yma ynghylch, ac yn ymddangos am yr AOC, am y cwm iawn. Felly, rydych chi'n gweithio'n cyfrannu cyfrannu wedi'i gweithio'r cwm iawn. Rydych chi'n gweithio'r cwm iawn, yn ei gydag, yn ymddangos i'r unig i April. Felly, mae'n gweld y ffyrdd sy'n amser i amser i Anzin, am Peter ac Naveen, mae'n dweud i'n ddigon i'r gweithio i fynd i'n gweithio. Ond yn ymgyrch, sydd ydych yn gweithio'r gwaith, ond mae'r grwp ffasilydd ffordd yw'r gweithio. Yn y ffordd, mae'n rhan o'r link yn y chat, yn y moment, ifrwyd, mae'n gweithio'r gweithio'r gweithio. Felly, fydd ddim yn fwy o unig gael, dwi'n fwy o enw, ac mae'n gweinio i'r hunain. A mae'n gweinio o'r informeith anodd o'r hyn sy'n gweinio'r hyn arno gyda'r hynny. A mae'n gweinio i'r hyn ac mae'n gweinio i'ch gweinio i'r hynny o'r pwysigau i'r blog ac oedd gan hyn o'r pwysigau. in there. So this is the first of a small series. The only bit of advance notice I'll give you is one that is about to come about, which is Dr Helen Crompton. Now she's someone I've known for about 10 years. So I've managed to persuade her to do a chat GTP AI roll up or a summary of all of our webinar series on this, and she'll be doing that on April the 8th. So it's a bit of a, and we got it for free. So that was the important thing. And obviously I've just done the advance notice now so that if you're aware of it, you know you can look her up and so on. But that's a but we will have others in the next couple of months. So watch out for the notices. So I will end that bit and I shall now go in and move into the main presentation for today. And I will pass in to Yasmin. Yasmin. Okay. Over to you. Yep, yes, thank you. Thank you very much introduction. Okay, so thank you very much for the special interesting group provided this opportunity for us. So and we are very delighted to have this opportunity to share our some experience and with all of you. Just mentioned, this is a collaborative work with our colleagues Peter Nico and Navid Kehan. So what I'm going to talk about here, initially I want to cover three major aspects. One is the talk about the student engagement. So what we we can use to try to capture understand the student engagement. And the second part related to challenges from the chat GPT from teaching on the learning perspectives. And the third part is a share some experience regarding how to incorporate the machine learning into teaching activities. But unfortunately, so on the way, perhaps don't have much of time to talk about the third part and what I can do. So and I already send the link to the chat box. So and if you are interested and you can click that link to see something shortly. Okay. So we are from Ulster University. University, Ulster University basically is multiple campus university across different regions. And we have Bairfast campus. This one is based on the Bairfast. We have Jordan's Town in Jordan's Town and the McGee in McGee Town and a city, Dairis City. And also we have the headquarter of the university in Corrine campus. So university has a strategy and called the people people place and the partnership basically try to unlock all of the potentials from the three major aspect, build the sustainable future for all of the society's things. So this is our the university's strategy. So everything the teaching and learning research is this strategy basically provides the role model for us conducted teaching, learning and the research. It is a big umbrella. So where we are, so all of us based on Bairfast campus just mentioned now you can click the link to see where the Bairfast campus it is and what it looks like. I give you a few seconds. Okay. So can you click that link and to see it. Okay. So this is a new campus and we just moved in so from the last year, from last year so and from this the digital and the view and you can look at the different and the part of the campus from Main Street even to the inside of the building. Okay. So perhaps if you are interested and you can view it later. Okay. So let me to move on the next part. So the first I like to see a little bit about the impact emerging technologies and how this emerging technology affect and the learning behavior. So all the past years and high education actually helped be impact by various challenges. Clearly is a pandemic. So a pandemic disruption and also include some government policy change. I think the important part here is some emerging technology actually the key driver to lead the we call the digital transformation. So this kind of transmission clearly to see for example during pandemic period and we put all of the things into the online. So and from the traditional class teaching into the online teaching. So since then there's a large body of research work talk about the digital pedagogies and talk about the digital curriculum design did even talk about the digital universities. So and finally Payson in 2020 first published one article. So and give some ideas regarding what looks like the digital pedagogies. So and if you are interested and you can look at the article. So and the recent year and recently and that we have attention. This is the test of the fire and voice alarm system. There is no need to take any action. I think we will just wait a minute. Well I think there's a fire alarm gone off where Yaston's presenting. So if we just hold on a few minutes it'll come back again. Yeah there was no need to take any action apparently. Yes. So maybe Peter or Navi maybe you want to add something here in the gap. They're probably on the campus as well. Oh of course yes. That doesn't last too long. Oh yes you should know yes being Ulster that's fire. Sorry about the interruption. So this is our routine test. OK sorry. Attention please attention please. The test of the fire and voice alarm system has now been completed. Attention please attention please. The test of the fire and voice alarm system has now been completed. Would we have the slides after the presentation. Yes. OK. Thank you. So sorry about this. Giving the number of challenges and basically we lead to some kind of hybrid and the teaching and the learning mode at the moment but not the hybrid basically and come back to the physical campus teaching. So and however during this period of time so teaching a number of stuff and the student already got used this kind of either online and hybrid teaching environment. So and that leads to another question regarding what is the student engagement. So how we define the student engagement. So now let me to give you one first case study to talk about our experience regarding the how to analyze the student engagement. So in 19 in 2013 we organized one workshop mainly dedicated to the understanding relationship between student attendance with attendance. So in that workshop we invited one kind of the speaker on the leaves the burn from Durham University of Durham. She did one continuous study and she is the author of the paper on attendance, attainment and the final year study. So this published 2006. When we invited her she that's already six or five years past and she continually working on this topic and the ring gave us a keynote. So from his keynote he gave some bully point and from his her talk she still think there's a very strong correlation between attendance attainment and also student attendance gradually reduced. So for example from the semester one and until the semester two. So this gradually and gradually reduced and also so on the morning time per hapus is the worst time for student attendance and also if we upload the material on the beforehand per hapus that will affect student attendance as well. So from her study and she found so generally on student attendance for attendance increased improved and in past five years. So that's the her and the conclusion on from his keynote talk. So we did something similar study and then we reveal the literature and try to see what is the best way to understand the student engagement. So on the cool published one paper in 2007. So he divided the student engagement into different category two main categories and the first one called the behavior category and basically this kind of the attendance this kind of engagement we call the engagement can be and captured captured for example physical class attendance and how many times of students can access blackboard. So another category called the emotion category and but in that paper he did not give any solution any suggestion and how we are able to capture this kind engagement. So and based on our research and we tried to use some way to see okay so what is the possibility for us to incorporate our research into and this and the issue try to capture some kind of the student engagement. So we come up with a sentiment analysis. This is our resort area. So what