 In this lecture, we will talk about learning analytics, educational data mining and academic analytics. As you saw in the last video, the society of learning analytics research is governing the conferences related to learning analytics and also the journal. However, learning analytics is not the only society which work with the learner data. So, there is other society called international EDM society. In fact, it started before the solar formed and the first conference on EDM that is educational data mining conference started 3 years before the learning analytics conference. Like solar, EDM also have the EDM conference and they have a journal. EDM actually focuses on developing methods and tools for data from educational settings where as learning analytics is tries to understand the students behavior trying to model the students in order to improve the students performance also improve the teaching strategy so the teachers can use those strategies immediately whereas, EDM is mainly focused on researchers. However, currently this is not true both EDM and LA working on developing models both communities are involving the teachers in it. So, EDM is mainly focused on research when it started. In EDM the more application of data mining, machine learning has been used. So, EDM is focused on developing learner models to support learning. Similarly, there is another group called academic analytics. So, this has been this term academic analytics has been introduced like a decade ago and it has been talked about applying learning analytics at the higher level like an institute level or the university level or the district level or the government decision on this learning analytics data. Academic analytics is learning analytics applied on large data and is to inform the authorities who can take decisions and policy changes in the institute or the district level or in the education sector. However, if you consider the academic analytics in a college settings or the university settings we can analyze the data from learner management system like LMS like a module or blackboard. What happens here is when we create LMS for this individual course a teacher can access to the how many students is participating in the course what are the assignments they submitted what are the performance of each students. But when we apply this LMS to the different courses in the whole university there are many courses will be run simultaneously in the university many teachers will be teaching the course you can collect all this data and apply it to understand how many teachers is coming to the classes what are the courses is performing well what courses is not performing well. So, these kind of information can give academic analytics for the academics to understand what is happening in the course works what is happening in the college. So, these information will help you to change the course structure in the next semester. However, recently in last couple of years the academic analytics word is replaced by similar to the business intelligence. So, there is no more academic analytics in the research field it is called business intelligence. Let us start with the activity which of the below is not classified as the academic analytics that is attendance of teachers, post percentage in a course X, performance of a school A in a city B or graduation rate of the students. This is a reflection spot question please pass the video and write down the answer for this question. Once you completed this activity you can resume the video to continue. So, the answer to this question is past percentage in course X. All other options are related to academic analytics such as attendance of teachers in the whole university or the performance of the school A in city B in the district or graduation rate of students in a college, where the past percentage in the course is related to a particular course a particular teachers want to know about how many students passed in this particular course. So, this is a not academic analytics compared to the other three options. However, if you want to apply academic analytics on the college level to understand how many courses has a past percentage more than 80 percent or some certain level this might be lead to academic analytics. When compared to the other three options this is not the academic analytics. In our course we will not talk about academic analytics, academic analytics a gray area for us because it is replaced with the business intelligence. We will not talk about academic analytics in our course. However, we will talk about educational data mining and learning analytics. As I mentioned earlier educational data mining and learning analytics are almost similar now and both apply developing the models for learners and also to understand the learners performance to improve the learners also share the data with the teachers. So, we talk about learning analytics and EDM interchangeably we will focus only on learning analytics in this course. We will use the word learning analytics going forward, but wherever when you see the education data mining our course content can be applied there.