MOOC Student Action Dataset
A MOOC online learning platform dataset of 7,047 students, 97 course content items, and 411,749 student actions, collected by Kumar, Zhang & Leskovec via SNAP (2019). Each record contains action ID, student user ID, course content item ID, relative timestamp, dropout label (0/1), and 4 pre-computed action feature dimensions. Bipartite temporal network of student interactions with MOOC course content — 1.0% dropout rate across 396,633 actions. Includes pre-computed embedding features per action, enabling direct use in temporal graph learning without feature engineering.. Suitable for student dropout prediction, temporal interaction networks, dynamic graph embedding, online learning behavior analysis.
Cite this dataset at
act-mooc.echo.directory
Analytics
The full graph has 7,144 nodes; this view shows the 1,000-node sample the pipeline drew for display. Node size is centrality, colour is community.
Computed over the full corpus.
Record
- Catalog key
act-mooc- Authors
- Kumar, Srijan; Zhang, Xikun; Leskovec, Jure
- Year
- 2019
- Source paper
- Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks
- DOI / arXiv
- 10.1145/3292500.3330895
- License
- Academic use (SNAP — Stanford Network Analysis Project) (LicenseRef-Research-Only)
- Records
- 7,144
- Nodes
- 7,144
- Edges
- 178,443 (directed)
- Nodes are
- user / course content
- Edges are
- student action
- Attributes
- nodes have no attributes; edges have no attributes
- Languages
- en
- Collected
- 2013–2014 (2010s)
- Tasks
- Node classification (binary;
dropout_label) inferred
From the graph: link prediction, community detection
Provenance
- Originally published at
- https://snap.stanford.edu/data/act-mooc.html link checked
- Hosted copy
- Mirrored on echo.directory on 2026-10-09, 47.8 MB across 3 files. Checksums in MANIFEST.json.
Citations
Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks
Srijan Kumar, Xikun Zhang, J. Leskovec
Journal Article · Knowledge Discovery and Data Mining · 2019
Cited by
- Graph neural networks for financial fraud detection: a review
Dawei Cheng, Yao Zou et al. · Frontiers of Computer Science · 2024 · cited 179× - A Label-Free Heterophily-Guided Approach for Unsupervised Graph Fraud Detection
Jun-Jun Pan, Yi-Xin Liu et al. · AAAI Conference on Artificial Intelligence · 2025 · cited 52× - TagRec: Temporal-Aware Graph Contrastive Learning With Theoretical Augmentation for Sequential Recommendation
Tianhao Peng, Hai-Tao Yuan et al. · IEEE Transactions on Knowledge and Data Engineering · 2025 · cited 47× - AnomalyGFM: Graph Foundation Model for Zero/Few-shot Anomaly Detection
He-Zhe Qiao, Chao-Xi Niu et al. · Knowledge Discovery and Data Mining · 2025 · cited 41× - Deep Temporal Graph Clustering: A Comprehensive Benchmark and Datasets
Meng Liu, K. Liang et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence · 2025 · cited 38×
Show all 10
- Zero-shot Generalist Graph Anomaly Detection with Unified Neighborhood Prompts
Chao-Xi Niu, He-Zhe Qiao et al. · International Joint Conference on Artificial Intelligence · 2024 · cited 37× - Improving Temporal Link Prediction via Temporal Walk Matrix Projection
Xiao-Dong Lu, Lei-Lei Sun et al. · Neural Information Processing Systems · 2024 · cited 37× - Rethinking multi-level information fusion in temporal graphs: Pre-training then distilling for better embedding
Meng Liu, Yong Liu et al. · Information Fusion · 2025 · cited 27× - DG-Mamba: Robust and Efficient Dynamic Graph Structure Learning with Selective State Space Models
Haonan Yuan, Qingyun Sun et al. · AAAI Conference on Artificial Intelligence · 2024 · cited 25× - DiffGAD: A Diffusion-based Unsupervised Graph Anomaly Detector
Jinghan Li, Yuan Gao et al. · International Conference on Learning Representations · 2024 · cited 24×
Citation data from Semantic Scholar, retrieved 2026-10-19.
How to cite
BibTeX
@misc{echo2026,
title = {ECHO: Exploratory Compendium of Humanity Online},
author = {Switzer, Braden and Zafarani, Reza},
year = {2026},
url = {https://echo.directory},
note = {Syracuse University}
}
@inproceedings{kumar2019jodie,
title = {Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks},
author = {Kumar, Srijan and Zhang, Xikun and Leskovec, Jure},
booktitle = {Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1269--1278},
year = {2019},
doi = {10.1145/3292500.3330895}
} APA
Switzer, B., & Zafarani, R. (2026). ECHO: Exploratory Compendium of Humanity Online [Data repository]. Syracuse University. https://echo.directory
Kumar, S., Zhang, X., & Leskovec, J. (2019). MOOC Student Action Dataset [Data set]. https://doi.org/10.1145/3292500.3330895
MLA
Switzer, Braden, and Reza Zafarani. ECHO: Exploratory Compendium of Humanity Online. Syracuse University, 2026, echo.directory.
Kumar, Srijan, et al. MOOC Student Action Dataset. 2019, doi.org/10.1145/3292500.3330895.