Document Type
Data
Publication Date
3-24-2024
Abstract
Personalized Learning Paths (PLPs) are a key application of Artificial Intelligence in E-Learning. In contrast to regular Learning Paths, they return a unique sequence of learning materials identified as meeting the individual needs of the students. In the literature, PLPs are often created from knowledge graphs, which assist with ordering topics and their associated learning materials. Knowledge graphs are typically directed and acyclic, to capture prerequisite relationships between topics, though they can also have bidirectional edges when these prerequisite relationships are not necessary. This data package provides a primarily un-directed knowledge graph, with associated repository of open-source learning materials that provide AI education, along with student profile data. These data are intended to support the automatic creation of PLPs for students and to enable the comparison of PLP design approaches. This technical data package is associated with the paper listed below.
Recommended Citation
Mochocki, S., & Reith, M. G. (2024). Data Supporting Research on Personalized Learning Paths. AFIT Scholar. https://scholar.afit.edu/facpub/1489
PLP dataset zip file 2024-03
Included in
Artificial Intelligence and Robotics Commons, Educational Assessment, Evaluation, and Research Commons, Educational Technology Commons
Comments
Please note:
The "Download" button on this page is only a placeholder file. The full dataset is available as a zip file by clicking the link under "Additional Files"
Associated paper: Mochocki, S., Reith, M., Borghetti, B., Peterson, G.L., Jasper J. and Merkle, L. “Computational Complexity and AI Approaches for the Personalized Learning Path Problem.” [submitted to IEEE Transactions on Artificial Intelligence].
Data zip file package cleared for public release, 88ABW-2024-0381 ; Abstract cleared for public release, 88ABW-2024-0382