• Computer Science, Mathematics
  • Published in ArXiv 2018

Enabling End-To-End Machine Learning Replicability: A Case Study in Educational Data Mining

@article{Gardner2018EnablingEM,
  title={Enabling End-To-End Machine Learning Replicability: A Case Study in Educational Data Mining},
  author={Josh Gardner and Yuming Yang and Ryan Shaun Joazeiro de Baker and Christopher Brooks},
  journal={ArXiv},
  year={2018},
  volume={abs/1806.05208}
}
The use of machine learning techniques has expanded in education research, driven by the rich data from digital learning environments and institutional data warehouses. However, replication of machine learned models in the domain of the learning sciences is particularly challenging due to a confluence of experimental, methodological, and data barriers. We discuss the challenges of end-to-end machine learning replication in this context, and present an open-source software toolkit, the MOOC… CONTINUE READING
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References

Publications referenced by this paper.
SHOWING 1-10 OF 28 REFERENCES

Deep Reinforcement Learning that Matters

VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Kjensmo. State of the art: Reproducibility in artificial intelligence

  • Odd Erik Gundersen, Sigbjørn
  • In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence and the Twenty-Eighth Innovative Applications of Artificial Intelligence Conference,
  • 2017
VIEW 5 EXCERPTS
HIGHLY INFLUENTIAL

The Practice of Reproducible Research: Case Studies and Lessons from the Data-Intensive Sciences

  • Justin Kitzes, Daniel Turek, Fatma Deniz
  • 2017
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Computing Environments for Reproducibility: Capturing the "Whole Tale"

VIEW 3 EXCERPTS
HIGHLY INFLUENTIAL

Student success prediction in MOOCs

VIEW 1 EXCERPT

50 Years of Data Science

VIEW 2 EXCERPTS