is the sentiment analysis? Sentiment analysis sometimes also called subject analysis or opening mining. Basically this kind of technology can be used to analyze large amount of text document and identify the people openings or attitude to against a different subject or topics. So how we can use this one. So basically we can use this one try to help us try to help us to analyze some fun feedback document for example and the look at the whole student react to this kind of feedback document and then use some kind of student action to understand the emotion attitude of the student engagement. So this is the main approach and we used. So here's some bullet point under what we are interested basically we're interested to understand the learning experience and their attitude to particular program or module actually. The second part we try to understand because the feedback sometimes is very sensitive for students as well. So the reaction on the feedback is very important information for us. So how we are able to capture the student action from the feedback that is a very important part of Hoha's as well. So based on the some kind of analysis results and then we can see can we use some the auto come to group student into the different the group and then see some student perhaps the need to take a little bit of early invention in order to improve their the learning and learning auto come. So that's the basically the kind of the motivation and what how motivation for this piece of the work. So we have a fun system. This one was divided by our colleague Peter. Peter is here. So if you have any questions and so you can ask these some questions Peter. So fun system basically is able to convert the feedback into files and each file will be named into email student email address and then release all of the files into students with a unique and a hyperlink. So the system is able to record the two time stats. One when the feedback released to student and when student click the feedback. So this system has collected large number of module data and we just use the file years the feedback data and then started to do some pre-processing by end of the pre-processing and we have 3,300 feedback data for general study. In order to narrow down the scope of the study and we clearly to see 3 modules and 3 modules and across the different years and from year one to year four and try to understand the student and the reactions to the feedback. So this is one example and just mentioned. So here the system gives us the when the feedback file has been released and this time stamp clearly indicates when student clicked on the feedback. So we just based on the two time stamps try to understand so-called the emotion and the engagement of student. So what do we did? So we collect the data and then we project the data into two dimensional representation and then we divide this two dimensional into four areas. So first areas indicate the student's past but with less attendance. So under this X axis represent so how much class the student physically attended. So Y axis represents the mark the student achieved. So from here you can see under this part basically the past with less attendance and this one is the past with a good attendance. This part indicates the past and with less attendance and this part and indicate the field with less attendance. Okay so and the four different areas can be used a different purpose of the phone on our study for our study in order to improve our teaching and into the learning as well. So we do the further analysis use the sentiment analysis. We look at the how many days and the student collect their feedback and then we divide this the time into on the four categories and the first category and within the five days for example. The second within the file to 15 days and the 20 days will be the mutual and after 20 days will be the potential risk and the risk. So and we use this one to identify basically five group of students and then the five group of students give us some kind of ideas and how what kind of actions should we take. So this is the first part and our research results. So um so how we use this one to support our students. So for example once we have the clear the the group of student and the人 we can take different actions and in particular for example if we have two assignments and student reactions to further assessment not good within the potential risk area and categories and then we can take some actions take some actions okay. So and another part because this system is ongoing system so and widely used within the university and and Peter also have some ideas regarding and also based on the some feedbacks regarding what is the best time for us to release for example feedback to students. How this time will affect the student reaction to the feedback. I think that also is the important part for our next step research. So this is the first case study. If you have any questions and so and we can help discussion by and of my presentation and meanwhile so you can and put some questions into the chat box as well. So the second part I am going to talk about is the challenge of the chat GTP in assignment. So this one basically based on our MIC course I'm the module co-ordinator of the deep learning and it's quite a lot of research on the generative AI. The chat GTP is one of the topic of our research. So I think the chat GTP already not new because it's released at the end of the last year and already raised a lot of issues and the interest to the different communities. Basically we can use the chat GTP to generate different things. For example we can use the chat GTP to generate the code generate the text and the images and the 3D models and even include the music as well. So and the chat GTP basically based on the data the data come from the different sources and come mainly come from internet and come from libraries as well and they based on the data to train the model and use that model to do different generation of the image code the music as well. So because it can be used to generate different things naturally and we can use from teaching perspectives we can use and either for example help us to generate some assignment, generate the feedback to student the coursework and the student also can use the chat GTP to generate answers to their coursework for example. So here I'll give you a little bit of the background of one one module and a couple of deeper learning. This is a standard 20 credit module and teaching hours and basically is this 24 lectures and 30 practical sessions and six hours for tutorials. Independent study is 140 so because there's a standard model module so there's a 200 hours module. So we look at the assessment because this is mainly created during pandanum period this 100% of coursework. So the coursework divide the two part the first part is the writing assignment take 50% and the second part also is the writing assignment. So and I give you an example about the first assignment so the second assignment okay so this is the second assignment so and clearly to see what is the objective of this one and there's some specific task and the required student to complete. In order to understand how students use this one or use the GTP to complete this this kind of assignment and I invited two students from this AI course and one student and he wrote the beginning of this year Mohammed and the header ceiling. So and the second one and Martin Ousserman so he is from Egypt he involved the module study from last year last year okay two students and have experience about the coursework and also have experience of using chart GPT. So the both students confirm they spend less than two hours to complete this coursework and they also indicate if students don't have any experience about the use the chart GPT so under the purpose where you use a few hours to complete. So based on the result and the reason we see so under the result and perhaps the kind gets the 55 even 60% of the mark. So look at the original specification of this as a coursework so how much time we expect the student to spend on this one. So for example we expect students spend around the 40 hours but now you can see students only use the two hours to complete this one okay. So that's really the significant challenges for us so when we divide our coursework on the assignment for students so under what kind of style or the what kind of way should we use what kind of way should we use. So here's the answers from students and you can see students directly and put the requirement of the assignment into the chart GPT and use this as the copter prompt. And after that student can get the answer like this this is the answer from the chart GPT okay and the student also can require the code as