Writing Main Areas of Content
Hello everyone. Welcome back to episode three of Intro to Portfolio presented by Advocate Cash App from Downing Town East High School and PA. And thank you to TSA Global for letting me post these videos.
Okay, so the content varies between all three events that I've been describing in this series.
So first off, data science that is more so charts, graphs, and analysis. So, I'll show you later in this video how to do it, but you just need to show your figures and how they are relevant to whatever you're investigating. So, it's pretty straightforward.
Audio podcasting, it's a little bit more open-ended or it's up to interpretation. So, it's how you want to showcase your digital model. So, usually they use a track timeline or something. It's just how you present your track timeline. I'll give you the dos and don'ts later, but for right now, that's all you need to know.
And then lastly, engineering design is just showing the solution that you came up with. So me and my team, we used a nitrogen filter. So we just took a few pictures of it and then connected it to the theme and then we filled a few requirements that were on the rubric.
So um firstly data science obviously I already said this but it's charts and graphs with analysis under it. So for data science and geospatial and all of these other research-based events, it's unlimited pages for the results. So, this is the time to get in everything you want to say. Obviously, it needs to be relevant, but let's say you couldn't fit something in the introduction or the purpose and methods. If you can find a way to kind of blend that in with your actual result section, then do it. If there's something crucial that you just couldn't say, say it now because there's no other time too.
Um, the analysis should not be too long after every single figure. I'll show you the exact format in the next slide, but it's just one or two paragraphs after the analysis or after the figure should be cited. So if you have like a chart or graph maybe like one paragraph um just describing what's in that graph or figure or whatever and then like the next paragraph can be connecting that to the prompt using studies done by universities.
Um you should also emphasize the statistics that are in the figures because it'll legitimize your claims. So if you know the judges will understand your claim and believe it a lot more if you don't just say hey the housing prices have gone up. You can say the HPI index has risen over 13% over the last 10 years and then you just cite the source of whatever data says you found that from and it'll legitimize your claim because there's going to be more reason to believe it instead of just you know your word.
And then lastly, make sure to utilize studies to back up your claim. So after you state all the statistics that you found from the data sets, also make sure to emphasize the connection because there's two parts of connecting a data set. is the actual numbers and like the correlations between it and why it connects. So for example um one of the indicators that me and my team investigated for data science was social mobility. So we found that social mobility increased as the lack of affordable housing decreased right but there's no straight connection. So we took um research from you Chicago from some researcher and connected the two.
Also, don't exaggerate the results from the studies to make your point stronger because everyone will see through it because as I said in the previous two videos, and I'll keep on saying it, there's no such thing as a perfect study. There's nothing, nothing's going to be perfectly correlated to something else. But the judges will understand because they're in the profession that they're judging. So, they'll understand, oh, this just shows a positive correlation or negative correlation. This is what happens in my job day to day. And there's still some sort of pattern between the two.
And then here's an example. So the first loop is a regression model. Obviously my team had graphs and charts but this is one page of the portfolio. So this is the source for the data set that we used below here. Um we made a regression model instead of a chart for this one because there were too many um data inputs and there just wasn't the graph that didn't look nice enough. Right? So here's the regression model. So this is that actual raw data. And obviously we kept the percentage increases on the side just so that the judges when they were looking at it they didn't have to do the analysis themselves. And then down here, just one pretty large paragraph showing what's up here, what this actually means, and then connecting it. You can see that we used two studies from MIT and then another study from UC Berkeley. Both of these kind of connected it. If you want to pause the video here and actually read this entire page out loud, you'll understand what I'm saying.
So, next is audio podcasting. The main portion of this event is a little smaller than the other two just because it's not so much based on the portfolio, more so the digital model. So this covers the track timeline and regardless of whatever digital model event you're doing like music production, digital video, audio podcasting, and whatever else, you will have something like this that just showcases what happens at what point in time in your submission. So I'll give you the example of the timeline that me and my group submitted, but it's really up to interpretation. Like there is no set standard. I'll just show you what we did and why we did it, right?
So, um, two things that I can give advice on that just should never happen. So, never do a text timeline where you say at 2 minutes and 10 seconds this happened, um, this sound effect played. It's just not going to look good and judges would rather see something visually appealing, especially in these sorts of events where it's not as serious in a way.
Um, and always incorporate some sort of visual. It doesn't have to be what I show you in this next slide, but it should be something visual so that the judges can really engage with it. And even with your visuals, make sure they're formatted formally. Don't just have it, you know, all randomized. I'd say make sure it's all in a certain pattern because there is still a level of formality to every single TSA event, even if it's a little bit one of those light-hearted ones like audio podcasting.
