Hello everybody, welcome to this conference. My name is Stefan Urena, I am the Chief Data and AI Officer at ESSEC Business School. And today we are going to talk about using Moodle Analytics in order to prevent students failure. So just a few words about ESSEC Business School. ESSEC is one of the leading business schools in Europe. I just put some metrics about the financial time, so we are ranked ninth as European Business School in 2022, number sixth for the Master in Management 2022, third in the Master in Finance, and ninth for Executive Education. We have 69,000 graduates worldwide. We have four campuses, two in the Parisian region, one in Morocco and one in Singapore. Plus we have an online campus. We're going to talk about it. We also have 220 universities, university partnerships in 46 countries, 31 double degrees. And we also have 169 permanent faculty of 37 nationalities, 24 learning and research years, and more than 1,000 partner companies. In terms of students, we have more than 7,000 students in full-time undergraduate and graduate programs from almost 100 nationalities and 5,000 managers in executive education.  So today we're going to talk about a specific problem which is preventing students failure for online degrees and online learning. So I guess that all of you are developing online programs over the, I have been developing online programs over the years, especially since the pandemic. But this trend is soaring over the years. And the problem is that for us, for instance, we have a lot of certificates online, even degrees like a Master for Executives in Digital Transformation. And that involves hundreds and sometimes thousands of students. So the problem is how can a teacher in a specific course follow thousands of students for a specific course? That is very difficult, even with pedagogical assistance. And so how can we do that? So given the number of students involved each year, tens of them fail, maybe sometimes hundreds, as I said, because we have thousands of students. So how can teachers follow all those students at this level with such a volume? And how can they identify those that are at risk without the contact of on-site teaching? So we found a solution at Moodle. It's a brick that is directly incorporated inside Moodle. It's called Moodle Analytics. It's a component in inside Moodle. What is it about? It's a component that brings predictive models regarding student success in online courses. It is primarily based on their interaction with the Moodle platform. So that means that when students are on Moodle, they are connected, they have interaction with their platform. It generates logs and those logs are used then in order to analyze their interaction with the platform and their likeness to succeed. With this automated system, we can then send notifications automatically to students at risk. I'm gonna show you the platform just after that. And one of the good things is that it is compliant with, sorry, it's complicated to manage too in the same time, it allows you to be compliant with the GDPR because everything is inside Moodle. It's a brick of Moodle. So you don't need to extract data from Moodle and load it in other data platforms to train models and make predictions. It's fully incorporated inside Moodle. So how does it work?  And then you have this interface. I'm sorry, we don't see very well, but I'm gonna explain you. So on this interface, you can see that there are three lines. The first line says courses at risk of not starting. The one that we're interested in is the third one, students at risk of not achieving the minimum grade to pass the course. It's a model that is incorporated inside Moodle. And so we used it and I'm gonna explain how. So when you are on Moodle analytics, you choose a model or a target, we say in machine learning in order to predict something. What we aim at predicting here is as it is written above, students at risk of not achieving the minimum grade to pass the course. So this target aims at preventing students failure. And so how does it work?  So these indicators in machine learning, we call them features, are variables that are correlated with what we aim at predicting. So for instance, we see the one in blue here. I'm sorry, we don't see very well, but it is written course access before start date. And so it is a feature that can contain some signal whether the student is likely to succeed or not. For instance, if a student goes to the platform before the beginning of the course, it means that he's involved in the course more than those that are not going to the platform before the start of the course. So it is a good signal. There are other predictors like any right action in the platform, any right action in the course, read actions amount, all these features capture some signal saying that the student is really involved or not in the course. Then you choose an analysis interval. And then you choose some training data in order to train those models. So the good thing is that all these things are done automatically. You don't need to go to some extra platform from the GAFAMs. You can do all these things directly inside Moodle without any knowledge in machine learning or data analytics.  So the historical courses that you had in the past to train the models to make some predictions. So this is just a screenshot from the documentation explaining the features that you can see inside the platform to make the predictions. So for instance, you have course access after before start date, the second one. It's a binary indicator which set up one. If the user has access to course before the start date, zero otherwise. You have all the description inside the documentation of Moodle Analytics. So I presented you this brick. This brick, which name is Moodle Analytics, as I said, aims at predicting students at risk of failing in their online course. So as I said, the system is good. It is user friendly. You can use it quickly without any knowledge in machine learning. You just have to read the documentation. But now, how can you be sure that the system is precise? That is to say, it's making predictions saying these students are at risk, but how can we be sure that these students are really at risk? That's something that is important to be sure because if you send notifications to students that are not at risk, then it can be a real problem because you will contact students for nothing. And so this is some metrics that we have been calculating. I'm going to explain. So this is a confusion matrix. And it is a matrix that we use in order to evaluate the precision of classification algorithms. So we have two metrics, precision and recall. The precision aims at limiting false alerts. So what do we see? We see in the column in blue, predicted positive. So what do we see? We see that we have 51 people, 47 were real positive and four are real negative. What does it mean? It means that out of 51 people that the algorithm predicted as at risk, 47 really failed in the historical data. In machine learning, we train algorithms and then we test them on new data that the algorithm doesn't know. But we have the outcome and we know what happened. And so we see the predictions of the algorithm and then we compare them to the real output. And we can see that in the end, 82% of the people that were considered at risk by the algorithm really failed. So it gives you a good confidence of the precision of the algorithm. There are not a lot of false alerts. And so you can be confident that the students that you're going to contact, they are really at risk. It is very likely that they will fail. So you're not contacting them for nothing. So that's a good news. Then the second metric is called recall. What does it mean, the recall? It aims at limiting misclassified failing students. So as you can see in the red row, we have the line real positive and we have two columns. The first column predicted positive, we have 47. It's the second column in reality, but the one in the middle. Then the one on the right, predicting negative, you see that we have 18. So what does it mean? It means that the algorithm made some predictions. It predicted that 47 were going to fail. And if we compare to the truth, they're really failed. So 47 were well classified. But on the column on the right, predicted negative, we see that 18 people were considered not at risk by the algorithm, but in reality, when we see the