Difference between revisions of "Modeling the Progress and Efficacy of Wisconsin CS Education and an ML Approach to Static Code Analysis"

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(Created page with "== Project Description == Computer Science education has never been so critical to the next generation of academics and industry leaders as it is today. 67% of all new STEM jo...")
 
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== Project Description ==
 
== Project Description ==
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Computer Science education has never been so critical to the next generation of academics and industry leaders as it is today. 67% of all new STEM jobs created are in computing, but only 11% of STEM bachelor degrees are in computer science. This massive disparity is exacerbated by a fundamental lack of CS education in middle schools and high schools across the country. This project aims to uncover new information about how CS education has changed in the state of Wisconsin, and visualize the data in such a way as to encourage policy makers to take a serious look at the approach schools take with respect to computer science. Furthermore, this project also seeks to aid CS education at the university level by integrating static analysis techniques into Marquette's TABot, used in the grading of student-written C code in classes such as Operating Systems.
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Computer Science education has never been so critical to the next generation of academics and industry leaders as it is today. 67% of all new STEM jobs created are in computing, but only 11% of STEM bachelor degrees are in computer science. This massive disparity is exacerbated by a fundamental lack of CS education in middle schools and high schools across the country. This project aims to uncover new information about how CS education has changed in the state of Wisconsin, and visualize the data in such a way as to encourage policy makers to take a serious look at the approach schools take with respect to computer science.
  
  
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* ''Gather, clean, and interpret CS education data concerning teacher licensure, enrollment statistics, program quality, and geographic location
 
* ''Gather, clean, and interpret CS education data concerning teacher licensure, enrollment statistics, program quality, and geographic location
 
* ''Develop models trained on the data to predict what CS education might look like in the future
 
* ''Develop models trained on the data to predict what CS education might look like in the future
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* ''Integrate existing static analysis tools with TABot and analyze the pros and cons of particular methods
 
  
 
=='''Milestones:'''==
 
=='''Milestones:'''==
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*Automate process of data scraping
 
*Automate process of data scraping
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*Read related literature on static code analysis
 
 
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|Week 4
 
|Week 4
 
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*Visualize/interpret collected data
 
*Visualize/interpret collected data
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*Experiment with different static code analysis techniques on sample code
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|Week 5
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* Correlate scraped data with existing external records
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|Week 6
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*Outline potential ML approaches for analyzing results
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|Week 7
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*Continue verifying data and creating visualizations
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|Week 8
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*Train and analyze ML models
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|Week 9
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*Congregate the summer's work and record final observations
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*Poster/paper/presentation work
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|Week 10
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*Poster/paper/presentation
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*Wrapping up
 
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Latest revision as of 18:16, 2 August 2020

Project Description

Computer Science education has never been so critical to the next generation of academics and industry leaders as it is today. 67% of all new STEM jobs created are in computing, but only 11% of STEM bachelor degrees are in computer science. This massive disparity is exacerbated by a fundamental lack of CS education in middle schools and high schools across the country. This project aims to uncover new information about how CS education has changed in the state of Wisconsin, and visualize the data in such a way as to encourage policy makers to take a serious look at the approach schools take with respect to computer science.


Project Goals

  • Gather, clean, and interpret CS education data concerning teacher licensure, enrollment statistics, program quality, and geographic location
  • Develop models trained on the data to predict what CS education might look like in the future

Milestones:

Week Description
Week 1
  • Initial meetings with mentor
  • Data science crash course
Week 2
  • Read related literature on CS education
  • Practice machine learning techniques
  • Begin scraping/cleaning data
Week 3
  • Automate process of data scraping
Week 4
  • Visualize/interpret collected data
Week 5
  • Correlate scraped data with existing external records
Week 6
  • Outline potential ML approaches for analyzing results
Week 7
  • Continue verifying data and creating visualizations
Week 8
  • Train and analyze ML models
Week 9
  • Congregate the summer's work and record final observations
  • Poster/paper/presentation work
Week 10
  • Poster/paper/presentation
  • Wrapping up
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