So, this is the example that me and my group submitted. So, there's four different, you know, soundtracks on our audio podcast. So, this is the main one. It's just who's speaking. So, we had four people in our group obviously. And then also like look at this here where the actual time marks. So, these all correlate obviously, but they don't have to be perfect just as long as they're generally in the same area. Um, yeah. So, this is just all the audio tracks and we have the dialogue summary over here. So, whatever is being said um by the speakers is just like summarized here. But also see how they're cut into sections because in our podcast we had like different sections of it. So the first section we just summarized what we said there and then we cut it and then we went to the second section because we always have like some sort of transition noise in between the two like you'll see the spy music correlated to this dialogue, the upbeat music correlated to this, the suspense correlated to this, etc., etc. And then the sound effects. Also, don't worry about the sizing here. Just make sure the text looks nice because if you worry about the sizing, you're not going to get it right. Obviously, the drawer opening isn't 10 seconds long, but as long as it's somewhere in the general area, the judges will get what you're trying to say.
And then lastly, engineering design. Unfortunately, I can't really show the portfolio for this one because the file got corrupted. I tried to find the national submission, but I couldn't. So, I'm sorry, but I'll just, you know, briefly go over what it should look like.
So, the main portion is the selected solution and the written summary of the iteration process. Both of these are worth double weighted, so they're both worth worth 20 points, which is a lot, especially in the TSA world. So, make sure that you know they're good. So the first point is be in depth with your reasoning for the selected solution. Don't just use all the pages describing what you picked. Talk about why it's better than the other proposed solutions. So obviously at this point in time you won't have the physical model when you're writing this but you'll have the CAD down. Emphasize on the reasoning like oh this is more energy efficient or this will do more in the future and has more production because of this right and then compare it to the other solutions. So like the proposed solution one only had, you know.5 watt efficiency versus the solution we picked had 1.3.
So again just like data science, use statistics to your advantage because judges won't listen to oh this one looks prettier or oh this one is smaller. Talk about why it's better because it's smaller. Talk about why it's better because it's prettier. You know you don't want to just say we picked this one and this is why. Here's more about it. cuz that's what the entire the next section is for. Right?
So, moving on to the written summary of the iteration process. So, this is not specific to engineering design. There's variations of it, but I'm just going to go over the engineering design one. But, everyone has some sort of type of this. So, make sure you're reading the rubric and describing your physical project like it asks you to look at everything specifically and don't just go off of this video unless you're doing engineering design because it could change. It's def it's probably not the same, right?
So for example, engineer design has four different descriptions for a minimum of four pages but there's no maximum. So this is where you would say everything that you need to say that you didn't have time to beforehand. So firstly what we did right before we mention any four of these points we did an extended description on the project. So we just said we talked about how our nitrogen filter did this and that which we couldn't mention in the aforementioned sections because you know it's for something else. So during the selected solution part of it, we're just talking about why it's better than alternative solutions and the real world solutions that are out there right now instead of extending it. And I think that's why we play so well.
So the four things that you need is how you test it or would test your disc of your creation. So how you tested it depends if your model's actually fully functional and how you would test it if it's too large of a scale for you to build an actual model and what you would do in industrial terms, especially if you're trying to manufacture your product, right?
And then secondly, the refinements that should be made to the testing criteria if it's tested by the team. So obviously your testing isn't going to be perfect and there's always something you're going to miss, but especially while you're high schoolers, you won't be able to do everything that you need to do to fully test it. So here's the time to disclose, oh, if we did this or that, maybe it would have been more accurate testing wise, right?
Um thirdly, the reflection on the effectiveness of the solution. So this is the reflection of how good it is. So even if it's not perfect, you can disclose that. You don't have to. The judges aren't expecting high schoolers to create the next thing to change the world. They're expecting you to give your best shot at it. So, if you're honest with your feedback and explain, oh, we could have done this and that better and as if you're starting this at like regionals or something, say as you go up throughout like the TSA competitions, it'll get better and better and we'll focus on this and that. Then they'll really understand and they won't like taking off points for it.
And then lastly, the last section is issues found during the iteration process. So, this is just everything that was wrong with the design that you fixed. So this gives you a little bit more room to talk about things like how you troubleshoot something. So for example, my team and I with the nitrogen filter at first it just didn't work like it wasn't waterproof because we had an electronic model. So we had to talk about how we had to waterproof it and then we had to troubleshoot that entire right. So that's just an example. But obviously you're going to come through trouble when you're building something. Nothing's ever going to be perfect. So just talk about that and then the judges like it'll make your team more personable to them so they'll understand your final result a little bit more.
So that wraps up our video. Thank you everyone for watching. Shout out to TSA Global for letting me post these videos on this account. And if you have any questions, let me know.