outcome on the left, they were real positive. That means that they failed. So that means that the algorithm failed or missed 18 people and predicted that 47 were going to fail and it was right. So that means that the algorithm was able to detect 76% of the people that were going really to fail but missed 24%. So I would say that on average, when you have metrics above 75% for precision and recall, it means that the algorithm is quite good. So you can be confident using it. It will bring value to the school. That means that you will be able to contact students that are very likely to fail. And in the meantime, you will be able to contact almost all the students that are going to fail. So these algorithms are good and the data model behind model analytics, which captures, as I said, all the logs and all the interactions of the student with the platform brings you some value because it is very precise. So here is, let's say the interface, when the predictions are made by the algorithm, they are made automatically. And when you have them, you can see the list of the students that are at risk. And as you can see in this interface, you can click on the button. Well, we don't see it well but you can select all the students that you want and you can contact them directly by sending a message automatically to all of them. So that's something good because in the end you have a platform that is able to do machine learning in a user friendly way. You don't need to understand all the things behind. There is a procedure and I will recap it at the end. And you have in the end an interface with all the students at risk. You can just contact them directly without buildings, some extra features more than what Moodle already offers. So in order to recap, so you need to have some courses on your Moodle platform because you need some history in order to train the algorithms. If you don't have any courses at the beginning, you need to run a few courses and then you will have some historical data that you will be able to use or that will be used by Moodle Analytics in order to make some training of algorithms and then predictions that are quite good as I just showed you. So first half courses on Moodle. Then you select the target as I explained. So for instance, the one that we are interested in here was students at risk of not achieving the minimum grade to pass their course but it can be some other predictors, some other targets like predicting students at risk of dropping out. There are different definitions. You can see all of them on the documentation of Moodle Analytics and you will be able to choose what you need given your problematics. Then once you have selected a specific target, then you will need to select the predictors. The predictors in machine learning, we call them features and the features are variables that you think that are correlated to the target that you aim at predicting and the algorithm will use them in order to be able to calibrate and make predictions as accurate as possible. Then once you have your online courses that are already inside Moodle, you selected the model that you are interested in or the target, you select the features, then you run the algorithms and you evaluate them and you see their precision. And so one measure that is interesting, it is the harmonical mean of the precision and recall that we saw just before, it's called the, sorry, the F1 score. And so this F1 score is something interesting and it is already inside Moodle Analytics. So when you evaluate your algorithms in order to see their precision, you will have this measure. It is not called F1 score, it's called precision, I think. I think that they use this word because it is easier for people from the business to understand that we are talking about precision if you are not from a statistical background, but this measure gives you the precision of the algorithm. And if it is above 75%, as I said, for instance, it's something that is very valuable, even 70%, but you will see what you have inside the signal that the system is able to capture from your courses when you run it. Then when you are confident with your algorithm, with your system, you have to include the teachers from those courses in the process in order to explain them what you have been doing and in order to have their agreement, in order to use these systems on their classes. That's something that can be a challenge sometimes because you have to explain the teachers what you're doing and it's not very easy sometimes, but take the time to explain those things and then you will be able to run them with them. Then there is also something that is important. I mentioned it, but quickly, for instance, for instance, us at ESSEC, we have online courses that last five weeks. So we launched these algorithms one week after the start of the course because we want to make those predictions as quickly as possible in order to prevent failure. So one week after the start of the course, which lasts five weeks, we have these algorithms that run and that are able to capture the students that really at risk. And so that's something that is interesting and valuable to us. Once you have the algorithms that are running, you have some predictions, then you have to explain after the teachers, you have to explain the pedagogical assistance how to contact the students from the interface that I showed you just a few minutes ago in order to send the messages, to tell them, hey guys, you are probably lagging behind the average of the cohort. You should hurry up to catch up your delay, something like that. You are free to choose the kind of message that you want to send. And then another advice is do not hesitate to repeat that a few weeks later or a few months later, depending on the pace of your course. But the more you will run those algorithms and the more you will try to detect over the time the students at risk, the less likely you will have students that will fail. So I encourage you to run that different times. So thank you for your attention and I'm available for some questions. Thank you. Thank you. Well, thanks for this very interesting presentation and it was so interesting to see some insights into an institution that is actually using Moodle Learning Analytics. I have a bunch of questions since this is also my interest in my research, but maybe I'll just ask one and then see how much time we got. So my first question is, could you just remind us when you started using Moodle Learning Analytics and have you observed any decrease in student dropout since? Yes, we carried out some A-B tests, but we did it only once. So we want to carry out more A-B tests in order to compare the effect of what we are undertaking here compared to doing nothing. So what I can tell you from the first A-B test was that we saw that there were 11% less failing students in the cohort in which we carried out these actions, but we wanted to carry out more tests in order to have more statistical evidence that this has impact. The point is that there are two things. The first one is to measure the precision of the algorithms. So this is certain and accurate. We measured it. We made some extra developments in order to have confusion metrics because you don't have them in Moodle Analytics. You just have the F1 score, so the global precision, let's say, and to have more in-depth analysis, we developed a recall and precision and the confusion metrics. So what I can tell you here is that the precision that we have for those algorithms in order to detect students at risk is the numbers that I showed you. So it's quite high. It's 76% recall and 82% precision. Then in order to measure the effect of the intervention in the students, we just saw that 11% less students were failing in the first test, but the thing is that we just sent one email and so we are right now carrying out new experiments, sending more emails as I just told you to try to decrease the number of students failing. So that's something that we are carrying out. Maybe I will be able to give you the numbers by the end of the year and we can keep in contact if you want to talk about that. Thank you. Hi, thank you for the talk. I was interested in knowing how long does it take you approximately to understand that the metrics that you're getting are precise. You were saying that you need to be running the courses to calibrate that the algorithm is working. So how long does it