[Music] hello and welcome to the learning antics tools causing and Peter this will be a two weeks course I'm long smaller children asked professor that education technology department of IIT Bombay I offered the previous course called intuition to learning antics four-week course prior to this course if you are done this course you can see the same content or similar content for first two weeks however this course does not require any previous so even if you are not done the introduction to learning and this course this course you can start due to web 2.0 the users can generate lot of data for example in social media networks like a Facebook there one point five six billion users monthly active users and Instagram Q it's a lot of data has been generated but what can we do with this data so how to use this data for an example lot of organizations are trying to extract values or patterns from this data and using a lot of cloud computing a lot of servers to try to understand what is a user behavior for example in Netflix the video streaming websites like Netflix the data for viewers users viewing behavior which movies they watch the ratings the likes and dislikes are used to recommend the next video further similarly in e-commerce websites like Amazon the users based on users purchases the system can recommend what will be the next product to purchase in future we can use this big gate of DNA and the systems can come up with the cure for disease like cancers or malaria it's possible because the Lord of data available a lot of DNA data of the humans a lot of medical data is available we can use those data we might copy the queue for these diseases like in other domains in education domain also we are lot of data has been generated now it's because of use of digital tools like learning management systems like a blackboard Moodle or there's a lot of educational apps coming in Google classrooms massive online open courses like MOOCs in Coursera and Peter or MIT course where a lot of this data has been generated it uses interaction with these interfaces has been collected and stored what can we do with this data let's start with the first activity assume that you are a teacher or you may not be teacher but please assume that you're a teacher and teaching the same course for the same class or for last 5 years to the third-year students so you are teaching say third year electronic devices course for last 5 years so you have data of the students for last 5 years but their background their profile their performance in the exams midterms you fewer than assignments xml assignments course you might have some feedback in the classroom all this data you might have it if your if you are to use this data to improve your teaching strategy or to improve the learners performance what step will you take please pause this video and think about your answer I don't know the answer after I didn't answer and resume the video to continue if your answers contain the words like I will collect data or analyze data I want to understand the learners learning or I want to improve my teaching performance so that the learners can learn better if you have this kind of words then you are thinking in learning analytics you already started thinking how to analyze the data to provide those analytics to the your data and can improve the students learning performance let's see what is a formal definition of learning analytics the learning analytics definition is still a debatable one but from the existing resources it be the branch of analysis that makes use of students generate a data for predicting educational outcome with the aim of tailoring education with the aim of adapting the content so that student can learn better or from a lack lack is the organization which started organization called solar which starts a conference called Lac the lab 2011 conference on webpage they posted this as the learning analytics definition learning Attucks is a measurement collection analysis and reporting of data about learners and their context for purpose of understanding and optimizing learning and the environments in which it occurs what does it mean it says learning analysis measuring collecting and analyzing the data not just analyzing just for analysis also for reporting this data to stakeholders of the LD reporting about learners and the contest the sole purpose is to understand how learners learn and that environment and can we do something to improve the learning in that environment in this definition there are few terms what is the purpose the core purpose is to understand the learners learning process and help them so that they can learn better in the environment data collection what data to collect how to collect data in which environment what data to collect how to use those data and analyze what to look for in that data and why we have to analyze also if we want to report the data to whom we how to report the data so or the stakeholders of ending for example for the stakeholders can be educators that is the teacher or the instructor the ability of a real-time insight into the performance of a learner's for example if the teacher has a dashboard of the students working on a particular exam a particular learning environment all the interaction is known given to the teachers by a dashboard a teacher can have a real-time insight into the perform months of learners including students were at risk and the teacher might know this particular students in itself the teacher can go and help them or the teacher finds out all class having misconception in a particular topic the teacher can teach the topic in a better manner or give a remedial content to that so it will be very useful for the educators like a teachers or instructors similarly for students for example if students receive information about their performance compared to the peers in the classroom or their progress compared to the peers in the classroom that can help them to motivate and achieve the goal for example if a student answers a question wrongly say the option PA student selected and it thinks the option might be correct but if we show to the student that in our class almost 40% of students selected this particular option and they are wrong the student might feel I'm not the only one who's given a wrong answer so everybody did not understand understand this concept so I can learn and I the students get motivated to continue if the student thinks I'm the only one don't understand anything in the class then they get demotivated they might and they might not in getting interested to continue further also the student want to know how much I progress in this particular course if the student knows that I progress 40% of content in this course I need to do another say 20 30 % historian can think of that's saying that the student can think that okay I need to put a more effort to continue this course or the student can decide whether to continue the course or drop the course so the Learning Annex can help the students motivate them and encourage them to complete the course and also help them to compare their performance with their peers finally the learning analytics data can be helpful for the administrator to make decisions in the world today with a lot of competition and less number of budgets and the competition is coming in the administrator should take addition that should provide optimal optimal solution for them whether to run a course or not if the administrator knows that particular course this is very less number of students and they know the why and if they can predict how many students will join the