well so and based on what I will get so and students can generate the prompt to generate the code as well. So a student did not tell me this code is working directly work or not but a student that tell me so they did not spend much time to make this one work okay. So what we learned so chart GPD under provide the good opportunities not only from the teaching perspective and also from the learning perspective as well so and the student can use the chart GPD to and use their own way to read the questions and then to get answers and then they can read Italy and then read the questions to chart GPD and then get the answers. It doesn't matter what time they want to use it as long as they have questions and they can use the chart GPD. So also for example for international students if they have language barriers they can use the chart GPD to have them to do different things in particular help them to complete and their assignment and the help to prepare the examination and the coursework that's what I already demonstrated so that's the opportunities but what is the the challenge is the complex cities and the chart GPD so and we can see the main objective of the coursework or assignment will be check a exam student the critical thinking and the problem solving skills. However if a student use the chart GPD and to create answers use the chart GPD to create some ideas so that means we are not able to achieve under what we pre-defined okay so and also if a student directly use the chart GPD as an answer so that involves the plagiarism also involves the misconduct as well related to the academic integrity. So student can repeatedly use this one to rely on use rely on chart GPD so that promotes the student's lazy. More things so chart GPD also creates some kind of disruptions to our teaching as well to our teaching as well so we initially design the our coursework with specifying the objectives however so when student use this kind of AI tools so and they can get different answers so and sometimes the general answer even challenging to us as well challenging to us as well so and something related to I think issues and the relate relate on the rely on tech technologies to do different things basically and that's the the challenges and from the generative AI tools when students use their use this kind tool to help them and do learning to learning. So we also noticed some new advice from the quality assurance agency in the UK and so under this is a basically and is kind of encouragement to to encourage and from teaching perspective from learning perspective to use this kind of new technologies so and for example the system statement clearly to see chart GPD is very useful and it provides a lot of opportunities for from the employee employability perspectives and also and pose a lot of challenges from the teaching perspective as well so they also create the one seven principles regarding the how to achieve the academic integrity integrity so and really regarding the this kind of things actually is the whole community rather than individuals rather than individuals so and I provide the link here and I provide the link here so and that you can have a look you can have a look so basically that is the some kinds of the guidelines from the QAA QAA so also from the different sources for example different units will say how different the based on QAA creates their own guidelines as well so um but so far on the way down to how clear the the solutions and the sort of way encourage the has to use the chat use encourage a student to use chance to complete their coursework or completed their teaching tasks or sort of way encourage and the complete stop on the student using this kind of tools so and now the come to the the summary of my presentation basically as a lecture so we are facing new set of challenges which I already mentioned at the beginning so and what extent so do we do bring the research into the teaching and this is the the big topic I think and today I don't have time to elaborate on our experience I hope and that we have a full opportunity so give you some detail so from the curatorial design perspectives and from the employability perspectives and why we think the incorporate the research into teaching practice is the more important so and we need to learn adopt to adopt a new emerging technologies for example particular from the generative AI chat gtp which I already demonstrated and also giving current the technology enhanced learning environment so and how we understand the student engagement that will be the important part for us as well so and there are a number of ways to try to capture student engage and with their program study but I think from our perspective we think is really challenging to understand the emotion the engagement for student the learning behavior learning behavior so but the important part where we are here is and we need to put a student in trun central and this is a guideline there's a strategy of the university university so and we have to understand the student's digital learning experience in order to put that in the central okay so this is an enjoyable my presentation so thank you very much for your pensions follow you hello hi thank you for the presentation very nice theme to start with because that's the way we seem to be going for the rest for the rest of these sessions so there's been a few questions come up in the chat here and there Elizabeth you were asking a few questions would you like to join in oh hello hello sorry I'm just trying to squirt the technology yeah um do you know you were talking about um that in your first case study the sentiment analysis which I actually had never come across but then I just googled it quickly loads of stuff came up so that just shows my ignorance but I really like that um focus on the emotional attitude of students so so that's very interesting um could you say a little bit more about that yeah um I think the sentiment analysis as I mentioned the sentiment analysis a sentiment analysis uh is a big restorative area in artificial intelligence so um basically underpin technology will be the motion learning so um so we can use motion learning to analyze a large amount of the text data to identify student attitude so um for our case study and actually we don't have a large amount of a text and what do we have we only have time stamps so we have um what time we upload feedback to students and what time as a system collected the student download their feedback okay so this is clearly this is a time stamp so and the some student for example we assume if some student engage their module study or program study and they should react the feedback very quickly so if a student not engage the module study or program study so a perhaps reaction to the feedback quite a slow quite a slow so that's the main important the the idea about the this approach so we try to understand their reaction and then we group them into five categories so for example for this part so basically student reaction good so and between under this period that period of time so we think no any potential risk for student studies however if student not collect their feedback and after 20 days so and then we if this is for first the piece of the course work and then we need to pay more attention on the second piece of work so we need to see why student reactions to slow and so this kind of slow give us the clear ideas and we need to put more attention to this cohort of students so I think that is the the important the outcome of our research 11 so it's okay thank you yeah that's kind of reading quite yeah this emotional analysis in that response which is an obvious one actually but yeah that's an interesting one to pick thank you so there's a few other little there's been quite a bit of chat going on in the background would anyone else like to add any questions at this stage yeah can I yeah so don't hear just an observation made on the previous slide you are showing here on the previous one where you showed the attendance rate and the marks yes looking at the scatterplot I would say there is no there's no correlation here between the attendance and the mark achieve would that be fair yeah I think this is our research result so um compare this research result with the least uh least burn from the term university so and it's quite different so and the way also this our continue study as well so um you can see actually this is a non-linear correlation so non-linear relationship so and from this one is a really difficult for us this clear correlation between students attendance and their achievement their achievement you're right so this gives us the idea about for example how we understand the student engagement can we see this group of students because