take you? When can you say, hey, this is working now? Well, if you have historical data, so the training data in machine learning, you need training data and test data. If you already have some courses inside your platform and let's say three or four, for instance, then you can just go on the Moodle Analytics documentation, train some others. As easy as I tried to show here, but I admit that the quality of what you could see here was not as optimal as I expected, but once you run the algorithms by following the Moodle Analytics documentation, you have the results almost in a few minutes. So let's say that the cost, the barrier to entry is just to read the documentation and undertake those experiments by just going to the section Moodle Analytics and Analytics Models, choose the target that you want, then select the predictors, then the analysis interval, depending on the timeline of your course, then choose the historical courses that you have and then run and you will have the output quite quickly, the precision. Yes, do you have any experience how this is impacting performance-wise the system when you're running an analysis and is it better to run this in the night hours or when do you run your analysis? Okay, well, you can automatically configure them and they, at the time that you want, so it's true that we do that during the night, but when you want to do it manually, you can, it is better to run it from the common line rather than from the platform, but you can do it from the platform, but it's better to try to do it from the common line for programmers, the common line, you know, the black screen from which you write code. Hi, hi, thank you for breaking it down. This has always been like a mystery box to me. What I wanted to ask you about is the read indicator amount. How does it actually predict or calculate, does it calculate the reading by dedication or by, you know, the logs within the course itself? How does it actually work? Well, it's a machine learning algorithm, so it takes a lot of variables as vector inputs and then it's a function that tries to minimize its error, so it tries out many times until it is calibrated well. That means that the error is as low as possible and then you run this function on new data and you see the performances and those performances are those that I showed here on new data that the algorithm hasn't seen in order to evaluate it. So I can give you more information if you want after. Yeah, well, thank you for the talk. As far as I understand the algorithm, you need to have similar courses, the one you trained upon and the one that you're predicting. Is this is also your experience or did you generalize it to be able to predict even, because instructors all the time change the course? Yes, I understand your question. You mean whether the structure of the course has impact normally? I mean, well, from our experience, we have courses that have roughly the same timeline. So I cannot say from courses that are very different, because if you make the predictions after one week, if you make predictions one week after the start, for instance, if you have a six months course and a five week course, people are not going, the students are not going to react the same way when they're going to be given the course. So I would say that it's probably better to try to make the training on courses that have a structure that is quite similar, even if it's not exactly the same, but not six months courses and five week courses. Even if the platform, let's say it does not care, but it doesn't make sense to make some predictions from courses that are so different. Any domain? No, you can have, it is, we don't care about the domain. Whether it's finance, corporate social responsibility, entrepreneurship, whatever, it works for any domain. You're welcome. Sorry? No.  Well, if you have any question, my email is urena, so u-r-e-n-a. You will be able to see my name on the Moodle schedule at essec.edu, if you have any question. Feel free to ask, I will answer with pleasure. Thank you.


Going on everyone Sam here with Lance sports guards ripping 22 upper-decks guy box middle-universe champions hobby 1 box break number 38 random pack style I'm gonna do the random now. We've got 15 participants 15 numbers 1 through 15 representing your pack number. Well, it ice We're gonna go six times one two three four five and six Kate and a down to Mike I six times on your squads one two three four five and five and six 14 down to 12 50 cent featuring nickel back. I have seen that one before Scott. I have seen that one before So Kate and a with 14 filibest 13 Paul H7 Kale F5 Rory B9 Bradley P11 Tony H6 Rory B1 Jerry F4 John H2 Jerry F10 Chen K 15 Paul H8 Rory B3 and Mike I with 12 so Get this copied over now Yankees did just announced. They're still in on Soto They're definitely still in on Soto for sure hundred percent. They're still in on Soto and Yamamoto plus it just further Getting for do go just further pushes the narrative of how bad that mookie bed straight was I get to laugh about that. Haha. Haha. I Laugh Let me get these organized. I don't think I thought he's going. Thanks. I'd love that But I don't think so. All right. Good luck. So again, I'm gonna have packs one through three four Or one through five six through ten and eleven through fifteen No, there you go. Yeah. No, no, I agree. It's a no way. Oh Tony goes to the Yankees. Hurry up pack one. Good luck, Rory Caleb Houston looking into the future. Henry Davis and Diego Jota. No, well, Tony's not going to the Yankees. No shot John H2 now. Good luck here John Desmond Ritter, Mondo Cruz, Scoot Henderson basketball Bryce Young, Metal Shredders and How do you say ukulele again? We ungulele. We ungulele. Jesus. That's tough. Rory at three So in Celestyn at 2,500 degrees, steal the draft AJ Griffin and Colin Morkawa. Number four Jerry F. Tony could be a Dodger. Very well to be a Dodger. Probably will be a Dodger Steven Bergewine Tiger Woods and Bryce Young. I know Davion Mitchell instead of Halley. God, dude Halliburton, Sabonis and Fox. Kale at number five. Good luck, Kale. Yeah, I don't think they were getting Doma for PMG. Davion Mitchell, though. Azard Thompson. That's not a PMG. That is a Z-Force. That's one there. I don't think Pacers were taking that deal. Tony H at six now. Good luck, Tony. Tony or Troy? Why do I feel like his name is Troy instead of Tony? Okay, we're good. We're good. Tony H. Sorry, Tony. Oh, we got a PMG. Can you slow this? That is a Garrett Wilson. Red PMG to 100. Congrats on that, Tony. Like, damn, Scott's really committed to that one then.  Paul H. We've got number seven. I don't think they would have traded Doma for Davion Mitchell. Another PMG? CJ Stroud to 75. Going to Paul. Congrats, man, on the PMG. What a time to get that. Good stuff, Paul. That's nasty. Two PMGs in this box. Not bad. Get that right into a mag. Kenneth Walker and Jeremy Sohan. Paul H also at number eight. Let's go. Paul, he's in chat. Hell yeah, Paul. Good stuff, buddy. That is not bad. Not bad at all. Walker Kessler, Armando Cruz and Kendall Brown. Rory at number nine. Riley Green, Z-Force, Bass and Metal Shredders and McGowan's. And number 10, Jerry F. Henry Davis, Sohan, Caleb Williams, Intimidation Nation, Caleb Williams, Bass and Leake Willis. All right, Bradley Strive at number 11 now. Strive at number 11. Doxon Strive. Who's that? No, that's his first name. Who's that? Doxon, dude. Give him his first name. I am. 50 cent joke one more time to really put it home. Lissa Lu, that is a cut above Caleb Williams. Nice one there on the insert. A cut above Caleb Williams and Brown and Cunha. Mike I at 12 now. Good luck, Mike. Find the last one. Strive, Rookie, Jota, Amin Thompson, Z-Force and Kenny Pickett. That wasn't bad. The cut above is pretty solid. You only get like one of those a box usually. John R with two in the mosaic mixer. Keith L with a 10 box spot. I think we're under, we're under 10 now. Jeez. We've got Phillip S with the pack number 13. We're losing it, man. Henry Davis and Malik Willis. 14, Kaden A, good luck here. Kaden, Tiger, Garland, Master Teague, Will Zalatoris. My guy in PGA a lot of times. Zali and Marcella Meyer. And Chen Kei at number 15. Chen Kei. So, the caddy from Happy Gilmore. Zalatoris. So Thompson. Shadon Sharp. And Peyton Watson. That'll do it for the break, everybody. Thanks again for the fill. Can't do a recap. Certified. Up next.


