Analyzing the Rubric
Hello everyone. I'm Advoc and I'm a student from Downtown East High School in Pennsylvania. And welcome to episode one of intro to documentation portfolios. In this video, I'm going to break down one of the most important but often overlooked parts of the TSA competition process, the rubric. Whether you're building something, designing something, or analyzing data, your portfolio is going to be judged using a rubric. And understanding it is key to maximizing your score.
So, some credibility before we hop into it. I'm a three times TSA Nationals top five um award winner I'd say in geospatial data science and audio podcasting.
Okay. So there's going to be three types of rubrics that I'm going to be analyzing today. So there's I'd like to classify three different events in TSI. Firstly data science and analytics or like geospatial technology. All of those are research-based, meaning that you're looking into something like a problem that they give you and you're using online materials to back it up. So data science you're taking data sets geospatial you're taking maps etc etc. Um you're not really creating anything now unlike these next two.
So secondly audio podcasting which is a digital model um in quotation marks because it's not really a model. It's more so just a project that you're making. So other things like this is like music production or digital video. Um all of these you have an online component and then your portfolio component. Both of them are pretty heavy in your scoring. So you want to make sure you focus on both of them.
And then lastly is engineering design. This is the physical model. So I mean it's pretty straightforward. You just make something and then you have a portfolio to support it. In this case, the portfolio matters a lot more in engineering design as you'll see in this rubric.
So the first one I'm going to go over is data science. So I'll start by looking over here at the required things and then going over the rubric and the actual scoring. So okay, title page, table of contents. That's easy.
Introduction and data overview. So, for data science specifically, that's just going over what the question or whatever you're investigating, what your research question is, and what data you use to find it. I'll go over this in future videos, like I'm going to go very in depth in future videos, but this is for a beginner understanding of how to look at a rubric, right?
Data dictionary. This is just to um define terms that may not be as common in English. So, for example, um the year that I'm filming this, we had a prompt on housing. So, we talked about gentrification a lot. and you know that's not used very commonly. So obviously we will put that in the data dictionary as well as other data analysis terms. The purpose was to also make sure when you're going through these rubrics these hyphens pretty much lead you.
Rubrics are obviously up to your interpretation. So however you interpret it you might not interpret it the same as I do but making sure that you pay attention to this blurb because it's very important because TSA doesn't give you much else except that and then obviously here. But yeah, making sure you look at this and everything that you wrote down or made fits that criteria is very important.
Moving on, purpose. So that's just explaining why the issue you picked is purposeful and matters a lot in this day and age. So if you talk about the lack of affordable housing, you need to talk about oh that's very important. I mean obviously that's important because some people just don't have homes. So talk about how that's relevant to the real world.
Methods. That's just how you obtained your data and like how you analyzed it. So for example, my team collected um data sets off governmental data sets in Kaggle and then we analyzed them using a Python ML algorithm.
Results. This is pretty straightforward. It's just all your analysis of every single data set that you collected and graphs and other um charts that will support the data. It's pretty I'll go over all of this in later videos. I'm just giving you a brief overview right now. conclusions. It's just a conclusive comparison of what you found to what your original hypothesis was in your introduction. It does not have to be perfect and I want everyone who's watching this video to keep that in mind. Your conclusion doesn't have to perfectly match what you predicted would happen because that never happened in the real world. It just has to show a connection between what you predicted and what actually happened.
Um, the next step is just talk, this isn't a very common thing. It's good to have, but it's not required. It's just talking about what happens after this and how you can fix it. So data science it's required but an event like geospatial which I also did it wasn't required but it's like nice to have you know what I mean because the judges will see this and be and say oh my god we actually have a feature in this this isn't like doomsday just because this issue came up there's a way to fix it.
References. Please please keep track of your references during your project, do not lose them and do not make them up at the last minute. They matter especially when you're putting your sources into things like your introduction or your purpose or your methods. You're taking sources that you're taking from university studies or like research journals and citing them. Make sure you keep track of them and make sure they're professional. It is a part of the rubric and it's very important. Don't forget it.
And then the appendix, this is optional.
Um, okay, moving on. So, just looking at the rubric itself, portfolio components, this is a free 10 points. Just don't miss anything. This is easy.
Intro, data overview, and data dictionary. This is also pretty easy. just talking about what the issue is and your data dictionary should just define terms that may be um hard or not easy to define.
Um purpose and methods. In this section you might not get all the points for it. This is just going over obviously your purpose and your methods. That's pretty redundant, but it's just going over everything like what you're going to do and why you're doing it. You should be able to get nine or 10 points on this for exemplary performance as long as you are concise with what you're saying and it makes sense and it's relevant to these study results.