you can see they did not attend the class very well but they still survived module study okay so what way they used what way they used to engage with module study I think this is a good question so that's why we come up with another part so um can we for example to clearly identify so-called emotion engagement under that group of students actually and if we use the emotion and the the engagement as the indicator indicator so and can we see that group of students actually emotionally engage module study very well so actually this our further research is the question question so because there are so many connections from different perspectives we haven't got a chance to look at how this is a group of students and uh the their emotion emotion engagement okay thank you um with someone else raise the hand I see oh yeah thank you um good afternoon thank you for sharing these interesting case studies and my question about the slide in the second case study about the prompt that you use in chat GPT yes do you provide the student with a certain standardized prompt or you give them the flexibility to use their own words so um because I try to understand the whole this is basically um I think in past half a year six months a lot of people to talk about the how to stop student use the chat GPT use whatever way uh stop student directly use the chat GPT to provide the answers to assignment so um the the case study mainly try to understand the student experience so I invited two students okay so um this student so uh you can see the the student two students actually study the deep learning model module module okay they have an experience on this course work on this course work so um but they also familiar with familiar with the chat GPT so I did not provide anything to student I just try to understand the student experience so what way student use uh to uh to generate answers to the assignment you can see from the I did not see I did not tell student how to do that and then you see student directly take the whatever I have in the course work as a prompt as a prompt I think that is a good indication so and if we clearly for example if when when we design course work so we need to take this factor into consideration try to award the student directly use under whatever we specified in course work as a prompt so I think that that will help is the one thing we need to learn we need to learn so um you can see student directly under use this one use what I specified in the in the course work under them to generate the answers and the two students use a similar way to generate the answers okay okay thank you okay anybody else got any burning questions at the moment and obviously all this will not be the last word we have on chat gtp I think it's just the stop way to go forward really um so I'm just going to remind people that we do have obviously some um the committee for alts special interest group is um open for invitations if you would like to join to become part of the committee there's a link in the chat and there's also it was a notice that went out by email this week so please feel free to nominate yourself I don't know whether Richard would like to say something at this point if he's still with me I'm still here um yeah I think it's it's just um Elizabeth I myself um I've come to the end of our three year term and we're looking for other um people to come along and and and take on the button and lead this um much needed interest group forward um I think regarded active run but specifically I think around the technology challenges we're facing with with artificial intelligence or generative AI et cetera um it's a fantastic time to come in and help shape the future direction of this group so really looking for anyone who's interested in becoming involved is chair co-chair secretary or even just member groups and FE um HE skill sectors this is a much frame much across multiple sectors group um so we're looking for anybody to come along and bring their experience and help share and shape where we're going to go forward with this but yeah so please do put your name into the hat you've got until January the fourth I think to do that the the I'll just post the link again on the chat for you so you can all see it yeah I okay okay yeah would you just do it Elizabeth yeah yeah it's all right more the more the merrier really um but yeah so um yeah please get involved if if you have the time or if anybody has ideas for webinars as well um you on our um forum there's there's other things you can submit and to get more involved with the group so thank you and thank you to Yassan and colleagues for today's presentation as well okay thank you thank you very much yeah I think that's all I think if no one's got any other questions then we can finish on time which is always good when people are in their lunch hour um and I say thank you very much for doing the presentation it's a very good way to start this new session for the for us and a reminder to if you wish to become part and also uh not forgetting to look out for the next notice for the next webinar so uh if if that's everything then we'll say goodbye


FC Barcelona presiden Johan Laporta said on friday leo mercy wanted to stay in Barcelona Laporta said the club and mercy had wanted to sign a new contract but salaries already represented 110 percent of the club's earnings and i thought it is spending much more than expected so much a move would be would have been so much that that move would have been financially risky Laporta said the coronavirus really impacted 2020 2021 season and the financial losses of the season would be double what had been expected hello hope you enjoyed the news please do subscribe to our youtube channel and don't forget to hit the notification button so you get notified about fresh news updates


So, as the process of growth, globalization, and poverty reduction rolled on, people in rich countries, or at least the educated elite in rich countries who knew about it, looked at it with delight. In the educated elite, I include international organizations such as the World Bank and other development agencies and foundations, as well as readers of the mainstream press, and most governments of rich countries, and me. But not everybody in the rich world was so satisfied. After 1970, growth in the rich countries slowed markedly. Many people struggled to be better off than their parents. For them, the expectation of progress that each generation had been and would continue to be better off than the previous generation was failing them. One reason is that low-skilled jobs in flourishing cities have become less available than once was the case, and the cities have become more expensive. The jobs that were lost were less skilled jobs, but the better jobs that were available were high-skilled jobs. People without a four-year college degree, without a BA, were increasingly in trouble. I will talk in a minute about the many reasons for the increasing division for people with and without a BA, but I want first to show you some evidence of just how badly hurt they have been. Capitalism in America today has stopped working for the two-thirds of the population who do not have a four-year college degree. Here are a few of the most important facts. Participation in the labor force for those without a BA has fallen, as have their real wages. Both participation in wages rise in good years and fall in bad years, but rises have never taken them back to the previous high. Even when working-class wages were doing well in the months before the pandemic, they were below what they had been at any year in the 1980s. Some have argued that these Americans are lazy and no longer want to work, but if that were two wages of low-skilled workers would be rising, not falling, it's the jobs that are vanishing, and with them the wages that allowed their parents to have a good middle-class life. And it's not just material well-being, marriage rates have fallen dramatically. Nonmarital childbearing has risen. Many children live apart from their fathers. Communities are failing, too, with church-going declining and millions of working-class men detached from any form of institutional support. Worst of all is what has happened to their health. More than 100,000 Americans are dying of drug overdoses every year. These are counted as accidental deaths, but the drugs they were using did not get into them by accident, and so there's always an element of self-harm. Suicides themselves has also been rising rapidly as have deaths from alcohol abuse. These are the deaths that Ann Case, who's sitting here, and I call deaths of despair in our book, and almost all of the increase in deaths of despair since the late 1990s has been among people without a college degree.