Despite the widely held belief that greenhouse gases are the primary cause of global warming since industrialization, some argue that solar activity may also contribute, but evidence suggests this contribution is less than 10 percent.


What's going on, everyone? Sam here with Layton Sports Garden, Drippin' 22, Bowman Inception, Baseball Hobby, 8 Box, half case break, number 6, Random Team Style. There's a look again at your random results. As always, you can find these on the website, under the results tab, as well as the link that's in chat. It's good luck, guys. Let's do... No, we're good. 16. Yeah. Here we go. Good luck. Whoa, whoa, whoa. Losing it over here. Oops. Jordan Byers for the Phillies. Base. Printing play. Nice. One of one. Going to the Dodgers. Alex DeJesus. One of one. Nice one there. He can recap it, though. We're out on that LA. We've got Ellie De La Cruz. Nice one there. 37 out of 50. Good man, Wahoo. He's a good man. Nice, Ellie. We've got 6 of 99. Yes, sir. Mercedes. Going to the Twins. Nice one, Minnesota. Vincent B. Yes, sir. Yes, sir, Mercedes. And Yonatan Classe. 6 out of 25. That's going to the Mariners. Nice one there. Congrats, Seattle. Drew Kay. There you go, Drew. Okay, guys. 20 to go on the next one. Number 7. We have 7 through 12, still available with spots. Definitely check those on out. This has been a lot of fun today. Again, 16 autos per break. I've hit some big ones. Hit a 1-1 already. Looks like we got a patch auto in here as well. So good luck, everybody. Here you go, Velazquez. And Eddie's Leonard. There's a blue. Samuel Munoz. 88 out of 99. Dodger's crushing it right now. Because they've got a Jose Ramos right behind it. That is 9 out of 200. And nice one here. Going to Texas. Ryan D. Luis on Hella Cunha. That is 3-color and 1-color hatch auto. 13 out of 50. Middle child, Cunha. Ronald's younger brother. Brian's older brother. Very nice. There you go, Merlin. And guys, just 20 to go on the next one. We're looking to sell that on out on Oat. Miquel Hernandez. Anthony Volpe. That's a gold. Colson Montgomery. 12 out of 50 for the White Sox. We've got 87 out of 199 is Taj Bradley for the Rays. And another jersey auto this time. Pedro Leone. 145 out of 200. Nice one there. Pedro Leone. I know. I'm excited for Volpe. Dominguez looks pretty good in spring training, too. Wahoos. Yeah, I'm excited on both of them. Imagine Volpe might. I don't know. Do you think he'll make the opening day roster? I think he'll be like a later in the year call-up. I don't know, to be honest. Willy Vasquez and George Valera. There's Edison Paulino. 22 out of 75. August edition. Yeah, it makes sense. Frank Mazzicato for the Royals. 191 out of 399. Nice one there. Congrats, Royals. Shane taking a spot. Justin taking a spot in 10. And we've got Colson Montgomery. 49 out of 99 for the White Sox. Yeah, sure. You mean like in terms of the hits, Garrett? What do you have in this? I know you're in this. Oh, you got the Padres. Padres aren't bad. They got a few guys in this east coast. Yeah, I got you. No problem, Garrett. You got it, buddy. There's James Wood. 8 out of 75 for the Padres. Speaking of one of the big players for your team, Garrett. There you go. James Wood. Going to the Brew Crew. The Brewers. Felix Valerio. 225 out of 250. Felix Valerio. Brewers. And then going to the Rays. 69 out of 99 is Curtis Mead. Curtis Mead. Furhahn taking a spot. We're down to 18 to go. And the next one, everybody. 18 to go. Number 7. Who is it? Jay Allen. 46 out of 50 for the Reds and the Gold. We've got going to the Giants. That is going to be Will Bednar. 41 out of 399 for the Giants. And nice one here for Todd D. and the Yanks. How about Roderick Arias? 17 out of 75. Nice one there. Congrats on that. You got a shot. What's that? Number 7. Yep. Sir. Breaking next, guys. Number 7. Guzman and Jonathan Mahia. This is an Orange Cherry Ford. 7 out of 25 for the Mariners. Nice one there. Congrats, Seattle. We've got Alex Benelis. That is 222 out of 399 for the Red Sox. There you go, UC. Alex Benelis. And a Brian Bayo. Also for the Red Sox. 59 out of 75. There you go again, UC. UC box right there. Sweeping the hits. Sweeping the auto. And last pack of the break. Last box, I guess. Box slash pack. Oh, thank god, UC. Let's go, baby. Who do you see? That was my favorite emote, dude. The one that Manny just dropped. I think that one's so funny. I think that one's so funny. Andrew Painter and Willman Diaz. We do. Base, base. There's a Trace Weenie. 24 out of 50 for the Inks. There's one there. Another one going to UC. Tyler McDonough. That is 23 out of 399. Tyler McDonough. And to finish it off, we've got Ricardo Cabrera. 97 out of 100 on the silver signings for the Reds. Cincinnati picking up a nice Ricardo Cabrera. You see with another two. Thank you, buddy. Let's do a quick recap. We've got Tyler McDonough, Brian Bayo, Alex Vanellis, Will Bednar, Roger Garius, Curtis Mead, Felix Valerio, Frank Mazzicato, Ricardo Cabrera, Colson Montgomery, Pedro Leone, Todd Bradley, Luis on Helicunia, Jose Ramos, Jonathan Colasse, Jocelyn Mercedes, and one-of-one printing plate, Alex DeJesus. That'll do it for your break, guys. Thanks again for the fill.


