AR-Vocabulary-Learning-Analytics
Project Overview
This project aims to analyze user data collected from an Augmented Reality (AR) technology-supported vocabulary learning application. Our main objective is to determine the impact of users' demographic information, interaction behaviors, and the specific AR features utilized on their final vocabulary learning success (Post_Test_Category).
The insights derived from this analysis will be used to enhance the effectiveness of AR-supported education and identify which application features best support learning.
Core Objectives
- Exploratory Data Analysis (EDA): Visualize the primary trends, distributions, and inter-variable relationships within the dataset.
- Learning Gain Analysis: Investigate the relationship between pre-test scores (
Pre_Test_Score) and post-test success (Post_Test_Category), and assess the effect of different AR features on learning outcomes. - Classification Modeling: Develop a machine learning model to predict a user's success category (Low, Medium, High) in the post-test based on their activity data (Engagement Score, Duration, Pre-Test Score, etc.).
Repository Structure
The project follows a standardized and sustainable directory structure.
AR-Vocabulary-Learning-Analytics/
├── data/ # Data files
│ ├── raw/ # Original, untouched data (AR_Vocabulary_Dataset_Char_Target.csv should be here)
│ └── processed/ # Cleaned and feature-engineered data, ready for modeling
├── notebooks/ # Jupyter Notebooks containing exploratory analysis and model development
├── src/ # Reusable Python modules (functions, classes)
│ ├── features/ # Feature engineering scripts (e.g., build_features.py)
│ └── models/ # Model training and evaluation scripts (e.g., train_model.py)
├── reports/ # Project outputs (visualizations, final reports)
├── .gitignore # Files and directories to be ignored by Git
├── README.md # Project description (This file)
└── requirements.txt # List of all necessary Python libraries
Setup and Execution
Follow these steps to set up and run the project in your local environment.
1. Environment Setup
Install all the required Python libraries using the requirements.txt file:
pip install -r requirements.txt