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Learn Explainable AI by Analyzing Student Performance

BeginnerGuided Project

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.

5.0 (27 Reviews)

Language

  • English

Topic

  • Skills Network

Skills You Will Learn

  • Explainable AI, Python, Machine Learning, Artificial Intelligence

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 45 minutes

Platform

  • SkillsNetwork

Last Update

  • August 7, 2025
About this Guided Project
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.

Instructors

Karan Goswami

Data Scientist

I am a dedicated Data Scientist and an AI enthusiast, currently working at IBM's Skills Builder Network. Learning how some simple mathematical operations could be used to make predictions and discover patterns sparked my curiosity, leading me to explore the exciting world of AI. Over the years, I’ve gained hands-on experience in building scalable AI solutions, fine-tuning models, and extracting meaningful insights from complex datasets. I'm driven by a desire to apply these skills to solve real-world problems and make a meaningful impact through AI.

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Contributors

Wojciech "Victor" Fulmyk

Data Scientist at IBM

As a data scientist at the Ecosystems Skills Network at IBM and a Ph.D. candidate in Economics at the University of Calgary, I bring a wealth of experience in unraveling complex problems through the lens of data. What sets me apart is my ability to seamlessly merge technical expertise with effective communication, translating intricate data findings into actionable insights for stakeholders at all levels. Follow my projects to learn data science principles, machine learning algorithms, and artificial intelligence agent implementations.

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