Hey guys, how's it going? So there's the session title make sure you guys are in the right place. My name is Chris straw. I work at Acquia I'm a project manager Turn program manager there, which is a fancy way of saying that I work with Acquia as big customers on there the big things they want to do in Drupal and mobile has become a relatively hot topic lately inside of the the Drupal Enterprise space and I first became pretty passionate about this topic in 2010 when I was at Oskon and I heard Tim O'Reilly talking about personal sensing technology and at the time I had a very close friend that was really into the idea of personal sensors She was the first person I ever knew with a Fitbit and since I was able to speak with her and get some feedback on this stuff and Hopefully be able to share some interesting knowledge about personal sensing with you guys so How many data points do you collect on a daily basis? There are some things that? You might carry around the collect data for you probably most everyone in here has a phone It's pretty popular also at conferences to take around a pedometer just because people like to brag about how many steps They take in between their sessions You know you guys probably also have an iPad some credit cards in the wallet Maybe you have a metro card or a subway card if you live in a big city all of you probably took airplanes or buses or taxis to get here today and You're probably a member of a social network or three these days and then You've probably used a mapping application And you know Google Maps or Bing or something like that from your cell phone or maybe from a desktop browser in the past week or so All of these things are capturing public data points about you So all of your search histories all of your social network interactions all this stuff has been captured And you're leaving little digital breadcrumbs around the internet about your particular browsing habits And it happens almost unconsciously now We've gotten to the point where we don't necessarily even really think about The fact that every single action that we take online and with our devices that are in our pockets leaves a trace of something that we did So lots of people think of this as mobile spine You know there's a lot of very well-known capabilities I think that people are generally cognizant of most people know their phone has a camera Most people don't realize it never really turns off Most people know that their their cell phone has a microphone on it, which also doesn't ever really turn off Most people know their phone has the internet But they also don't really think about the fact that even when they're not actively using their phone It's sending messages back and forth about what you're doing your habits lots of things about you And then lastly there's there's GPS and other positioning systems within people's phones They're constantly collecting data about Where you are if you're moving if you're stationary You know It's pretty easy to for a GPS to tell if you've just gone on an elevator because it'll have an elevation point for you at the beginning It'll go dark for 30 seconds while you ride the elevator and then pick up your elevation point again at the end There are also some other not so well-known capabilities that Then mobile devices have so also on on that cell phone of yours you have an accelerometer An accelerometer works just like a pedometer and it's going to tell exactly how many steps you're taking It's going to know things about your breathing habits Things about you know you when you're sleeping all these other sorts of things that that accelerometers combined with microphone technology Can pick up and learn about us Most of them have thermometers now there's not a ton of of Mobile weather stations stuff out there yet But that's one of the big ideas behind mobile devices is this idea that we would We would be able to have temperature information about you know how warm or how cold we are or any sort of other Weather-based information about the environment that we're in For the more you have a clock it's pretty basic But the clock is really key to all this because your phone is able to keep track of the length of time that you're doing certain things And that's very important for understanding the action that you're taking at that at that given moment and The compass aspect of it and that's pretty self-explanatory but one thing that has been coming up in in Mobile device land lately is the ability to monitor the power output of your phone So at any given time you have a bunch of different applications that are running on your phone And I was just reading an article on rewrite web the other day about how all of those mobile applications That we all use that are free to download They're constantly sending data back and forth across the internet and that's draining the batteries from our phones and You're actually able to profile The power usage and power consumption on your phone and understand what mobile apps you're running So any application that has access to Monitor the battery life of your phone can also know all the other apps. They're running on it So the point of this is all our phones know more about us than we think Even the most technically savvy among us have probably heard one thing that was surprising that tirade about mobile devices But there's there's more to it than that. There's also digital fingerprints that we leave in in other ways We've lots of application data about us and all of us use mobile apps Most of us use computers iPads that sort of thing Those all through their applications are also collecting information about our habits There's also things that we like to refer as proximal data Proximal data is data of the people next to you It does not necessarily matter so much if you forgot your cell phone today But if someone else notices that you're here and tags you or talks about you or checks you in or you know Whatever else what name your you know for square Facebook like application of choice Via proximal data, we can also understand a lot of you usually proximal data is used to collaborate Primary data so data that you're presenting but oftentimes proximal data can be used in place of primary data and Then lastly you have environmental data. So there's there's lots of environmental sensors It's talks not really about these things, but it's important to know that every time you swipe your credit card Most of the time when you're in taxicab You get photographed or you leave little digital fingerprints around via your your wallet and the transactions that happen in your wallet so This is all pretty cool. Actually. I mean we ended up with try quarters I don't know how growing up as a little kid Watching Star Trek with my dad when I was like 10 11 years old that That I saw Spock with that try quarter and I have one of those in my pocket That is so cool The thing that I think that We always thought about for mobile devices We always thought about mobile devices delivering us information We want to know more things about the world around us But it's kind of flip-flopped. Um, these devices now instead of telling us a lot of data, which they do do They're actually used to gather a tremendous amount of information about us And I'm not sure if we ever really thought about it that way until recently and we can gain a lot of insights into human behavior So one of the big shifts in that mentality has been from this idea of active participation versus passive participation So when you're talking about actively sharing something you're making a conscious effort to log on to Facebook and to type something in or to Check in or any of those other things that require an action from you to actually do But now there's a whole lot of passive communication that's happening when all of us, you know download an app from The the iTunes store, you know we all we all look at that license agreement for about a quarter of a second before hit accept and We just gave away All of our data that could be captured by that program So once you opt in you're constantly passively monitored until you opt out again and the point of that is is now that there is is Data being collected about you even when you're not consciously providing that data. It gives us a lot more information to work with So one of the things that I don't want to dive too deeply into here But it's definitely worth mentioning is this is scary for a lot of people because people think about the idea of privacy Especially people that have Things in their private lives or controversial figures or whatnot that they don't necessarily want those things to be public Privacy is a huge concern and we have yet to solve it. There's a lot of bills going on in the European Union right now around Privacy and ownership of personal data The United States is also considering similar things with the Obama administration So there is a there's a whole lot to do here And this is kind of the bike shed of this talk is I don't want to dive too deeply into Privacy implications. I'm sure it'll come up and if any of you have questions about it I understand at least a little bit about it But I really want to try to focus on something different and and what that is is I want to look at How this data can be used within society to collect information about all of us not any one of