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4230. Enhancing Business Education through Bayesian Knowledge Tracing: A Framework for Personalized Learning
Invited abstract in session WB-31: Learning Analytics and other Text Analytics tasks, stream Analytics.
Wednesday, 10:30-12:00Room: 046 (building: 208)
Authors (first author is the speaker)
| 1. | Nicolas Nunez
|
| Centrum PUCP, PUCP |
Abstract
Our research addresses the limited use of Learning Analytics tools and techniques in providing personalized learning experiences for MBA students. We propose a novel framework that incorporates Bayesian Knowledge Tracing (BKT) and other predictive modeling techniques to create customized learning tracks for MBA programs. Our approach leverages data from standardized admission tests and student performance to generate tailored recommendations, addressing a significant gap in the application of learning analytics in graduate business education.
Preliminary results, based on a small sample, indicate that the use of BKT can improve the learning experience for MBA students. By developing a clear framework for implementing BKT and other predictive modeling techniques, this research aims to provide academic directors, deans, and key decision-makers in graduate business schools with a practical tool for enhancing personalized learning in their programs.
The proposed framework has the potential to be adapted for a broader audience beyond MBA programs, contributing to the advancement of personalized learning in various educational contexts. This research aligns with the growing need for data-driven approaches to optimize learning outcomes and student success in higher education.
Keywords
- Analytics and Data Science
- Decision Support Systems
- Developing Countries
Status: accepted
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