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Thang Truong

Thang Truong

(27-31) Thang Truong

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Project 1: Polls

Started on: Jan 27th, 2024

Staff Guidance: Chris Pittman

Description: The aim of this project is to incorporate the PDF integration into UT Instapoll. This features help student to download all of the poll responses as a study guide for a test. The PDF study guide comprises all of the questions, answers and the correct answer choices.

Documenting my thought process for this project. There will be two parts:

  1. Creating the route, the controller, the blade template.
  2. Transform the Blade Template into PDF

1. Create the route, the controller and the blade template:

Route: This step establishes the URL end point to let the user access to get the PDF. For this, I chose teacher/course/1/poll_pdf.

Controller: The controller part controls the logic of back-end behind this operation. It will get the polls from the database so that I can access the question, response and the correct answer choices.

Blade Template: The blade template displays the HTML with all of the poll responses and correct responses. The template for the PDF is still in consideration.

HTML Result: The result looks something like this.

2. Part 2 is still in progress

This part, I still need to integrate the library to get PDF integration working.

Project 2: Qualtrics-LTI

Started on: Jan 20th, 2024

Finished on: Jan 27th, 2024

Staff Guidance: Chris Pittman

Description: Adding key and value to facilitate the process of adding these values onto the page.

November Updates

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November Updates

November went by so quickly. It has been half of the month. For this past month, I have done more work with Shiny App and Texas Politics project.

Shiny App Project

Advisor: Stacy Vlasits

Description: For this project, I continued to work on implementing features for the application built in Shiny App. The client requested two more features to be added: working with URL input and working with CSV files.

In tackling these two challenges, I isolate the function that handle the data uploads. The answer is that it is in page 2 and then, I looked at how to handle URL input and turn that into a table format. When I looked at the function that handles uploads of the data, I actually find a solution that works for both URL input and CSV input. I just need to dynamically change the extension accordingly to whatever the format of the file uploads. For example, the CSV would have csv extension and TSV would have tsv extension. Moreover, for handling URL string, I just need to extract the base of the string to get the extension.

A function like this solves both problem at the same time:

# Get file extension from either uploaded file or URL
ext <- if(!is.null(input$file1)){
tools::file_ext(input$file1$name)
}else if (!is.null(input$urlInput) && input$urlInput != “”){
# extract extension from URL
tools::file_ext(basename(input$urlInput))
}else{
“csv”
}

Texas Politics

Advisors: Chris Pittman

Description: For this project, I only get the overview of what the project is about. I did not start to look closely into the solutions. From the outset, it seems like the project involves a place for storing all of the updated elements including charts and blogs. Also, the project incorporates a change in the database with a boolean value “updated me” so that the blog, chart will be included into that place. The idea behind is that every year, there would be blogs, charts and other elements that get to be updated. Currently, there is one guy that have all these information but to streamline the process, we can leverage technology to keep track of all these information. It helps to onboard new people quickly to the updated part of the project.

October Updates – Thang Truong

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October

Project: Shiny App

Started on: Oct 1st, 2024

Staff Guidance: Stacy Vlasits

Description: 

  • Modularizing the application: before, everything is in one single file and the file content is very long. This makes it hard for developer to debug. Therefore, I proposed an idea of modularizing the application into sub-files and let the main file refer to these sub-files.
  • Make application work in the modularization of the application: the next challenge is that with the modularizing process, the logic of the modularizing code is different. Thus, I spent some time to adapt the working prototype to work in the new modularizing structures.

Project Laravel PHP extension

Staff Guidance: Chris Pittman

Started on: Oct 1st, 2024

Ended on: Oct 15th, 2024

Description: 

  • When an app is created, there are some utility functions that the app always need. Two of them are when creating users and reporting course retention (course that no longer active).
  • Thus, I make those two utility functions in Laravel PHP that can be imported in other apps.