us So look at some early adopters of this. I don't know if you guys have heard of Hassan alahi He was targeted By the United States government as a watchlist candidate not that long after September 11 He had been traveling around the world and it just got back to the United States and was detained by the FBI for for some time and He is an artist. He travels around and does art installations and what Hassan found out is that You know, he was always in the system from this point on and he was questioned on a weekly or a monthly basis about his activities And so rather than wait for the FBI to come to him He started going to the FBI. He got the card from from one of his agent handlers and said, you know what? I'm just gonna let you know where I'm at and This turned into something over time Hassan had been doing this for a couple of years where he would send, you know Oh, hey, I'm going to Senegal and by the way, here's a picture of me with, you know, a tiger or something like that I don't know a pony But he would start sending the FBI agents his vacation photos And soon it became a lot more than that They were sharing all sorts of personal anecdotes and all sorts of other things and then his FBI handler left And so it was back to that same routine of, you know Get called in every couple of weeks to go get questioned by the FBI And so what he decided to do is he decided to turn into an art project and he he set up a website Which you can view right there and it's literally his entire life where he is at every moment I think it even has the phone calls he makes It's literally everything about that guy in a timeline and over the years He's amassed hundreds of thousands of photos data points everything about his life is tracked on the internet Another person that that is kind of inspirational in this is a guy from Microsoft by the name of Gordon Bell and What Gordon Bell did and this was actually largely controversial at the time is When he became an executive at Microsoft He started carrying around a portable camera around his neck and he took pictures of everyone he met with every day mundane objects like the food he eats Every single thing every time he interacts with a piece of technology He's filming what he's doing and he's amassed what is considered to be probably the largest online repository of Computing and technology in existence. It's almost like a museum of Technology through this guy's eyes over many many years It's one of those interesting projects to browse. I highly recommend I know most of you don't like to type in a Microsoft comm donating But I highly recommend looking at that site because it's a really really fascinating look at technology so these guys were at the forefront of understanding what mobile sensing really meant and While I said I don't want to focus too much on Individuals I think that it's important to realize that these guys are pioneers in a way of understanding this whole idea of mobile interaction and personal sensing So let's get back to what the meat and potatoes of the talk is if you will Populations of people are always more interesting than individuals The yes, there's privacy concerns associated with individuals. Yes There's individual people that we can look to to try to understand this space But really it's about understanding us as people in a population You know, there's there's a lot of different sizes of population you can talk about But really we want to look at insights into us as humans and to really understand our own behavior So I'm gonna show you guys some examples here in just a second So one of my favorite sayings is a population a statistical population is not just a collection of anecdotal points You have to define a population by a measurable group of people that have common characteristics And that population data should then be able to be sliced and diced based on those characteristics Finally the the size of the population is not really relevant. I mean you can have a population of this room We're all at Drupal con. We're all attending this session So we can be considered a statistical population and we could probably get some interesting stuff from us about our mobile sensors right now But it doesn't necessarily matter if we're kind of talking about all you know However many billion people are on the planet or if we're just talking about the people here so one of the most interesting ways of gathering a true population of mobile sensing Devices is this project right here is sponsored by Nokia. It's the LaSenne data collection campaign What Nokia did is they said well it's it's Really hard for us to get people to opt-in to having their mobile devices monitored by us Moreover, we don't really have an app to sell that provides a tremendous amount of value so we're just going to give 200 people cell phones for free for life and The caveat of that is we have to be able to collect every bit of data about those people and so it started in 2009 and They've been operating since then with these 200 individuals that live in Switzerland, and they're literally tracking everything about their habits at all time and They've got in a tremendous amount of research published around the human behavior habits of all of these people And they're not looking at any one individual. They're looking at the collective habits How they interact with each other how their friendships work like what creates interest among people that will allow them to actually stop And talk to other people and these phones are incredible because they have Bluetooth sensing technology They have the internet they have GPS all the bells and whistles that smartphones have But they also whenever two people that are a part of the project come close to one another they share data and that really enables a really interesting thing because it creates not just this idea of Nokia watching over all of these people, but it creates this network of sharing between all these people so they can all learn about each other and One of the big research projects was was understanding the relationships that they created via this project and I really encourage you guys. I got a link to this up in the upper right corner It's kind of hard to read. I encourage you guys to go look at the research These guys have done because it's really brilliant and amazing stuff So let's think about the size of this data for a second Zenga is you know a popular casual gaming company that has like six of the top ten Facebook games and You know, I'm sure everybody's at least seen words with friends. If not played it themselves If you look at how many people use Zenga 360 million people and the vast majority of them something like 80% of them connect via their mobile devices At that rate that is so much larger than all of those other things like we're talking about you know Way bigger than the entirety of Xbox live way bigger than all of World of Warcraft Way bigger than all of these things that we consider to be really big technology platforms And because the power of casual gaming reaches out to us and provides us with this little bit of entertainment We're willing to give Zenga everything about ourselves in order to play games Furthermore if you look at Facebook Facebook as of you know December, I think Claimed 845 million monthly users And of those about 60% Connected via mobile application. That's not using your mobile device to go to Facebook comm. That's actually using a Facebook mobile app That is really tremendous that 60% of people are using Facebook via app so looking at the population there you have hundreds if not thousands of data points that are collected about people that use those two applications and They do it willingly and freely and almost without thought If any of you I mean I'm sure some of you guys use both Facebook and a Zenga game There are literally thousands of data points about you because of your use of those applications But what is really interesting is that you're able to cross-reference all of these between Associates, so you know you play words with friends against other people You also have other sensors, so Facebook for example is able to tap into a lot of hardware features your of your phone around location And and non-personal sensors So when you report that you're at Drupal con that is in and of itself a data point even though It's not directly related to the device that you're working on and Then there's also loads of information that is unrelated to that application that is collected I know that that my phone collects information about my music habits and that is often shared on Facebook if I'm playing Spotify on my phone So every Spotify song I listen to on my cell phone gets linked back to Facebook and other people get to look at what I'm looking at All of those things are again these little digital breadcrumbs that we leave about our behavior and habits So seriously Think about the amount of data that we're talking about here We're talking about hundreds of millions of people collecting thousands of data points every month That is a tremendous information repository just in two applications that is Could provide an incredible insight into our human lives and our behavior And again not any one person's behavior But think about the ability to track things like the spread of a disease Because people are posting about it on Facebook