Generative AI and Knowledge Discovery in Learning Analytics
Start Any Time
Work on your pace and you will have instructors available to help you answer any questions.
Duration
Approximately 3 weeks, 3-4 hours/week
Fee*
$1000 Professional Rate
$400 Full-time Student Rate**
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Certificate Course Description:
This course explores how cutting-edge technologies—Generative AI and advanced data mining methods—are transforming the field of learning analytics. You will begin by examining the challenges and opportunities of working with large-scale educational data, gaining practical experience with techniques such as data preprocessing, feature aggregation, and ensemble modeling (e.g., bagging and boosting) to uncover meaningful patterns and improve model performance.
Building on this foundation, the course will guide you through the role of Generative AI in personalizing learning at scale. You will critically analyze how tools like ChatGPT, Claude, and Gemini can be integrated into learning analytics systems to deliver automated feedback, adaptive support, and individualized learning paths. The course also examines how these technologies are reshaping assessment and evaluation, with discussions on authentic assessment and system-level design.
By the end of the course, you will be equipped to design and critique AI-enhanced learning analytics systems using real-world datasets, either through a hands-on project or a comprehensive final exam.
Module 1: Knowledge Discovery and Data Mining
- Understand and evaluate the challenges and limitations of traditional data analysis methods when applied to large-scale learning analytics datasets
- Explore and compare different methods for handling and processing large datasets in the context of learning analytics
- Investigate the application of ensemble methods (e.g., bagging, boosting) in learning analytics to improve model performance and robustness when dealing with large and complex datasets
Module 2: Generative AI in Learning Analytics
- Critically evaluate the potential and limitations of Generative AI tools (e.g., ChatGPT, Gemini, Claude) for enhancing student learning experiences.
- Analyze how Generative AI can be integrated into learning analytics systems to provide more personalized and insightful feedback to students and educators.
- Explore how Generative AI can be used to personalize learning paths and create adaptive learning environments for individual students.
- Discuss the implications of Generative AI for the future of assessment and evaluation in higher education, including the need for new approaches to authentic assessment.
Module 3: Course Project or Final Exam
You’ll have an opportunity to do a little project where you will solve Colab notebook exercises, one corresponding to every module. That will provide you with a nice experience to apply the fundamentals you will learn in the modules to a larger, more authentic, context. It will be self-graded and you will receive a sample solution.
You will have an alternative option to take a final exam where you will answer 20 questions. The exam can be taken multiple times and each time new questions are randomly selected from a pool of questions.
You are also free to do both the course project and the final exam, we will consider the one in which you score more for counting towards the certificate.
Basic knowledge of Exploratory Data Analysis and Classifiers in Educational Data Science is desired.
No programming prerequisites but experience with a programming language (e.g Python) will be helpful.
Educators, researchers, and data practitioners seeking to apply AI and data science in personalized learning, adaptive tutoring, and large-scale learning analytics.
What you'll learn
This course will help you:
- Investigate and apply ensemble modeling methods (e.g., bagging and boosting) to improve the performance and robustness of learning analytics models
- Critically evaluate the use of Generative AI tools in education, including their potential for automated feedback, adaptive tutoring, and personalized learning
- Explore the implications of Generative AI for the future of educational assessment, including authentic evaluation strategies and system integration
Course Instructors
Dr. Paulo Carvalho
is an assistant professor in the Human-Computer Interaction Institute. His research explores how AI can revolutionize learning through the creation of engaging, practice-first and practice-only environments. Using data analytics and computational modeling, he investigates patterns in student learning, motivation, and interest to develop precise models that enhance educational experiences. His current work examines how generative AI can transform practice-focused approaches, simultaneously boosting student engagement while enabling teachers to provide more personalized support…..
Dr. John Stamper
is an Associate Professor of Human-Computer Interaction at Carnegie Mellon University. Dr. Stamper has a PhD in Computer Science from the University of North Carolina at Charlotte. His main area of research is focused on using “Big Data” from educational systems to improve learning. He is also the lead researcher behind DataShop, which is the largest open repository of log data from learning systems….
Certificate
Upon successful completion of the program, participants will receive a verified digital certificate of completion from Carnegie Mellon University’s Open Learning Initiative.
In addition to the knowledge and immediately applicable frameworks you will gain by attending your selected courses, you will benefit from:
- A digital, verified version of your Executive Certificate (Smart Certificate) you can add to your resume and LinkedIn
- Networking with a global group of your peers and instructors for advancing your career
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