Dear students, in this module, we are going to discuss that whether sex and gender are socially constructed or not. Gender and sex are socially constructed concepts. It means that they are created and reinforced through social interactions and different cultural practices. It is not the case that gender and sex both can be used synonymously. When we talk about gender and sex, we usually think that these are the same concepts. Since sex is a biological feature and gender is a socially constructed factor, there is a huge difference between the two. On the basis of sex, we cannot determine gender and sex cannot be determined on the basis of gender. This is the saying of modern feminists and gender studies' proponents. According to their opinion, sex and gender are two different entities. Traditionally speaking, we often use them synonymously. In our opinion, especially in the opinion of traditional societies, your gender is determined by sex itself. That is, your role in the role of the queen of labor is your sex. If you have a man in the society, then the society will expect a different behavior than you have a woman in the society. So, the differences between a woman and a man, or the distribution of work, the determining factor considered in traditional society is the biological factor. On the other hand, in modern societies, your sex cannot determine your gender. According to this concept, gender is socially constructed. That is why you cannot do any special work with any particular sex. For example, who will do the distribution of children? We cannot determine on this basis that since a woman has given birth to a child, she will be responsible for the distribution of children. Similarly, the household work cannot be determined on the basis of a woman. There are only biological features on the basis of this. Therefore, the household work will be given to a woman. The reason is that today we have a very dynamic gender roles under this ideology. Men are doing the work that traditionally women used to do and women are doing all the work that men used to do traditionally. So, even in Pakistan, if we look at it, traditional gender roles and expectations are deeply embedded in cultural and religious practices which can limit individual's expression and communication with the non-traditional gender roles. So, a kind of cultural clash that we see today, especially in urban societies, in which the role expectations are changing today, men are expected to do household work. If there is nothing else, they can distribute their hands and take part in it. And women are expected to get education in the job market. So, the study of the social construction of gender and sex requires a multidisciplinary approach by incorporating different perspectives from sociology, anthropology, psychology, and cultural studies. So, if we really want to understand this phenomenon that whether sex and gender are socially constructed, we will have to see whether it is a social aspect, psychological aspect, or economic aspect, which is actually determining different gender roles in your society. Food for thought is that if you look at the changes in the social construction of gender in the Pakistani society, especially in media advertisement, you will see that the traditional gender roles are shifting. And from this, we get a strong point of view that gender roles are socially constructed.


Good afternoon. Thanks for coming to joining me today. My name's Dave McAllister. I'm an open source technologist for a company called IngenX, which you may have heard of. And it's one of our major open source projects. IngenX open source is probably one of the most used load balancer, reverse proxy, web server, whatever you want to name for this. But I'm actually here today to talk about something near and dear to my heart. And it's about distributed tracing. But I'm going to start off by pointing out that tracing is a data problem. There's a lot of data that comes into tracing. And because of this, observability is now also a data problem. However, the more observable we make our systems, the faster we can figure out what the heck is actually going wrong with them. And observability has a lot of different characteristics to it. It's usually made up by three classes of data. Metrics, basically, do I have a problem? So this is your learning structure. Logs, why the problem is happening, or what the application or infrastructure thinks is going on. And the new one here, this thing called distributed tracing. Where is the problem occurring? And basically, it's changed the model of how we have approached monitoring. From looking for things that we knew could go wrong, to being able to figure out why things we didn't think could go wrong or didn't think up in the first place, otherwise known as the unknown knowns. Unknown unknowns can go back and figure out what's going on with those. And that gives us visibility into the state of the system itself. Let's us do predictive analysis, as well as reducing this mean time to clue. Mean time to clue means you may get a faster mean time to response and resolution. But basically, tracing helps you get to mean time to clue. And so we've been doing this for a while. Distributed tracing is not new. The original Dapper work came out in 2010. But why now? Well, all of a sudden, we've had this thing called microservices suddenly take off. We have lots and lots of architectures that are built on the concept that is made up of applications that are as small as necessary, not as small as possible, that are linked together in loosely coupled forms. And when we add that to this structure around cloud, things can become a little bit more challenging here. Nice thing about them is that we only change the pieces that we need to change. And we can change them any time we want to in each of these pieces here. Each of these pieces can be owned by a totally separate team. And sometimes, they don't even talk to each other. The largest structure I've ever seen for microservices has over 1,000 unique applications being built in that. If you look at the Amazon front page, if you're buying something, it's about 135 independent microservices running each page. But when we go to microservices, we get all these weird challenges that come into place. And this thing on the right is called the Knieven Framework. And what has happened is in mathematics, my background here is, you know that you never change two variables at one time, because you can never then figure out what's actually going to need to be changed for this. With the concept of cloud and microservices, we've changed two things at once. We have changed the complexity structure. Microservices have made things a lot more complicated, a lot more moving parts loosely coupled. At the same point in time, when we moved to cloud, we have added chaotic behavior in the fact that we have elastic and ephemeral behavior. So now, we don't know what's going on. We don't know where it's going on. And we have to figure out where the exact problem occurs at any given moment. And that leads us into this structure of distributed tracing. So what's distributed tracing good for? Well, what you're looking at here is called a red monitoring grid, rate or duration. And it's actually tracking the request as they go through our system. And in this case, I'm pulling them together. By the way, this is not real time. I'm pulling them together and aggregating them in 10-second clumps so I can see what the things are telling me. I'm also using a strip chart to show me the live verification of data as it's coming forward. And then I can see what my request times look like in each of these pieces. This adds a capability of metrics to our environment. So we now have more metrics from our distributed tracing than we had before. And the unique thing is that it's actually looking at each individual request as it goes through the system. It's not accumulating them. I'm accumulating it so that I can make some sense out of this. So when I look at this, this is what gives me my chance to build that MTTC structure for this. And with this, what has happened is that we now have a number of new structures for monitoring that have come into place. ROM, which is real user monitoring. What is the experience of a specific user as he transmitted the system? Synthetics take that real user monitoring, build a synthetic user so that we can actually test each time that's going to happen what's going to happen inside of those. NPM, network performance, APM, application performance, infrastructure. All these pieces break into the environment that then takes advantage of these things. And you can then see and break it down by workflow how the work is progressing through this. All of this is driven