Engineering – Page 13 – UROP Spring Symposium 2021

Engineering

Programmable Tools for Teaching Data Literacy in Social Studies Classes

Participation in high school computer science classes has always been low, especially among students from traditionally underrepresented or underserved groups. This research project aims to explore an approach of creating opportunities to integrate computer science into existing non-CS classes which creates the opportunity for students to engage with computer science and gain an introduction to programming. By creating curriculum and proprietary data visualization tools for high school social studies classes, this project hopes to create computational tools that social studies teachers will actually adopt, and that will then identify an increase in student’s self-efficacy in regard to computer science. Our procedure to obtain student success data in learning data visualizations will be conducted through data logging of the proprietary software DV4L (Data Visualizations for Literacy). The product created for this study includes our own proprietary tool DV4L, as well as curriculum for high school teachers to understand and teach existing data visualization tools. I have been working to create minimal manuals hosted on the project web book that help teachers use data visualization tools. Additionally, I have been working on creating slow reveal graphs that allow students to understand the components of various data visualizations. Finally, I have been supporting the underlying infrastructure that hosts these manuals and tools by creating a new server platform for the project web book. With these products we hope to be able to change high school social studies instruction in the state, and eventually change the social studies classes on a nationwide basis. Our project hopes to see an increased understanding of data visualizations in high school students, and in the future see an increase in computer science class participation.

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Programmable Tools for Teaching Data Literacy in Social Studies Classes

A missed opportunity in K-12 education is the introduction of computer science principles and data literacy. The research project, Computing for Data Literacy, aims to give educators the ability to teach data literacy in their social studies classrooms. This is a design based research project in which we are introducing a potential solution to a problem and then analyzing how well our solution works. This is different from traditional hypothesis testing research. Our solution is through the prototype web tool named DV4L, “Data Visualization for Learning”. DV4L allows for students to ask historical driving questions and then get the relevant data visualizations so they can inquire about the answer. The contribution that my team and I are making is tracking the activity of each user on the website to determine whether they are gaining educational curiosity. My team and I make sure to never attach student identification information to our log files as to maintain the privacy of the students.”Gaining educational curiosity” is measured through many different fields. First, the student would be gaining educational curiosity if they were asking driving questions that were out of the syllabus or assignment given to them. It also tracks whether they write down notes to go along with their new graphs. Lastly, it also checks if the students were looking inside the underlying script, or source code, for each of their graphs. With those measures and more we are going to be able to analyze whether students are benefiting from data visualizations. This project is still ongoing, and we are hoping to have our tools in actual classroom settings sometime this semester.

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Programmable Tools for Teaching Data Literacy in Social Studies Classes

In the world of teaching social studies to k-12 students, one of the key learning objectives that teachers lack the proper resources to teach is data literacy. This study includes the key components that interviewed teachers wanted as part of their data literacy tool. An important takeaway from these interviews was that teachers believed that not many interactive or useful digital timeline tools existed for use in their classrooms. From the established criteria from the interviews, an interactive timeline allowing students to create event blocks that would then appear on a timeline would be the most useful tool for the team to create. First, three individual mockups were created from the criteria and then put together into a finalized mockup on Figma. From the Figma mockup, the best development tools needed to be decided to achieve the cleanest and most functional result. The working prototype was created by splitting up the development into a timeline and event block component which were then joined to create a final functional prototype that satisfies the initial design requirements. This study is part of a larger effort to incorporate useful programmable and interactive tools into classrooms across the country to aid with teaching and learning effectively.

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Programmable Tools for Teaching Data Literacy in Social Studies Classes

In a rapidly evolving world centered around connectivity and technology, data literacy and basic programming knowledge have turned into some of the most valuable skills children can learn in school. The problem is, however, learning data literacy and programming skills can be daunting even for college students and adults, and research needs to be done on ways to make learning these key skills easier for children. In order to introduce foundations of data literacy to students, one option is to combine data skills with other, more traditional, classes. This project is about creating a timeline tool for middle school students. We hope this tool will allow students to provide data on important historical events and generate a visualization of the data in the form of a timeline. While the project has not finished, we currently are hoping to create a tool that is both easy for teachers to teach and students to use, all while being helpful in advancing student’s knowledge of history and data literacy. In doing this, we hope that this will result in satisfied teachers who are comfortable combining non-data based subjects with data-based learning through this timeline tool and beyond. we hope to see an increased interest in programming and data among students as well as an increase in data literacy. In the end, we are aiming for an easy-to-use timeline creator that will empower students to learn about programming and gain basic data literacy.

