At a Glance

Name: Learn Explainable AI by Analyzing Student Performance
Price: Free CAD
Rating: 0.0 (0 reviews)
Author: karan_goswami, wojciech_fulmyk

Uncover hidden patterns in student success using Explainable AI (XAI) with IBM’s AI Explainability 360 (AIX360). You’ll step into the role of an educator trying to uncover why some students thrive while others struggle. Using Protodash Explainer, you’ll identify key student profiles and analyze patterns in study habits, demographics, and performance trends. Starting with data preprocessing, you’ll build AI models, apply PCA for visualization, and leverage Explainable AI (XAI) techniques to make AI-driven insights transparent and actionable. Perfect for data scientists and AI enthusiasts.

Imagine you’re an educator with a classroom full of students, each with their own unique learning journey. One student stands out—perhaps they excel in some subjects but struggle in others. You start to wonder: Are there students with similar backgrounds and study habits? What factors contribute to their success or challenges? Traditional AI models can predict outcomes, but they rarely explain why students succeed or struggle. This is where Explainable AI (XAI) comes in.

In this hands-on project, you will explore IBM’s AI Explainability 360 (AIX360) toolkit to develop clear, actionable explanations for student outcomes. With the Protodash Explainer, you’ll identify representative student profiles and uncover key academic success factors. By leveraging transparent AI models, you’ll analyze relationships between study habits, demographics, and performance trends, making AI insights more interpretable.

Understanding why a student is at risk is just as important as predicting their performance. This project highlights how explainability in AI empowers educators to design better learning strategies, create targeted interventions, and support students more effectively.

A Look at the Project Ahead

In this project, you’ll work with a real-world student dataset to explore factors influencing academic performance using transparent AI models. Learn how to preprocess data, apply interpretability techniques, and evaluate predictions using tools like the Protodash Explainer and PCA.

By the end of the project, you will:

Preprocess and encode datasets for AI-driven student performance analysis.
Build and train interpretable models using Random Forest Classifiers and XAI tools like the Protodash Explainer.
Identify similar student profiles using Protodash to understand key success factors and group dynamics.
Visualize relationships between students and prototypes using PCA for dimensionality reduction.
Evaluate predictions and explanations to ensure the reliability and transparency of your models.

What You'll Need

To successfully complete this project, you’ll need:

A foundational understanding of Python programming and libraries such as pandas, scikit-learn, and matplotlib.
Basic knowledge of AI and machine learning concepts, especially classification tasks.
A web browser to access tools and run your code.

By the end of this project, you will have built an AI model that not only predicts student performance but also explains the reasoning behind each prediction, allowing educators to take meaningful action based on data-driven insights.

Offered By: IBMSkillsNetwork

Learn Explainable AI by Analyzing Student Performance

At a Glance

A Look at the Project Ahead

What You'll Need

This Guided Project is part of the following Learning Paths