Published January 20, 2026 | Version 1.0.1
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Supplementary Materials: Fifteen Years of Learning Analytics Research: Topics, Trends, and Challenges

  • 1. Masaryk University, Faculty of Informatics
  • 2. Carnegie Mellon University, Human-Computer Interaction Institute, School of Computer Science
  • 3. Michigan State University, Department of Counseling, Educational Psychology & Special Education
  • 4. Monash University, Faculty of Information Technology

Description

This repository contains supplementary materials for the following conference full paper:

Valdemar Švábenský, Conrad Borchers, Elvin Fortuna, Elizabeth B. Cloude, and Dragan Gašević.
Fifteen Years of Learning Analytics Research: Topics, Trends, and Challenges.
In Proceedings of the 16th International Learning Analytics and Knowledge Conference (LAK '26).
https://doi.org/10.1145/3785022.3785131

Preprint: https://arxiv.org/pdf/2601.07629

@inproceedings{Svabensky2026fifteen,
    author    = {\v{S}v\'{a}bensk\'{y}, Valdemar and Borchers, Conrad and Fortuna, Elvin and Cloude, Elizabeth B. and Gašević, Dragan},
    title     = {{Fifteen Years of Learning Analytics Research: Topics, Trends, and Challenges}},
    booktitle = {Proceedings of the 16th International Learning Analytics and Knowledge Conference},
    series    = {LAK '26},
    location  = {Bergen, Norway},
    publisher = {Association for Computing Machinery},
    address   = {New York, NY, USA},
    year      = {2026},
    numpages  = {12},
    url       = {https://doi.org/10.1145/3785022.3785131},
    doi       = {10.1145/3785022.3785131},
}

Repository content:

Data, code, and results for the paper. Please see the README.md file in the attached ZIP archive for details.

Attribution (How to cite):

If you use or build upon the materials, please use the BibTeX or full-text citation entry above to cite the source paper.

Acknowledgment:

We thank Xavier Ochoa for compiling the LAK 2011–2019 dataset.

Files

2026-LAK-Svabensky-materials.zip

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Additional details

Related works

Is supplement to
Conference paper: 10.1145/3785022.3785131 (DOI)
Preprint: 10.48550/arXiv.2601.07629 (DOI)

Funding

Czech Science Foundation
Algorithmic Biases in Machine Learning Models in Education 25-15839I
Jacobs Foundation
2023151201
Australian Research Council
DP240100069
Australian Research Council
DP220101209

Software

Programming language
Python , R