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A supervised learning project using eXtreme Gradient Boosting Trees. This project creates a predictive model for understanding the dynamics of gender in intro CS at Berkeley for years 2014 through 2015.

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Cultural Analytics

Identify Factors that Predict Intro CS Experience Based on Gender.

A Supervised Learning Project.

This project adds a predictive model for understanding the dynamics of gender in intro CS at Berkeley for years 2014 through 2015. This work builds on previous research done in fulfillment of a Computer Science Education Ph.D., HipHopathy, A Socio-Curricular Study of Introductory Computer Science.

I did a mixed-methods formative research that answers the question, “What are the socio-curricular factors that lead historically underrepresented students to choose CS?”

I broke down the problem into multiple well-defined components, each one solving a specific aspect of the problem. The projects generated can be found in the following repositories:

Analysis

An analysis of this project can be found here report/CSExperience.pdf
The technical implementation can be found in:

Data

The dataset used in this project not available for mass consumption, as it contains sensitive, personally identifiable student data. To generate this dataset, I created a survey that includes the following attributes for each data point based on a Likert scale of 1 to 5, 1 for strongly disagree and 5 corresponding to strongly agree. Some items have yes and no answers.

Self-reported attitudes about CS

  • atcs_1 I like to use computer science to solve problems.
  • atcs_2 I can learn to understand computing concepts.
  • atcs_3 I can achieve good grades (C or better) in computing courses.
  • atcs_4 I do not like using computer science to solve problems.
  • atcs_5 I am confident that I can solve problems by using computation
  • atcs_6 The challenge of solving problems using computer science appeals to me.
  • atcs_7 I am comfortable with learning computing concepts.
  • atcs_8 I am confident about my abilities with regards to computer science.
  • atcs_9 I do think I can learn to understand computing concepts.

Gendered belief about CS ability

  • atcsgender_1 Women are less capable of success in CS than men.
  • atcsgender_2 Women are smarter than men.
  • atcsgender_3 Men have better math and science abilities than women.

Career driven beliefs about CS

  • atcsjob_1 Knowledge of computing will allow me to secure a good job.
  • atcsjob_2 My career goals do not require that I learn computing skills.

Self-reported attitudes about computational thinking

  • atct_1 I am good at solving a problem by thinking about similar problems I’ve solved before.
  • atct_2 I have good research skills.
  • atct_3 I am good at using online search tools.
  • atct_4 I am persistent at solving puzzles or logic problems.
  • atct_5 I know how to write computer programs
  • atct_6 I am good at building things.
  • atct_7 I’m good at ignoring irrelevant details to solve a problem.
  • atct_8 I know how to write a computer program to solve a problem.

Self-reported attitudes about CS class belonging

  • blg_1 In this class, I feel I belong.
  • blg_2 In this class, I feel awkward and out of place.
  • blg_3 In this class, I feel like my ideas count.
  • blg_4 In this class, I feel like I matter.

Self-reported beliefs about collegiality

  • clet_1 I work well in teams.
  • clet_2 I think about the ethical, legal, and social implications of computing.
  • cltrcmp_1 I am comfortable interacting with peers from different backgrounds than my own (based on race, sexuality, income, and so on.)
  • cltrcmp_2 I have good cultural competence, or the ability to interact effectively with people from diverse backgrounds.

Demographics

  • gender Could I please know your gender

CS mentors and role models

  • mtr_1 Before I came to UC Berkeley, I knew people who have careers in Computer Science.
  • mtr_2 There are people with careers in Computer Science who look like me.
  • mtr_3 I have role models within the Computer Science field that look like me.

Prior collegiate CS exposure

  • prcs_1 Did you take a CS course in High School?
  • prcs_2 Did you have any exposure to Computer Science before UC Berkeley?
  • prcs_3 Did a family member introduce you to Computer Science?
  • prcs_4 Did you have a close family member who is a Computer Scientist or is affiliated with computing industry?
  • prcs_5 Did your high school offer AP CS?

License

The contents of this repository are subjected to the GNU GENERAL PUBLIC LICENSE.

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A supervised learning project using eXtreme Gradient Boosting Trees. This project creates a predictive model for understanding the dynamics of gender in intro CS at Berkeley for years 2014 through 2015.

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