Document Type
Data
Publication Date
5-2025
Abstract
Personalized Learning Paths (PLP)s are sequences of learning materials and activities that are designed to deliver personalized education to students. Unfortunately, PLPs are often defined and implemented based on faulty cognitive science practices. The PLP research community may benefit from a problem domain and data set that is derived from a scientifically supported cognitive science discipline. This data is published to support the paper, “A Two-Stage Multi-Objective Personalized Learning Path Problem Based on the Cognitive Theory of Multimedia Learning.” This data set includes the data used in this paper, the questionnaire used to gather learner profile data, and the python code that implements the paper’s algorithms. Researchers are free to use this data to support their own work to advance the state of PLPs.
Recommended Citation
Mochocki, S. A., & Reith, M. G. (2025). Data Package Supporting Research on Personalized Learning Path Problem Derived from the Cognitive Theory of Multimedia Learning. Air Force Institute of Technology. https://scholar.afit.edu/facpub/2151
zip file with data, sequencing results,Python code,
Read_Me.txt (8 kB)
Read Me file in plain text
Comments
The "Download" button on this page is only the ReadMe file for the dataset.
Please see the Additional Files area at the bottom of the page for the dataset zip file.
The data in this folder (attachment zip below) supports the paper "A Two-Stage Multi-Objective Personalized Learning Path Problem Based on the Cognitive Theory of Multimedia Learning." The data components are publicly released to facilitate additional research on Personalized Learning Paths and related fields.
Approved for Public Release. PA case number on file.