Tekano Mbonani
A-STEP Impact on Student Success (ASIS), the web application performs hypothesis testing and linear regression/correlation analysis of student tutorial attendance and final exam mark (performance) data, to determine the tutorial impact on students. The code requires student tutorial attedance and performance data files to compile a tutorial impact report per faculty, term (semester) and campus. To achieve this, the data is combined to determine student attendance frequency per module (subject) for the selected faculty, term and campus. The majority of the python code is modularized through functions to get a clean code. I wrote this app for a large tutorial project for students of the University of Free State, comprised of seven academic faculties on three learning campuses. The code allowed my clients to assess the project's performance and impact on its attendees, in real-time, within minutes.
You will need to install the following software on your system in order to run/edit the Python and R scripts.
- Mac OS/ Ubuntu 18.04 OS
- Python 3.10.12
- R 4.3.1
- Textedit/ IDE - spyder, jupyter-notebook or R-studio
- libraries
- pandas
- numpy
- scipy
- pyreadr
- datetime
- fpdf
- matplotlib
- seaborn
- glob
- dplyr
- streamlit
The data used here was collected between 2019 and the first term of 2022, by the tutorial project of students on the University of Free State. The data was collected and stored weekly, where I have access to perform statistical analysis and reporting.
The Web Application has successfully been deployed on the streamlit cloud, users can access it here: https://kxyuzlhdqujmaajlisngma.streamlit.app/