course in the next year then they can make a decision whether to run a course or not they should convert the course into some other topic or something like that because it will law it will say a lot of cost in the today's world because doing the global competition in higher education it's always useful to look at the data and make a wise decisions as as you know that learning analytics is a related a new field even compared to the other new fields like a machine learning or artificial intelligence since it's a very new field there's no standard test book available for learning analytics however in this course we will cover the basics of learning analytics in terms of kinetics applied to Education data then we will explain some tools useful for this course like a tools to analyze the data and we will also refer to some content from the textbook so the textbook for this courses and book of learning analytics from the solar community the book is actually compilation of research articles explaining applications of Lda in a different fields so when you read this book you can understand that every chapter introduces something about learning analytics and they'll be applying earlier and some other topic so if you're interested in learning analytics after this course please take this book or read our papers in the recent conferences and you will understand the fields in learning attics then you can pick one field which you like to pursue your research interest in LD I will briefly describe the course outline in this video the week one will be discussing what is learning analytics on academic antics and what is relation between this and with educational data mining very briefly we'll start about that then I will introduce what are the levels in learning erratic the four levels in learning analytics with examples in week two we'll talk about the data collection what data to collect in each environment for data pre-processing we might give you links to the external sources so we want you to go and study what is data pre-processing from the others resources and we might have a assimilation quiz based on that reading exercises also in week two we will introduce a tool called Daybreaker as a freely available open source tool everybody can use and will demonstrate the tool maker which we use it nervous cause going further also will introduce the ethics and data privacy when you collect data from the students what are the things you should follow in week we will introduce a basics of machine learning this course is not mean to teach machine learning also the course may not involve mathematical details of each algorithm so this course is designed for anyone with a very little mathematic background to understand and how to collect data how to start doing analysis so in a week three we will introduce what is machine learning what is supervised and unsupervised learning and very basics introduction to what are the metrics you should look for when you do the machine learning apply the machine learning algorithms for your data also in week 3 we will introduce a tool called orange that tool is commercial for commercial purpose it is not free however academic users if you have academic email ID it's free to use so we will demonstrate that on the tool called orange if you don't have access to that tool you can continue using makeup in this course in week 4 we will introduce descriptive analytics or to describe the data and that what is that session and how to look at the data all the data is generated in a dashboard and we can show using Excel Google sheet you can produce all these visualizations from the dwar we don't need a sophisticated software for the research purpose so we can use Excel or Google sheet to realize this data and in a week fine we'll introduce is another tool called I set this tool is used for visualization also for a diagnosis purpose this tool is developed by our department so we'll demo that tool which help you to visualize the data transfer from one stage to other stage in a time different time periods and we will talk about the diagnostic context and diagnostic analytics thoughts with correlation regression we'll talk about correlations in week 5 in week 6 we will have a sequential pattern mining and the tool for sequential pattern mining will be described the tool will be available free and we will upload the links to this tool and anyone can use this tool if the data is formatted in right format we will show the demonstration of that also will introduce another tool called processed mining the tool called pro-am this is also freely available for educational purpose so anyone can use it if you have educational ID also the pro-am is available for everyone I think so open source so but week 6 will be introducing several tools like a baker orange ice at SPM tool and throw em so five tools we plan to introduce in this course so after the mid semester break in a week seven we will talk about predictive analytics and we talked very briefly about what are the features to select and doing a linear regression using the tool called Baker's so because again I mentioned it's open source so anyone can use it and in week 8 we will talk about decision tree and we'll explain this with the orange so whenever we have a demonstration when we are talking about explaining a party to algorithm with a tool which means we will have a course assignments so you might be you have to use the day tool and we provided data using that data you are to predict something or to create something and report data sensors so yeah so when we are talking about this demonstration of tools will be adding assignments in each week and in week 8 we will have described what is dish entry with orange and we will talk about an a base as I mentioned this course although it's not ml course we will touch all this algorithms which are very basics will explain the concepts how it works then you'll also show how to use tools to execute this algorithms week 9 will go for the unsupervised machine learning that is clustering and will show what is clustering and with the demo in week 10 we will jump to a different fields called test genetics or natural language processing applied for educational domain so we will show you how we can use test analytics to develop algorithm that can automatically grade students essays a simple algorithm and also we will talk about the latest development in NLP that's called word embedding however vectors and in week 11 we will talk about multimodal learning analytics that is how to collect data from multiple sensors like I guess cater for my eye trackers or facial expressions for using webcam log data or some bias and so later like EEG or EMG or GS or data's oh can you collect these status and how can use these data analyst these data to create a model so we will show a bit about what Oh to collect data what is this data is used for and what does this model looks like the final week we will teach about advanced advanced topics in LA and what are the topics it's basically topics covered in the handbook of LA also the latest topics published in lack conference and helium conferences so that's the course introduction so in this course we briefly describe what is definition of LA or LD means your to collect data analyze data then you have to report it at the stakeholders to improve a performance of the students then we described the course outline the course outline involves five tools a lot of assignments and lot of data to collect and a lot of exercises in it and that's all for the motivation video this is a fast video thank you for watching [Music]