This is I wouldn't say this is free points but you should be able to score pretty high on this because it doesn't matter how right you are in a sense. It doesn't matter how much it's matching your prediction as long as these graphs show that you did make the connection between the two. So let's say for example in the lack of affordable housing which my team did there wasn't any evidence to support it. You still are saying oh this could be an issue. There's just not strong evidence. So make sure you just keep all your graphs that are relevant, especially those that are like specifically affordable housing. Like there's an HPI index for example that you know we have to keep that no matter what even if that doesn't show a positive or negative correlation.
Conclusion and next steps. This is also a free nine or 10 points. um well, not free, but it's just to conclude all of like after looking at all your data and looking at all your analysis, sum it up in a few sentences. Again, you only get one or two pages for each of these, so make sure they're extremely concise, but they're getting your point across. You don't want to waste words saying something extra that just doesn't need to be said. And then lastly, quality, effectiveness, and mechanics. This isn't too hard. just if you really need to run your thing through Grammarly, but if you're making it on like Google Docs or Canva, you'll see when you make a grammatical error. So, you should be able to get all the 10 points here.
Okay. So, moving on to audio podcasting or the digital model, you'll see that the documentation portfolio is weighted a little bit less because it's more based on the digital model you created. So, the podcast or the music piece you made or the video you made, whatever. Right? So, okay, this is a free podcast cover.
All right. This is um just you know it's specific to audio podcasting. It's just what the cover of the podcast is. It's pretty self-explanatory.
Work log. Um this is very important. Please do not make this up or make it last second. Work logs are extremely important because judges do look over them to see how you created whatever project you're making. No matter if it's a database or digital model or physical model, whatever, you need a work log. So make sure you're not making up on the spot and just recording everything as you go. Right?
The um the self-evaluation of the piece. This is pretty easy. So, you just take in the rubric. It looks like this, but it's for the model you made. So, for example, me and my team, we graded our audio podcast. And on that audio podcast, we said uh we got a nine for this because of that, right? It's just reasoning for why you graded what you grade and what you're asking them to grade you.
Audio composition. This is just every single voice, sound effect, or music track that's played while your um podcast plays. We made this a chart, which I'll show in a later video to make it seem more formal, and I'll go through how to make this. It's pretty straightforward, though. Um, G through J is just sourcing everything that you used. So, these are audio elements that weren't created by the team. Just sort cite them in MLI. Um, this is a list of software, hardware, and other instruments that we used to develop it. This is pretty straightforward. So, obviously, we used a computer, we used FL Studio to create it. We used some background instruments, you know, etc., et, etc. And the list of references should connect to audio elements. That's pretty straightforward as well.
And then a student copyright track list. This is easy. Um, portfolio components. I will say this again and again. This should be a 10 every single time. Especially if you watch this video, you see everything that you need in the rubric and there's no reason that you don't have any of it.
Podcast cover art. This is specific to audio podcasting.
See work log and self-evaluation. Both are extremely important. Please do not skimp out on them. See how they're worth a lot of points.
And then the track timeline. Again, I'll go over this in another video because it is kind of with every single digital model event, but yeah, we'll get there.
Um, and lastly, engineering design. This rubric is extremely long, so I think I'm just going to stick to the actual table because we're going over time.
Um, so firstly, portfolio components. Again, and again, this is free points.
Identification and problem definition. This is kind of like the introduction in data science. This is just going over what you're investigating and why. Um, I'll go over this in the next video, how to write these. They're pretty straightforward.
Information gathering. This is also just writing down everything about your problem that you can in that little one-page gap. So, it's just writing all the vital information that the judges need to know before they read on in the documentation portfolio. I'll go over how to write this and possible solutions in the next video.
Possible solutions are just ideas that you can have that could potentially fix the problem. This is kind of hard to create because you have to create these by yourself. But make sure they relate to the problem. Do not just phone it in or find some design online and just copy that. They need the judges to know if this isn't coming if this didn't come up with the team and you took it from someone else, right?
Um selected solutions. So this is just talking about what your solution is or what the solution out of the three possible solutions you picked and why. And then obviously CAD-ing it and then showing the judges what you did.
And then obviously next is the written summary of the iteration process. This is just how you built what you built. So iteration one, iteration two, that's just the versions of it. So let's say you are making a water humidifier as an example, right? So version one or iteration one will just be like bare bones and then iteration two will be the improvement and so on and so on. The judges will want to see this. This is like the work log before the work log communication of a solution.
This is just the conclusion. It's also just recapping everything that your iteration does and how it does it and how that relates to the problem. Again, you only have a few pages for this. So, you know, be concise with it. As I keep on saying, I will keep on saying this. It's very important.
Um, lastly, work log. You know, I already covered this. Be honest with your work log and keep track of it as time goes on. There's a lot of repetition between every single rubric when it comes to documentation portfolios. will always see these two categories. That's why I'm going to stress how important they are because they are free points if you do them right.
And then references and resources. I'll go over how to source in another video, but just doing in text citations, using credible sources from universities and public research journals, and using them within pretty much everything in this documentation portfolio, you can use it on.