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Metabolism and Histone Deacetylases: A Systems Biology Perspective

Histones are proteins that provide structural support to chromosomes and help condense nuclear DNA into chromatin. Histone acetylation is a process that diminishes the affinity between histones and DNA so that gene transcription is more permissive. This process is regulated by the opposing actions of histone acetyltransferases (HATs) and histone deacetylases (HDACs), both of which are critical to many cellular processes such as DNA damage repair and proper transcription. HDACs are sensitive to the metabolic state of the cell, and the dynamic between metabolism and histone acetylation impacts several biological processes, including development and immune function. HDAC inhibitor drugs are currently being explored for treating various conditions, including cancers, viral infections, inflammation, neurodegenerative diseases, and metabolic disorders. However, it is currently a significant clinical challenge to identify subsets of patients sensitive to HDAC inhibitors. Thus, this review compiles recent applications of systems biology methods such as high throughput drug screens, cancer cell-line profiling, single cell sequencing, proteomics, and metabolomics that can help to determine the interplay between metabolism, HDACs, and HDAC inhibitors. Compiling these systems approaches can ultimately help identify epigenomic and metabolic biomarkers for patient stratification, enable the design of synergistic combination therapies, and illuminate the gaps in our knowledge regarding this interplay. For future studies, metabolic modeling and machine learning methods can be used to analyze gaps in our current understanding and identify additional potential biomarkers for patients sensitive to HDAC inhibitors.

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Percolation modeling

Under Professor Ziff, our research group devised various methods of modeling types of percolation. These models were initially based in C and were unoptimized for large-scale run times. We rewrote these programs in C++ and implemented various algorithms and memory structures which helped reduce the runtime of the simulation algorithms. These small improvements yielded a boost in productivity as percolation models have to be run for hours on end to yield large enough outputs to provide viable research data. These small improvements are very important, as improvements in program runtime compounded over hours can have a huge impact and allow researchers to get more done in less time.

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Determining the thickness of glaciers in regions without measurements

Glacial erosion is a major contributor to the global rise in sea level so it is important to be able to predict the long-term fate of a glacier. One important factor is ice-thickness, which can in turn tell us the total volume of a glacier and how it fluctuates over time. However, although the surface elevation of glaciers can be directly measured, it is much more difficult to determine the elevation of the bed of the glacier. Direct measurements of the ice-thickness through boreholes are expensive and time-consuming, so a previous study examining the Columbia Glacier in Alaska estimated ice-thickness using the mass continuity equation between adjacent flowlines. They used available data for velocity fields covering different portions of the glacier, the surface mass balance (the difference of ice accumulation and ablation), and several digital elevation models (DEMs) over a span of about fifty years. Comparisons with existing direct measurements of ice-thickness demonstrated the accuracy of this method, but this amount of data is not available for many other glaciers around the world. We are working with the same DEMs and plotting the surface elevations in different years. We will then try to determine the ice-thickness by calculating the surface slope and driving stress of the glacier, and we will compare our results to direct measurements as well as previous studies. If this approach is successful, we can then apply it to accurately estimate the ice-thickness of glaciers for which more data is lacking.

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APIS Ice Cover Modeling

We updated statistical models that model the onset and extent of ice cover along and around the Apostle Islands National Lakeshore(or APIS) – a US National Park that is famous for its picturesque rock formations in winter and accessed by foot. Over the past 50 years, climate has changed dramatically, leading to extreme variability in when and if Great Lakes freeze to a safe amount. We modeled when ice cover reached a specified extent, and if ice cover would reach that specified extent in a certain year. This project models ice cover extent using survival and beta regression models using teleconnections and historical daily ice cover data dating back to 1973. Analysis of results shows a significant increase in ice cover variability after the 1990s. The model accommodates for this change, and can be split into two different models.

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MetabolonR: An Intuitive R Shiny Application for Metabolomics Analysis

Zyad Shehadeh Pronouns: He/Him Research Mentor(s): Fadhl Alakwaa, Research investigator Research Mentor School/College/Department: Neurology Department, Michigan Medicine Presentation Date: Thursday, April 22, 2021 Session: Session 2 (11am – 11:50am) Breakout Room: Room 11 Presenter: 1 Event Link Abstract For privacy concerns this abstract cannot be published at this time. Authors: Zyad Shehadeh, Fadhl Alakwaa Research

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Utilizing bioinformatics to decipher transcriptomic networks in bone and joint development

To gain a comprehensive understanding of the functions of the myriad of cell clusters within the human body, many cell biology research studies utilize a process called single cell RNA sequencing to analyze the expression of different genes across cell clusters. In order to properly analyze the information gathered from the single cell RNA sequencing process, researchers must hire an outside body to run an analytical program to develop plots and graphs to visualize the data. Problems may arise regarding the accuracy of the developed plots and graphs because of their lack of understanding of the research project itself. This may lead to incorrect assumptions regarding the gene expression and, in turn, the function of different cell clusters. For the Hankenson Lab’s work on gene expression during bone and joint development, I personally ran the analysis and developed the plots for single cell RNA sequencing. The first half of the project entailed using the Single Cell Portal to track the different expression of genes within the WNT pathway to gain a biological understanding of the study. The second part focused on utilizing the Seurat library within R Studio to run multiple analyses on different data sets involved in the study. This involves normalizing the data set, identifying variable features within the data set, running a principal component analysis, and a non-linear dimensionality reduction. All these different programs allow for the development of heat maps, violin plots, and UMAPs. These plots will then be used by Hankenson Lab to further their study in identifying different cell clusters involved in bone and joint development as well as each cell cluster’s function.

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