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<!DOCTYPE HTML>
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<head>
<title>Competitions by Sam Thach</title>
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<a href="index.html" class="logo"><strong>Competitions</strong> by Sam Thach</a>
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<section>
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<h1>South Carolina Big Data In Health Competition 2022</h1>
</header>
<span class="image main"><img src="images/Competition2.png" alt="" /></span>
<hr class="major" />
<h2>Overview</h2>
<p>
This year's Big Data In Health Competition had all teams work on 5 big data sets from 2016 to 2020 in order to answer which certain variables were used to create a binary flag. The flag was whether or not someone's
hospitalization was unnecessary. The competition's first round lasted 24 hours and the second round another 24 hours. My team participated and presented in the first round but did not make it to the second.
</p>
<hr class="major" />
<h2>Results</h2>
<p>
Because data was confidential, I can not share names of variables or results in detail. However, I determined there were about five different variables that contributed to the flagging algorithm.
</p>
<hr class="major" />
<h2>Significance</h2>
<p>
Improving the flagging algorithm will help medical experts to prevent unnecessary hospitalizations.
</p>
</section>
<section>
<header class="main">
<h1>Special Interest Group Computer Science Education Student Competition 2022</h1>
</header>
<span class="image main"><img src="images/Competition3.png" alt="" /></span>
<hr class="major" />
<h2>Overview and Motivation</h2>
<p>
I worked as a research assistant in the Computer Systems Engineering Technology department at the Oregon Institute of Technology. I was given two choices of research project and decided to choose one focused on higher education recruitment.
The project consisted of creating six word clouds for six computing majors at OIT and having students participate in a web game. In this web game, students matched up the word clouds to the computing major. My motivation behind this was a personal one
since I had changed majors in my sophmore year due to not understanding what my previous major entailed. My research advisor had participated in SIGCSE before and submitted my work under their student competition. I presented virutally for the first round but did not make it to the finalists.
The judges were very interested in my motivation and work since it could be expanded to more majors other than computing majors.
</p>
<hr class="major" />
<h2>Results</h2>
<p>
We had a total of 26 participants out of a total 97 recruited. Students who participated had the most difficult time distinguishing majors that were within the same department. For example,
The Computer Systems Engineering Technology Department had three of the six majors and were the lowest scores at 27%, 23% and 38%. This is compared to other majors such as applied mathematics with a score of 73%.
This showed me that computing majors within one department are most difficult for students to distinguish due to many overlapping courses that translate to the word clouds as overlapping words.
</p>
<hr class="major" />
<h2>Significance</h2>
<p>
Since students have difficulty distinguishing similar majors, I have determined a course of action to alleviate this. Course descriptions should use laymen terms that incoming students understand rather than complex words that only those who have taken the courses understand.
Course Descriptions should also have as few overlapping terms as possible. The issue with this approach is that many majors have overlapping courses which make it difficult to limit the number of overlapping terms.
</p>
</section>
<section>
<header class="main">
<h1>American Statistical Association Datafest</h1>
</header>
<span class="image main"><img src="images/datafest_logo.png" alt="ASA Datafest Logo" /></span>
<hr class="major" />
<h2>Overview and Motivation</h2>
<p>
2022 Datafest was a weekend long competition that was done in a team. We were given educational data and tasked with finding informative results.
The motivation was to find informative results that could provide useful insight towards how education is provided.
</p>
<hr class="major" />
<h2>Results</h2>
<p>
Data was analyzed in the statistical coding language R and a visually appealing writeup was generated in R markdown. Our results were presented asynchronysly via prerecorded video. Results showed that students who spent too long or too little time watching assigned videos did not learn as much as
students who watched in between. In other words, there is a sweet spot of time that students should spend learning/studying; too long gives bad results and too short gives no results. My team was awarded an honorable mention of most insightful finding.
</p>
<hr class="major" />
<h2>Significance</h2>
<p>
In the real world, and as a university student myself, there are many students who do not study at all or study too much. The results we found in this competition showed that doing so is counter productive. There is an amount of time that is the most
ideal when it comes to learning or being productive. If I had more data and the time, I would try to create an algorithm to determine the ideal amount of time for each person.
</p>
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<li><a href="Work_experiences.html">Work Experiences</a></li>
<li><a href="Competitions.html">Competitions</a></li>
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