strategy or the teaching strategy is most effective. For example, collecting the pre-test and the teaching method, lot of data, interaction data during the class, these data can be useful to give the support and feedback to the learners. For institutes, if we talk about EDM for the institutes, we can use the data like a student's usage of resources, how much a student used resources like books available or LMS is like a modal, the student uploaded assignments on time, student's engagement in the class, all this data can be collected and we can apply data mining on that and we can model the student's interaction behavior in that particular video resources or the institute resources and the institute can take decision whether we should put more money on that particular resource or not. So EDM in the sense applying a data mining on educational data also can be useful to develop the learner model to understand the student's learning in the learning environment, to provide some feedback or to make some decisions at a different levels. Whereas this other world called academic analytics. So if academic analytics helps the institutes address the student's success and accountability while fulfilling their academic missions. Academic analytics help institutions to address student's success and accountability with while fulfilling their academic missions. Or simply academic analytics is learning analytics applied at institutional level or the university level or the regional level or the national level. For example, analyzing the data from learner management system like a modal for the institute level saying that how teachers interact with the students, whether the teachers present in the class all the time or the teacher students interaction in modal all these details can be used by the institute to make a decision about their teaching strategy or the hiring principles or the LMS data can be useful to predict whether to run a course next year or not. So the academic analytics is similar to business intelligence. So what happens in last four years back from 2000 say 16 onwards people don't use the word academic analytics instead they call it as a business intelligence as they use it in other domains. Like business intelligence is a very common and popular word in other domains like health, finance. Now they call the same word business intelligence in academic institutes also is called academic analytics previously. Academic analytics you can think of applying data not just to university level also the district level the government officials can use this data predict whether a particular school is doing good or bad all these things can be done. For example which course will get enough registrations this is for the institute or which school in the district needs more attention like the school is not performing well and why it is not performing well and what are the factors is affecting its performance. So do we need more attention or can we predict which school will not perform in next public exam. Those kind of information can be looked at as academic analytics or business intelligence. So let us start with the activity for this video you learned about what is academic analytics. So which of the below is least important for academic analytics or which of the below is not considered as academic analytics. For example attendance of teachers or past percentage of student in a course S or performance of a school A in a city B like a school in a particular city or graduation rate of students in a particular university. You can pause this video think about your answer write down your answer after writing it down you can resume the video to continue. Past percentage of a particular course is important for teachers than institutes. It can be considered as academic analytics for the institutes also but given a other three options like attendance of teachers which means a teachers of all the courses or performance of a school A in a city B like we are compiling school performance among all other schools or graduation rate of a students in a particular course over the years we think past percentage is not as much as important for academic analytics compared to it is important for the teachers. So we can classify this also learning analytics. So if I put past percentage of a student in a course X it is definitely a learning analytics but given these other three options even this can be considered to be a learning analytics. So what is the difference between academic analytics and learning analytics? So academic analytics provides support to operational and financial decision making for the stakeholders such as management or the executives. The focus is on business of the institution like business of the institutions or focus is on how to improve the education developed particular district what are the new methods to implement to improve the education of the country or something like that focus is very high level whereas the learning analytics the achievement of a specific learning goal or the students performance in a particular score or students company in the particular goal is very very important. Here the focus is on student the student who is learning whether the student learns or not. So the stakeholders or can be students instructors also researchers also can be the institute management but mostly it is for instructors and learners. So the the primary purpose the focus is on student and out help how can we help them to learn better how can we help them to achieve the learning goal. So for us our take about LA and EDM is this. So we can see LA and EDM are interrelated over in this course we will use the term learning analytics for all this EDM and LA related topics. So we use one word called learning analytics. For us the academic analytics is a area which we do not touch it is a kind of gray area. We might talk about that field very rarely but the focus of this course is only on learning analytics and we might call the techniques algorithms in EDM also as LA. This is our take for this course. So in this video we talked about what is educational data mining also what is academic analytics and you might have understood the difference between LA and academic analytics. In this course as I mentioned EDM on LA are same for us and also we use the term called LA going forward and we will not talk or focus on academic analytics in this course. Thank you.