Tracking political trends tracking any of these other things that that you could look at by looking at you know a billion points of data about human beings and These are some of the types of questions that you could ask a data set like that Where were people when they were playing words with friends? I mean were they sitting on the toilet were they You know riding a bicycle that would be really hard. Hopefully they weren't driving You know how long did they use them What was the length of time that people interacted with these sorts of things and what does that mean about our behavior? You know, are you are you trying to close down Facebook real quick while you're at work because your boss might be looking over your shoulder? And then what actions did you take and that's really where the the key is is understanding the actions So again just sort of reiterating what I just said You're probably bored if you're playing Zenga Or you're listening to me present at Drew Baccon But anyway, the idea is is that you're able to model Pieces of human behavior based on our use of these applications So this does a lot of interesting things The implications for healthcare are huge if any of you have a Fitbit You guys can know relative to other people that have Fitbits what your exercise level is We could also like I said track the the spread of contagions Zombie outbreaks for example There's a lot going on in the protest space about this I mean the protester was a big topic this year. They're this past year rather and in protests you're seeing applications like Ushahidi and other push-based data applications out there that are really aggregating a lot of information about Protesters and it's on both sides You also saw a lot of things with Not necessarily protests related stuff, but natural disasters the earthquake in Haiti is a great example of that Because of our ability to collect information about mobile data we through Ushahidi people were able to see Which roads were open where security centers were where hospitals were still open all based on the aggregation of this mobile data And then other things where it could be interesting is I mentioned the mobile weather stations earlier But the cartel project from MIT is all about collecting information about the speed at which you're traveling Based on the GPS in your cell phone And if you're in your car it'll know you're in your car and it'll be able to figure out where traffic is blocked And you know any of you that That have been in Boston and driven, you know any of the highways in the middle of town Can understand why the folks at MIT would be interested in something like that Aqua is based in Boston and it sometimes takes you know 40 minutes to get to the office from downtown So those sorts of things allow us to have an unprecedented window Into all the different facets of our lives that could benefit from understanding more about how other people are experiencing the same thing So here's a few projects. I wanted to point you guys to I Mentioned the LDCC earlier on a previous slide, but there it is again The open geocoder project is really fascinating It's a mobile device app for mapping where it takes map points and tracks your your GPS coordinates and provides sort of a quasi Open-layer like overlay where you can present. Hey, I want to know For example, I live in Portland, Oregon I want to know that I'm walking around in the Pearl neighborhood in Portland, Oregon And so that open open geocoder allows you to say I'm in Portland, I know that by my GPS coordinates other people have tagged where I am standing as the pearl So I am actually in the pearl Open sense which is Interesting project Not to be confused with HTC's opening of their sense UI that's different Open sense is all about again a mapping application around personal data and sensor data It's probably the most developed API I've seen around Being able to collect sensor information. So any of you that are more into the development side of things I really encourage checking that out. They're actually some companies They're starting to do some really interesting things based on the open sense ideas And then I mentioned cartel with the the folks at MIT monitoring traffic patterns So eventually this all has to come back to triple, right? We are at Drupal con and as much as I love to sit and talk about mobile sensing technology. There is really a point to all of this You know the important thing in Drupal is is Drupal is really known well for its data model and There should be ways for us to look at this mobile sensing information and be able to aggregate that data and store it in Drupal Maybe not to the multi hundred billion Data point model, but being able to store Aggregate data that has been pre-mined or pre-filtered and then visualize that data So Drupal on the the end of this can work in mobile applications as that content store as The collection of the mobile data and as the visualization of that data And that's a really powerful combination and I think that there is a lot of interesting stuff to be done in Drupal around these projects and You can see one of them there maps.ed.gov has a broadband penetration map that is a visualization of Broadband data now they don't store their data in Drupal the map is pre-rendered Except you can still browse it and see it in a way that is really interesting using Drupal So those are some ways that we as a community can look at how we can interact with this this new idea of mobile sensing technology and Hopefully jump on top of it and do some really interesting things So that's it if anybody has any questions would be happy to take them They said to go up to the microphone if you had a question or if you are too shy you can shout it at me And I'll repeat it for you So the question is let me see if this works In the ideas of responsive design We want to be able to create better UX via responsive design for mobile devices Is there any way to tap into personal sensing technology to enable that to happen? Is that a fair statement? So the idea behind responsive design we don't get to know a lot in the web app space yet about devices But it's coming and right now we get sometimes things like orientation. So a website will change when you turn an iPad sideways We get geolocated data often But there's not really you know access to the thermometer or something like that on your cell phone I think that HTML5 is enabling a lot of that to start to happen where we're we're figuring out how to get at the sensing portion of the device easier and in terms of the responsive design idea, I think that it especially related to aggregate populations It would be interesting to be able to do ad hoc mobile usability testing over a population of people so you have a Mobile website that is out there and via some sort of data collection system on a sensor You're able to see how people are using that site when they're using that site and and understand when people want to visit your site and How they're using it when they're there on a mobile device and what's working? What is it? I think if that's where this is really enabling that sort of thing I mean the responsive idea has a lot and I'm not really as versed on How that might interact with sensing technology other than some of the stuff we get in HTML5 right now Um for like all these data points. Is there anyone that's really processing the this vast amount of information? like Like on a broad scale like say the entire Verizon network or something like that Well Verizon is probably doing it You know one of the one of the good ones that I should have mentioned, but I didn't Twitter is archiving all of its tweets in the National Archive and so That right there is a vast amount of people's public opinion And I mean it's probably a lot of like snooki references and cat pictures But at the same time like there's probably valuable things about election data and about You know Arab Spring how a lot of that happened on Twitter a lot of the organization of that And so that data is out there and the US government is using it through a lot of open data projects And then moreover Twitter is using it to to continue to Have a basis of understanding public opinion. You also see a lot of things in like the ratings folks the the people that do like the Nielsen ratings and all that sort of stuff where You know you'll look at at policy perceptions So a lot of perception sensing across these big amounts of data but a lot of it is really holed up right now in an individual company and A lot of it is like, you know the guys that own the apps, right? So Zenga probably has a tremendous amount of data about everybody plays words with friends Or Scrabble or what's the scramble that scrambles the new one or and then You also have places like Google where Google has a Tremendous amount of data housed within it and it allows us to window into some of it But not all of it because they see that data as as their leverage and what they're selling All right, cool Great presentation. Thank you. Thanks for Drupal today What is the best tools to start aggregating and capturing this data on our sites right now? so Again, I mentioned HTML5 and the ability to capture especially like geo located data a lot of it has to do with integrations So using things like services and feeds and pulling in data from other areas and