with some bits from distributed tracing. Now, tracing itself is defined by concepts. And we're going to start with a span. And a span represents a single unit of work. You get to define what the unit of work is. Your unit of work could be the entire microservice or your unit of work could be pieces inside of a microservice or your unit of work could be a complete application put together for this. The trace is all of the spans together that made up the request from start to finish. If a request does not end, it does not count. It is non-valid. And because of this, most application systems actually have timeouts for traces because you can't see the trace until it's actually concluded for this. With this, however, just getting these numbers is meaningless unless you again know what's going on, where it's going on and how things are propagated throughout the system. So let's take a look at a trace real quick here. This is known as a directed acyclic graph. It is one of the common ways that you will take a look at tracing. It has some interesting information. In this particular case, for instance, I can see that my trace came in through an API point that the distance inside of that API was 260 milliseconds. It then passed to two different places and the pass took a period of time which then breaks down into other pieces. So I now can see the path my request took through the system. I can see how long each piece took for each of the systems and should something goes wrong, I can start looking at where errors could occur. I can also see over here the far side what's going on in detail from each of these pieces. This gives me all of the information I need to know about where something's going wrong. So I can now actually track every single request as it goes through the environment. Each of those pieces tells me a particular information. This is one of the two principle ways that people look at traces. The other one is a waterfall plot. And a waterfall plot basically breaks down and does the exact same sense of information but it shows it to you by running across a list of how things have progressed down from parent to child and where they are. And then over here it tells you the amount of time that each one of these spent inside of those pieces. So this trace is actually made up of this span and this span which then is made up of each of these different spans going across. These two spans which are the principal child spans here are almost exactly the amount that the principal span traces. There's a little bit of slop inside the system because when we're doing this loosely couple of communication between nodes, the node metrics for time are not standardized or stable. The best standardized time in a network time protocol is roughly a hundred microseconds. And so it is very easy to get a little bit of slop that shows up inside of those. So again, I can see the span names. I can also start seeing where my breakdowns of performance, where the actual performance looks like for each of these functionalities. And so this makes up a very clean and simple way of looking at that. However, again, it doesn't tell you where things are going on, what the underlying infrastructure looks like or any of those pieces. And that is something we thought about. And so when we think about this, we now have what's called baggage or you can think of this in lots of other terms. But distributed tracing includes the ability to provide things that are carried along with a trace. And so you can carry things along the trace that says, okay, so here's my container that put on here. Here's the Kubernetes node that it was running on. Here's the type of span that it was looking inside of here. And these things are what are built into the system. These are semantic conventions. If you make an HTTP call, you will get a series of baggage that comes along with this. What type of HTTP call? What the response code was, did you get a 504 or 404 or 301? You'll get where it came from and where its target was as built into the distributed system for this. So each of these pieces, we can look at the trace, break it out and actually see the underlying infrastructure. If you have ever dealt with a noisy neighbor problem that's eating memory, you have to have this. You cannot find what's going on because the application is perfectly fine without finding out where the noisy neighbor occurs. So what do we need to do this tracing thing? You need a unique ID. And unique IDs need to be able to be able to be propagated through the system correctly for this. You need instrumentation. It's not simple to do this. We'd like to have a single telemetry so we can combine all this data together. We'd like to support all possible languages, all the developer frameworks. And because we're in a cloud, it's got to be distributed environment capable right away. Enter this thing called open telemetry. Open telemetry is the future of observability and is the defining metric for distributed tracing. Metrics, mostly logs coming. So each of these pieces come into play for those functionalities. But everything starts with a unique ID. Span IDs can repeat. Trace IDs may not repeat for that. And so inside of open telemetry, we provide those three things. We provide a trace provider, which is the start point. It actually makes the things happen. Trace creates the spans and passes propagates that thing. And span is the operation that actually traces the activity that's going along. If you look at something like a service mesh, you get this almost all for free. You get it from end point to end point. You don't get the inside pieces for that. If you are looking at doing the other functionality pieces, there are different classes. Currently, it supports almost all languages, but your mileage may vary. Some of them are much more mature than others. So if you're going to implement this, you got two basic options. You're looking at traffic inspection. I just want to know how things are communicating through the system. Service meshes, almost all service meshes have this built-in from day one. You just get to propagate and you get to understand what your traces look like. Code instrumentation, how you put things inside of the code. And code instrumentation can actually vary being auto-instrumented. Again, your mileage may vary, or manual instrumentation. And you can do both at the same point in time. And so when you focus on code, you're basically going to add this client library dependency for this. You're going to focus on the service-to-service communication first. You then can add the spans inside of your applications to get a closer look at the application functionality. And then you can add function-level calls and all those other pieces. But you can start simply and then build complexity that goes out.  Automatically, just add your file. Add the Java jar file. Automatically, you've got trace data coming in here. You've got a lot of trace data coming in. Most of it is probably meaningless to you. So you can go in and subtract out what you don't.  The two can be mixed together. And on our work, we tend to mix them together. We start automatics, subtract out what we want, and add in the new features that we want. So problems, performance issues are obvious. Happy users are obvious inside of here. These things combined together give us missing context that we're not available to see other ways for that. And then when you're doing this, if you're doing this, you want to be sure that you understand why you want to trace. Start with your service boundaries. Repeat as you need to. But be aware of information overload. We are talking a lot, a lot of data coming in at one time. And so distributed infrastructure gives you, or tracing gives you views into the application, the user, and the infrastructure. The win is when you get most applications, most third-party applications, they're now hotel ready out of the box. And so projects that are open source projects, if they're making anything a request, they need to be that as well. Great proxy for user happiness.  But it does not magically solve your problems. And so finally, I do want a closing thought, simply because I like this. Back in 1976, 79, Brian Kernigan, Unix Preventors, wrote, the most effective debugging tool is still careful thought, coupled with judiciously placed print statements. Distributive traces are our new print statement. They give us insights, but it's still up to us to figure out what's going on and why it matters. And with that, I'd like to thank you for letting me have 15 minutes on stage today. You can find me on LinkedIn. And if you get a chance, we'd love to know what your thoughts are on where the directions are going for observability, DevOps, and tracing in general. Thanks.


