Grade Analysis

This project analyzes survey data on university students' mathematics grades, applying statistical methods (ANOVA, linear regression, and t-tests) to identify key factors influencing academic performance. Based off of https://github.com/prashantva/stem_ed_fall2018 and “Factors Influencing Success in Advanced Engineering Mathematics Courses: A Case Study” by Athavale et al. (2021).

Contents

Data:
-> raw: has all the raw survey data collected from SurveyMonkey.
-> MA23(...) files: cleaned survey data to be processed using prelim.R before analysis.
-> data: files from survey ready for analysis. Also includes the following files: datafor_r.xlsx: 2018 student survey data from “Factors Influencing Success in Advanced Engineering Mathematics Courses: A Case Study” by Athavale et al. (2021).
poll2023.csv: teenager's average screen time from https://news.gallup.com/poll/512576/teens-spend-average-hours-social-media-per-day.aspx?utm_source=chartr&utm_medium=email&utm_campaign=chartr_20240617.

datadictionary.txt: A description of all columns in MA23(...).xlsx files.

Data Preparation:
cleaning.R: shows how files were cleaned from raws to MA23(...).xlsx files.
prelim.R: joins all cleaned files by mathematics courses and returns calc3.xlsx, diffeq.xlsx, and all.xlsx, which are needed for all analysis files.

Analysis:
anova.R: Comparing screentime, Calculus II grade before and after COVID-19 using ANOVA. Includes violin plots, QQ plots to verify normality.
linearModels.R: Correlation plots and stepwise regression of grade in mathematics class and other survey results. Includes correlation plots.
tTest.R: t-tests of Calculus II grade, screentime, Elementary Differential Equations grade, and self-reported study hours. Includes box and whiskers plot of the 4 variables of interest.

Building the R file for visualizations

For master branch:
No building needed, all analysis files have required pathing and data.

From original survey data:

1. Start with cleaning.R to process all the files in gradeAnalysis/raw, you will need to change the file names in the path.
   
2. After obtaining all gradeAnalysis/MA23(...) files, run prelim.R to get files in gradeAnalysis/data required for analysis.
   
3. Run desired analysis file (anova.R, linearModels.R, tTest.R).

Support

Contact mihuynh@clarkson.edu or pathaval@clarkson.edu