then like I said, I mean Drupal can scale pretty well, but having billions of data points is is pretty tough to just all fit in anything more than a table And so I think that that where Drupal is going to really shine is if you're able to have some sort of of mobile data Aggregation service and then pulling that aggregated data into visualizations That's where it becomes really interesting for Drupal right because now all of a sudden I can see traffic patterns From the cartel system overlaid on a local map And I know that that's kind of been done, but There are a lot of interesting things especially in in the the disaster recovery and relief space Restore the golf is a great example of that where they're using a lot of open-layer stuff to to visualize The impact of the whole deep water horizon thing So that's where Drupal really shines right now It's not necessarily in the the collection of that data, but really in the aggregation visualization of it. Okay. Thank you I guess this is sort of along the same lines of like implementation like with Drupal's back end being By default my sequel. Do you think they'll need to switch that out for another database system? To take full advantage or are you just using this as purely visualization? Well, you can do that. So, I mean we have the ability to have a different database in the back end of Drupal And so, you know, you might store your visual visualizations in Mongo because you don't want to build the visualization every time You might want to have I don't know. So here's another dirty word oracle As the the main store for all of these data points and then interface with that I mean all of those things are capable in Drupal Which is is great and you know again that speaks to the integration side of things too, right? I mean if we ever really need to have that massive data capture somewhere It's very easy to integrate Drupal into that data system and and use the the tools available to us to collect that data And then visualize it yeah, there's there so the question was about the the Nokia funded research project in in Switzerland and a gentleman was asking if There's any known effects to their behavior based upon the research that's being being performed with that population Yes, absolutely. There is it's one of the things that they tout as an interesting result of the project that People's ability to identify others that are in this same program has actually led to more interconnectedness between members Then would have existed normally And it goes on to explain a lot about that and in a lot of very in-depth research e organizational behavior e sort of ways but the interesting part is the ability for people to connect better between themselves and To understand more about common and shared interests The research is there on the site. You can actually read the paper. It's really good really fascinating anybody else That's actually that's a fascinating question actually so the question is you know, what about the idea of of Using Drupal to facilitate an understanding of the data that we're collecting and Trying to build tools that more than just visualization collect perception of the data itself And I think that that is a really interesting space for Drupal Because we have great community features because we have collaborative environment because we are an open-source project And we're built by a lot of different people understanding perceptions and attitudes like that I Think that that would be a really interesting way to explore how we could do this in Drupal and how we could really make something interesting So you talked a lot about the sensors and the data that you collect from cell phones But I just wanted to mention something that I had actually been involved in a little bit Which is other kinds of sensor mobile sensor Collection through devices that you actually want to carry specifically for that purpose. So the Fitbit is kind of like that but More on the open-source Side of the ecology. There's a website called Pactube P a c h t u b e And it's designed for the collection of sensor data from any kind of device that you can attach to the web Basically, so a lot of people are using Small hardware devices like arduinos and things like that and sensors in their gardens on their rooftops And and that they carry around with them or put in cars And building sort of virtual networks of people collecting similar kinds of information and then displaying it through Pactube Which is also an open-source project. So something else to look into so with Pactube wasn't that the the one where the grant proposal went Out to do mobile weather stations And it was it was with the US government where we were talking about Aggregate collection of mobile weather information based on individuals That was the genesis of the project and it's since expanded to basically collecting any kind of sensor information about anything And you can sort of generate your own Pactube profile and then say I'm gonna be collecting data about Soil moisture and temperature in the Pacific Northwest or something. Yeah, yeah so I went to Washington State University where We care a lot about farmland because that's all that's really there and so we had a lot of this this Pactube stuff with Soil moisture and rainfall Because the water table and Pullman is really really high and we have a lot of our t-jun wells and stuff like that And people wanted to be able to forecast Like crop planning and stuff based on soil moisture and it was all this whole mobile sensor idea I mean not related to a person but you'd weatherize one of those things and stick it out in a field one of the One of the things that I like most about that Model of doing it is that the data that they collect is open So if you wanted to build a Drupal site to do analysis and aggregation of a specific sort of topic You could go out and find other people who were storing Data points on Pactube and bring that data into a Drupal site for analysis or Or representation somehow. So yeah, thanks for bringing that up. That's a great point anyone else Thanks a lot for listening to me guys appreciate it


Hey math learners, this is Mr. Marks, your friendly neighborhood math teacher. Remember, it's not just about getting the right answers, it's about learning and growing along the way. Now, today's problem is a great example of thinking abstractly or outside of the box. But first, let's talk about the elephant in the room. This is an often dreaded word problem. But word problems don't have to be so overwhelming. What do you say we tackle this problem together one step at a time? First, let's read the problem's instructions. Doubling a rectangle sides. The length and width of a rectangle are doubled. How do the perimeter and area of the new rectangle compare with the perimeter and area of the original rectangle? Illustrate your answer. So the term illustrate tells us we're going to be doing a little bit of drawing. But exactly what are we being asked to draw and why? Let's first start breaking down this problem by drawing a plain old rectangle. And let's just give it a plain old length of two and a width of one. Now our instructions say the length and width of a rectangle are doubled. How can we illustrate or draw that? Let's draw a second rectangle with length and width doubled. So this second rectangle will have length double two or four. And the width will be double of one or two. Okay, we have a set of example rectangles here. And now we were tasked with comparing perimeters and areas. Let's take a note of them. Remember, perimeter is the length around the shape. And area is the square units in the interior. Our first rectangle has a perimeter of two plus one plus two. Plus one or six units. And an area of length two multiplied by width one or two square units. Our second rectangle has a perimeter of four plus two plus four plus two or 12 units. And an area of length four multiplied by width two or eight square units. All right, take a moment and step back. Do you see the relationship between the perimeters? What about the areas? Let's start with the perimeters. It appears the new rectangles perimeter is double that of the original rectangles perimeter. Let's write down this conjecture. And what about the area? Well, it appears that the new rectangles area is quadruple or four times that of the original rectangles area. Let's make note of that and complete our conjecture. Wow, it looks like we've done it. We've illustrated our thinking with proper labeling and we've answered the question all while showing our work all along the way. Now I'd like to leave you with this final challenge. How do you think we did here? How could we prove ourselves differently? Are there other ways to write our solutions? Let me know down in the comments. Props to you for taking some time out of your day to do some math with me. I hope you followed along and if you made mistakes, that's great. Remember that every mistake is a step towards learning something new. This is Mr. Mark signing off. I'll see you next time with another math problem. What did you think? Did you approach this problem differently? Let me know in the comments. And if you enjoyed this problem, show your support by liking and sharing this video. And don't forget to follow my page to stay up to date on more math related content. Until next time.