All right, I'm just gonna hook the detector up to this section of track here Clipping a test clip to each rail. This track is already fed and is Knowing to have no short What I'm going to do is simulate adding the feeder incorrectly to this track To see how the detector performs Okay, that's a great audible indicator of a short I can work under the table with this system and hook up feeders Without stopping to check for shorts. I'm going to know the instant I do something wrong Before I tighten down a screw before I make a solder connection. This can't help but save me time. I like it I hope you do too


In this presentation, we will take a look at a closing process for a partnership. We're going to enter the information in the left-hand side in the general journal and post that to a worksheet so that we can see the effect on the accounts in the worksheet. Our worksheet has a trial balance. The trial balance has the green accounts being the assets, the liability count in orange, the capital accounts in light blue, then the income statement accounts including the revenue and expenses in the dark blue. The net income is at $7,000. We have $7,000, which is revenue minus expenses of that $7,000. Debits are in positive numbers, credits negative numbers to result in debits minus credits of zero. So we're in balance because the debits equal the credits. Our goal here, like any goal of the closing process, no matter what type of business, is to close out the temporary accounts. So the temporary accounts will be similar for any type of business in terms of the income statement accounts of revenue and expenses. And then the things that will differ is when we get to the capital type of accounts, meaning a sole proprietor will only have one capital account, which makes it a lot easier. And a corporation is actually usually easier as well because it will be closed out to a retained earnings account. With a partnership, however, we're going to have the multiple types of accounts that we're going to have to close to for the multiple capital accounts here. For the purposes of this problem, we're going to assume an even allocation, kind of the most basic type of closing process in terms of the profit sharing. Later, we'll take a look at some more complex types of profit sharing for the net income allocation. Here, we just want to get used to the same kind of closing process and note the differences and what textbooks might put together when asking problems related to the closing process and income allocation. So we're going to close out first revenue, then expenses. Then we're going to close out the income summary to the capital accounts. And finally, we'll close out the draws. That's our four step. That's a typical four step process, the same that would be used for any type of business. So we're going to start with revenue. Revenue has a credit balance. We're going to do the opposite thing to it, a debit. So I'm going to right click and copy the revenue. We'll put that up top in K2, right click and paste 123. The amount then will be 10,000. Then we're going to credit something for 10,000. So we're going to put a negative of that 10,000. We could just type in negative 10,000 as well, but I like to use that formula. And we're going to put that into our new account, this clearing account. The account that will be zero before the closing process and after the closing process, income summary. So we're going to right click on the income summary and copy. We will put that in K3, right click and paste 123. So that's going to be our first of four journal entries. So here's the revenue account. We're going to put that in the revenue account on the trial balance in cell Q12. So within Q12, we will say equals, point to that 10,000, bringing the 10,000 credit down by 10,000 to zero. Then we have the income summary account, which will go to the income summary here on the trial balance in cell Q11, where we will say equals, point to that 10,000 credit, bringing the zero up in the credit direction to 10,000. So we in essence have just moved the revenue credit to the income summary from the revenue account. Now we're going to close out the expenses. We only have one here just in our example, just to give a quick example of this. And so we'll close out the expenses here as if they're all grouped into one expense account. And we'll close that out to the income summary. So our goal here is to make all temporary accounts zero. So I'm going to right click and copy the expense. I'm going to put that on the bottom. So I'm going to skip a line, skip another line, and put it in cell K6. Right click and paste 123. And then the amount is going to be a credit in M6 of negative 3,000. Then we'll debit something 3,000 L5. I'm going to say negative of this number and enter. And the account we're going to put that into is the income summary account. So we're going to go down to the income summary, right click and copy. We'll put that in K5, right click and paste 123. Then we'll post this out. So here's the income summary account. We're going to post that to the trial balance income summary account here. There's something in it. So we're going to double click on it, go to the end of it and say plus. And then point to that 3,000, bringing the balance from 10,000 down by 3,000 to 7,000. Then we're going to go to the expenses. So here's the expense we're going to post. Here it is on the trial balance. We want to be in the middle column in Q13, where we will say equals. Point to that 3,000, bringing the 3,000 balance down by 3,000 to zero. So the effect here is that we closed out net income to the income summary. It's important to note that process because many book problems will actually just start at the income summary when talking about the allocation of net income to the partners. They often just say, well, the income summary is here before we close it out to the capital accounts and then we need to close it out to the capital accounts. For our purposes it's nice to actually close out the process so we know what comprises net income and we know how that allocation is going to work. It's going to first go to the income summary or we can think of it as going through the income summary, then we can use that income summary account to close out to the partners. In this case, we're just going to use an even allocation, just a one-third even split allocation between the three partners for the example of the closing process. If we had any more complex type of allocation method, then we would need to go through a table and go through whatever the allocation will be. But whatever it comes out to between C, K and M, we will have to allocate this 7,000 net income. So the net income is going to go down and we're going to allocate to these three partners. So I'm going to right-click and copy net income. We're going to put that in K8, right-click and paste 1, 2, 3. Then the amount in 7 in L8 will be 7,000. Then we're going to credit the capital accounts, which will be C's capital. I'm going to hold down control, K's capital, hold down control, and M's capital. Let go of control. Now I've just highlighted these non-adjacent sales. I'm going to do this so we can copy them all at the same time. You don't have to do it this way. We could just copy them one at a time or just type in the accounts as well. But I'm just going to right-click and copy these. We'll put that in K9, right-click and paste 1, 2, 3. Then we're just going to get the amounts. And the amounts are just, I'm going to assume that our profit-sharing agreement is a simple profit-sharing agreement of one-third allocation to each partner. So we're just going to take one-third of that 7,000 to each one. So to do that, I want to flip the sign. So instead of setting equals, I'm going to say negative that number divided by 3. Enter. Same thing here. We're going to say negative of the 7,000 