EPM (Educational Process Mining)

Team Members: Meredith Luo, Seyoung Nam, Yinsheng Kou, Youngwon Kim, Wenjin Zhang

Logo

Background

It is recognized that online learning is becoming increasingly important. Especially during the COVID-19 pandemic, online learning is indispensable in billions of students’ learning journeys. We see that many online learning platforms have emerged, and there are various ways to record how students interact with these platforms in the form of log data. These log data, which cannot be collected in offline classes, should be helpful for instructors and students to understand the learning progress better. However, due to the complexity of the log data, it is challenging to make them interpretable and meaningful for students and instructors.

Project Objective

Our project intends to build a web tool to make log data informative for students and instructors. Using our web tool, students can visualize their learning progress and receive guidance for the course review to boost their final grades. Instructors can better understand their students’ academic performance and easily construct student groups for the group project, which would possibly have a relatively similar academic potential between groups.

Data Source

Educational Process Mining (EPM): A Learning Analytics Data Set

Overview: A group of 115 students of first-year, undergraduate Engineering major at the University of Genoa studied over a simulation environment named Deeds (Digital Electronics Education and Design Suite), and their log activities on the online learning platform, intermediate session grades, and final exam grades are recorded.

Repository Structure

.
├── epm
│   ├── data_prep
│   ├── graph
│   ├── modeling
│   ├── user_db
│   └── tests
├── data
│   ├── Processes
│   ├── all_log.csv
│   ├── final_grades.xlsx
│   ├── logs.txt
│   └── intermediate_grades.xlsx
├── docs
│   └── data_info  
├── Dockerfile
├── LICENSE
├── README.md
├── .streamlit
├── static
├── app.py
└── requirements.txt

Installation

This application is running upon the Docker container. Please download and install Docker from its official website if you have not installed Docker in your operating system. After the installation, please follow the steps below.

1. In your command-line interface, move to the directory Dockerfile is located (root directory of this project).
2. Build the Docker image by entering the following command.

   $ docker build -t epm:latest .

3. Run app.py on the Docker image.

   $ docker run -p 8501:8501 epm:latest

4. To run the application, open your Internet browser and enter localhost:8501.

Project Design

Usage

Requirements

To start, you need to have docker installed. Whatever system you use, follow the tutorial on docker's official website and you can get it done smoothly.

Installation

Clone this repository and run the following code under the repository's root directory:

docker build -t epm:latest .

Then run the following command under the same directory:

docker run -p 8501:8501 epm:latest

Turn to browser and navigate to the URL:localhost:8501

Use Cases

Student Use Case

1. Go to the Log In page, select Student

2. Type your numeric student ID under ID and your password, then click Log In

3. You can now check your log activities across six sessions under the Behavior Analysis section and your grades under the Grades section. You can also get suggestions for the final review under the Review Alert section.

Instructor Use Case

1. Go to the Log In page, select Instructor

2. Type your ID name and Passord, then click Log In

3. You can now check every student's and class average log activities under the Behavior Analysis section and students' and average grades under the Grades section. Under the Grouping Assistant section, we provide you some suggestions for grouping students. We also provide information about who have logged in this website under the User Profiles section.

Feature Request and Bug Report

We want our website to be helpful and informative to both instructors and students. If you would like to see any new features on our website, or if you have any suggestions for improvement or want to report a bug, please feel free to raise a new issue.

Acknowledgements

This is a course project for CSE 583 Software Development for Data Scientists. Many thanks to professor David Beck and our TA, Anant Mittal.