 In this learning dialogue, we will see what data is to be collected in a technogenized learning environment. Tell or technogenized learning environment is a learning environment to teach a specific domain or a specific task or specific problem or specific skill to the learners. Tell is like other learning environments, it contains learning objective. The learning objective can be a student has to learn this task or student has to solve a complex problem so that you will learn some skills or set of question answers to be answered. And it has resources like reading materials, tools, so the student can use these things to answer the complex problem or to answer the questions. Sometimes tell might have a support like a mentor or tutor or a guide, end, feedback or something like that. We will discuss about one such kind of tell environment that is called metal. Modeling based estimation learning environment. Metal is a learning environment created to teach engineering estimation skills to second and third year undergraduate engineering students, specifically for mechanical engineering and electrical engineering students. Metal is created by IIT Bombay Education Technology Department. Here is a short video to show what metal is. Please observe carefully what are the tools in metal, what are the action student can do, think of that angle then let us move on to activity. When the student logged in we will get the instructions then we will move on to the problem. The introduction video will show there is a complex problem which can be broken down into smaller sub problems. So the student can create solution for sub problems and combine the solutions to solve the bigger problem. Here metal gives the sub problems for engineering estimation like quantitative modeling, qualitative modeling, calculation, estimation, evaluation all these sub problems are needed in order to understand the engineering estimation skill. These each sub problem is further broken down into a task. You can see that in the right side of the screen. This is called problem map. After solving the problem map the student has given the real time problem. So student starts solving the problem. So student solving problem map and selects one particular sub topic. When you select a sub topic you will be provided with a set of questions and answers to answer and also you can ask a guide me to understand how to interact with the system or there are other tools like a simulator where student can play the simulator video and try to understand what is going on and also there is a graph to interact with and these graphs are interactive. Like simulator we have some other tools say calculator and scribble pad. In simulator there are two pages you see the second page now where the learner can change the value of the variables is a calculator screen and there is a information about different values parameter values. Now you have seen metal. Metal is the tell environment to teach engineering estimation skills to grad students. And you saw the various kind of interaction student can do like they can interact with the simulator or they can use calculator they choose sub problems on their own that is a one big problem is given that is engineering estimation. In that there are lot of sub problems is there student can choose a problem on their own in any order. So, you have all this information there consider you are a teacher you like to use metal to teach engineering estimation skill to a class under grad students class. What data will you collect about the learners? What data you would like to record from the metal learning environment? Please think your answer you can pass this video and write down what data would you like to collect in metal after completing this activity you can resume the video to continue. So, as we discussed in the MOOC environment data collection there are some common parameters that is timestamp of each event or action learner ID session ID. In tele we do not have the IP address because tele runs in local system and we can collect each individual students ID and session ID. Apart from this common parameters in tele environments we like to collect all the tools the environment as like a simulator calculator and all the behaviors in this tools like a simulator has a lot of behavior they are changing the graph they are changing the value of the variables. So, all this information the behavior student does in the tools has to be captured. Each student can navigate to multiple pages like problem maps, screen, ask for the guide all this information which page the student is navigating should be captured. And response to the questions when in each sub problem there are set of questions to be asked to the students and students is responding to it what is those response. When the student log in when the student is log out with a timestamp these information should be captured in tele environments. Here is a two example to show the raw data in the metal weak store metal is implemented using node.js and the log data the students behavior is stored in a MongoDB. You can see the MongoDB we already add a structure like ID ID is student ID session ID is there and the page ID is exactly which page student is in is in the problem task qualitative model the student has selected the qualitative sub task in the problem map. And the student is asking for int that is a log data timestamp is there. In a second example the student is in the simulator page the student choose qualitative model from their event to simulator page. In another two examples the student is in simulator page but he moved to screen two in the simulator page that is simulator has two pages. And in the next example the student changing the accuracy value in a screen two lower accuracy high accuracy values. So if you arrange these log data in a time sequential manner and look at the log data we can recreate the scenario the student gone through that is a student was in quantity model he was asking hint then he went to simulator the event went to second screen in simulator after going the second screen he went into the lower accuracy change something there and we even identify how much time he spent on each action. So we need to note down this much rich data in a finer grain level we need to note down the level of in each second the students interaction with the system instead of for each second we record a data based on the events.