Week 38: Instapoll + Presentation

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Project: UT Instapoll

Started on: Mar 26, 2024

Staff Guidance: Chris Pittman

Description: 

  • Task: Change ‘Normal’ grading explanation for non-MC polls #110: The explanation for ‘Normal’ grading reads “Students must select a correct answer”. This makes sense for MC polls, but not for text entry or attendance polls. Change the wording here when the poll type changes.
  • Visual Description:

Breakdown of the task:

Update:

Phase 1: Operation Finding Where To Change

  • Identify in the codebase the explanation of normal (resources/js/components/PageTeacherPollSetDefaults.vue) -> Actually, this vue file is for the poll defaults. Might need to consider: Should we also change the explanation in the poll defaults? ✅
  • The actual location of the file is PageTeacherPollEdit instead of PageTeacherPollSetDefaults ✅
  • After changing the file, need to do npm run build to update the vue file -> The PageTeacherPollEdit works ✅
  • Change the explanation to some other text and test it out ✅

In this screenshot, the normal becomes crazy normal (works) ✅

Phase 2: Experiment with if/else statement

  • Include if/else statement to change the explanation when the question type changes (include v-if for that label)
    Might do something like this so that whenever it detects the question type to “text entry”, the explanation changes to something else. ✅

Screen Shot 2024-03-26 at 3 52 48 PM

  • The third steps include the knowledge of how to retrieve the question type and when to trigger the change in the question type (only use if/else statement is sufficient) ✅
    Might look something like this:

– Test it fully with UT Instapoll

Some considerations:

  • The text: What should the explanation for multiple choice be?
  • Is this the optimal solutions
  • Are there any cases where it breaks? (Think about edge cases?)

Something to take in mind when changing:

  • Whenever the vue.js file gets updated, always npm run build
  • Always use === instead of only == for stronger comparisons.

 

Project: STA Presentation

Started on: Mar 27, 2024

Staff Guidance: Mike, De’sha

Description: 

  • Plan out the STA presentation outline

https://docs.google.com/document/d/1j4EJSDJxxyTbDPehnLjNEdy0JwYLmTp3yKytZXwnaOo/edit?usp=sharing

 

 

WEEK 36

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WEEK 36 + 37

Project: Shiny App

Started on: Feb 26, 2023

Ended on: Mar 4, 2023

Staff Guidance: Stacy

Description: 

 

 

Project: Shiny App (Part 2)

Started on: Mar 4, 2023

Ended on: Mar 7, 2023

Staff Guidance: Stacy

Description: 

  • The client wants a new map to be hosted and developed in shiny app.
  • The process is very similar to the previous project when I need to display a fully functional map with sliders and zoom working properly.
  • Therefore, applying what I know from before, I can generate a temporary HTML file and display that on the website
  • Also, I just finished the functionality of downloading the map.

 

Project: Whisper AI

Started on:  Feb 19, 2023

Ended on:  Mar 4, 2023

Staff Guidance: Chris

Description: Finish the evaluation for Whisper AI 

Whisper AI 

  • The Whisper AI converts the audio file into a spectrogram to analyze and predict the corresponding text captions.
  • It takes a 30 second chunk of input, converts it into a log-Model spectrogram, analyzes the image and applies encoding-decoding architecture to predict the text caption based on those images. 

Some key insights: 

  • Lower HST produces much cleaner version of text (not including filter words as much as higher HST (from 0.2 to 0.5)) 
  • There are some variation of inference ability of each time: eg. truth work done instead of tooth work done. Therefore, a lot of words are predicted differently 
  • Even with the same threshold, there is variation in running the models from time to time. 
  • A lot of models do not capture the first intro sentence from the lady. 
  • 1.3 is the fastest and 0.1 is the slowest -> Might suggest that as the parameter increases, the time it takes is faster -> It means it does not need to re-process so many times. With 0.1, it means that it re-process every 0.1 sec after silence is detected -> suggesting more time to process

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