divided by 3. And one more time, same result, negative of that number divided by 3. So now we've got these three adding up to 7,000 or the debit of 7,000 minus the credits adding up to zero. Now we're going to post this income summary closing out to the capital accounts. So here's the income summary. Here's the income summary here. We want to be in the middle in Q11. Double-click on that, go to the end of it, say plus, point to that 7,000, bringing the balance down to zero. Then we're going to post C's capital. So here's C's capital on our journal entry. Here it is on the trial balance. We want to be in cell Q6 where we will say equals, point to the 2,333, and, oops, wait a sec, I'm in the wrong zone. We want to be in the capital in Q5. So I deleted that, delete that, and go back up to Q5 and we want to say equals, point to the 2,333, bringing the balance up from 40,000 by 2333, 2,333 to 42,333. So of course the capital accounts are increasing and that's because the partnership owes the owners more money. That's why we're in business. We earned revenue. We're increasing the capital account representing each partner's share of that revenue earned and in theory, amount that can be withdrawn from the partnership to the partner. So we're going to do the same for K. Here's K's capital. It's going to go here in the trial balance. We're going to be in cell Q7 where we will say equals, point to that 2,333, bringing the 50,000 balance up by 23332, 52,333. Then we have M's capital which will go here to M's capital. We are in Q9 where we will say equals, point to that 2,333 which will bring the 30,500 balance up by 23332, 32,833. Next journal entry, last step of the four step process is to close out the draws. So the draws we have, we see that each owner has a capital account and a draws account, capital account draws account. The capital account is a credit, the draws account a debit. If we sum those two up, they add up to a net of something less than the total capital or 40,333. That's what is actually an owed to the owner in a way. The net value of the partnership which is allocated or owed to this particular owner. Now what we want to do is just make this one account. We want to close out this temporary account of draws to the capital account and we want to do it for each owner. We could do that with three separate journal entries which means this is a debit. We would credit it and debit the capital account, decreasing capital, bringing this to zero. Typically it's done with one journal entry because it's part of our four step process. So we will close out all draws accounts to all capital accounts in one process. So to do that, we're going to do the same kind of thing here and we're going to debit the draws. So I'm going to copy all the capital accounts first. So we'll put our cursor on C's capital, hold down control, put our cursor on K capital, hold down control and M capital, holding down control, then let's go of control and then right click on the selected areas and copy. We're going to put that in K13, right click and paste 123. Then the amounts are going to be whatever's in the draws accounts. So C's capital account and the draws account, we have 2000 in it. So 2000 there. Then we have 1000 in K's withdrawals. So 1000 and then we've got 1500 in the draws for M, 1500. Okay so then we're going to close those out to the withdrawals or we'll close out the draws to the capital accounts. So we're going to pick up the draws in a similar way. We're just going to highlight. We're going to put our cursor or select the cursor on C's withdrawals which is in 06 and then select or hold down control and then we'll put our cursor on K's withdrawals and M's withdrawals. So what we're doing is selecting those, let go of control and then we're selecting those non-adjacent cells with the cells that are not next to each other at the same time. We don't have to do that but it's a little faster to do that. We might as well learn how as we go. So we're going to right click on those cells and copy and we'll put that down here in K16, right click and paste 1, 2, 3. Okay so then we're just going to put the same amounts here. So here's the C's capital will be a credit to the draws. So I'm going to do that with a little formula. Instead of putting equals we're going to put a negative to flip the sign of that 2000 and enter. We'll do the same for K's draws instead of equals we'll put negative of that 1000 and enter and then M's we're going to put negative of that 1500 and enter. So there we have it. We have all the capital counts and the draws. We're going to post this out now starting with C's capital which has 2000 in it. Here's C's capital up top. We're going to be here in Q5, double click on it, go to the end of it and plus point to that 2000 bringing the balance from 42,333 down by 2000 to 43,333. And then we have K's capital which has 1000 in it. Here's K's capital on the trial balance. We're going to go to Q7, double click on it, go to the end of it and plus and then point to that 1000 and enter. So that'll bring the capital count down. Then M's capital we'll go here to M's capital we're going to be in cell Q9, double click, go to the end of it and plus and point to that 1500 and enter. Now we'll record the withdrawals which should make all the draws accounts go to zero. So we will start at Q6, Q6 where we will say equals, point to that 2000 bringing the 2000 balance down by 2000 to zero and then we'll be in Q8 where we will say equals and point to the 1000 for the withdrawals bringing the 1000 draws down by 1000 to zero and then we're on the last withdrawals here for M which will be here in Q10 where we will say equals, point to that 1500 in M18 bringing the 1500 balance down by 1500 to zero. So that's what we have now. We're left with pretty much a post closing trial balance one in which there are no temporary accounts, no income statement accounts, no revenue and expense accounts and no draws accounts and in any of the draws we're left with then revenue or not revenue. Assets minus the liabilities which equals the 123,000 in this case and that will be equivalent to what's in equity which will be the sum of all of the capital accounts 123,000 in the capital accounts. So very similar to a sole proprietor here in the post closing trial balance only difference being that instead of one capital account remaining we have three capital accounts. We need to make sure that we're keeping track of these capital accounts because they represent the amount of net assets that is due to or owed in the partners to the partnerships. In other words you can think of it if we liquidated the partnership then in theory the partners would get these amounts. Now that doesn't happen, we'll do a liquidation problem multiple of them but that doesn't happen in practice most of the time because we might sell something like the equipment on the books and we may not get 80,000 for it, we may get more than 80,000 for it, we don't know until we sell it. So if we were to get exactly 80,000 for say the equipment and any other assets we have inventory and whatnot then this would be the exact amount of allocation that we can allocate out but if the liquidation process ended up in something different happening then we'd have some different type of allocation. Also note that we have an even allocation of the net profits here when we allocated the net income we don't have the same even capital accounts here and that is often the case meaning the profit sharing does not necessarily coincide with the draws or with the investments and therefore the ending resulting capital accounts will not be in the same type of ratio as the profit sharing it's not a one-third, one-third, one-third each of the net assets allocated to each partner because the things that could differ are investments and draws which aren't on a one-third, one-third, one-third basis.