The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
Temporal predication of dropouts in MOOCs: Reaching the low hanging fruit through stacking generalization
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Citations
Cites
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Jolliffe, I. (2005) · Encyclopedia of Statistics in Behavioral Science · doi:10.1002/0470013192.bsa501 · Cited by 14,310
Wolpert, D. H. (1992) · Neural Networks · doi:10.1016/s0893-6080(05)80023-1 · Cited by 7,951
The use of the area under the ROC curve in the evaluation of machine learning algorithms
Bradley, A. P. (1997) · Pattern Recognition · doi:10.1016/s0031-3203(96)00142-2 · Cited by 7,217
Chiang, L. H., Russell, E. L., & Braatz, R. D. (2001) · Advanced Textbooks in Control and Signal Processing · doi:10.1007/978-1-4471-0347-9_4 · Cited by 6,594
Ensemble Methods: Foundations and Algorithms
Zhou, Z.-H. (2012) · doi:10.1201/b12207 · Cited by 2,534
Educational Data Mining: A review of the state of the art
Romero, C., & Ventura, S. (2010) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/tsmcc.2010.2053532 · Cited by 2,251
Ensemble Methods: Foundations and Algorithms
Zhou, Z. (2012) · Cited by 2,051
Learning analytics and educational data mining: towards communication and collaboration
Siemens, G., & Baker, R. S. J. D. (2012) · Proceedings of the 2nd International Conference on Learning Analytics and Knowledge · doi:10.1145/2330601.2330661 · Cited by 1,075
Measuring classifier performance: a coherent alternative to the area under the ROC curve
Hand, D. J. (2009) · Machine Learning · doi:10.1007/s10994-009-5119-5 · Cited by 1,003
Cited by
Ouyang, F., Zheng, L., & Jiao, P. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-10925-9 · Cited by 831
Predicting academic performance of students from VLE big data using deep learning models
Waheed, H., Hassan, S.-U., Aljohani, N. R., Hardman, J., Alelyani, S., & Nawaz, R. (2020) · Computers in Human Behavior · doi:10.1016/j.chb.2019.106189 · Cited by 573
Natural Language Generation Using Deep Learning to Support MOOC Learners
Li, C., & Xing, W. (2021) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-020-00235-x · Cited by 133
Self-citationAn early feedback prediction system for learners at-risk within a first-year higher education course
Baneres, D., Rodriguez, M. E., & Serra, M. (2019) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2019.2912167 · Cited by 128
Coussement, K., Phan, M., De Caigny, A., Benoit, D. F., & Raes, A. (2020) · Decision Support Systems · doi:10.1016/j.dss.2020.113325 · Cited by 125
Metadata
- Authors
- Xing, W., Chen, X., Stein, J., & Marcinkowski, M.
- Venue
- Computers in Human Behavior
- Publisher
- Elsevier BV
- Published
- 2016-05
- Volume
- 58
- Pages
- 119-129
- ISSN
- 0747-5632 (ISSN-L)
- OpenAlex
- W2414670006
- Cited by
- 256
- References
- 43
- FWCI
- 48.71
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Temporal predication of dropouts in MOOCs: Reaching the low hanging fruit through stacking generalization. https://app.cochat.ai/papers/10.1016/j.chb.2015.12.007
- The paper
- Xing, W., Chen, X., Stein, J., & Marcinkowski, M. (2016). Temporal predication of dropouts in MOOCs: Reaching the low hanging fruit through stacking generalization. Computers in Human Behavior. https://doi.org/10.1016/j.chb.2015.12.007
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
