A FRAMEWORK FOR PERSONALIZED LEARNING PATHS IN CALCULUS: A NETWORK SCIENCE APPROACH
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Authors
Herrero, Gordon B.
Subjects
mathematics education
graph theory
personalized adaptive learning
personalized learning path
Network of Knowledge
network science
graph theory
personalized adaptive learning
personalized learning path
Network of Knowledge
network science
Advisors
Gera, Ralucca
Date of Issue
2025-03
Date
Publisher
Monterey, CA; Naval Postgraduate School
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Abstract
Returning students—those with undergraduate degrees who return to graduate school after a break from academia for five years or more—bring vast real-world experience to graduate degree programs but often have different needs than direct-pathway students—those who proceed from undergraduate programs directly into graduate school. This research creates a framework to develop personalized learning paths (PLPs) to support returning students reviewing calculus by modeling a Network of Knowledge based on the textbook Calculus by Frank Morgan. We implement the framework using two algorithms to produce PLPs unique to each returning student based on available time, prerequisite knowledge, and importance of topics. The framework can be extended to other textbooks, and the application can assist both a student attempting to self-study as well as an educator looking to develop or revise the curriculum for a review course. This approach can be further applied to the broader field of emerging adaptive learning technologies in other disciplines.
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Distribution Statement
Distribution Statement A. Approved for public release: Distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.