Corpus ID: 49210787

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 Baker and Christopher A. 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… Expand
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References

SHOWING 1-10 OF 30 REFERENCES
Replicating MOOC predictive models at scale
TLDR
This work demonstrates the importance of replication of predictive modeling research in MOOCs using large and diverse datasets, illuminates the challenges of doing so, and describes the freely available, open-source software framework to overcome barriers to replication. Expand
The Need for Open Source Software in Machine Learning
TLDR
It is argued that the situation can be significantly improved by increasing incentives for researchers to publish their software under an open source model, and a resource of peer reviewed software accompanied by short articles would be highly valuable to both the machine learning and the general scientific community. Expand
Reproducibility in Machine Learning-Based Studies: An Example of Text Mining
TLDR
What information about text mining studies is crucial to successful reproduction of such studies is considered, including a set of factors that affect reproducibility based on the experience of attempting to reproduce six studies proposing text mining techniques for the automation of the citation screening stage in the systematic review process. Expand
A Data Repository for the EDM Community: The PSLC DataShop
TLDR
In recent years, educational data mining has emerged as a burgeoning new area for scientific investigation because of the increasing availability of fine-grained, extensive, and longitudinal data on student learning. Expand
Deep Knowledge Tracing
TLDR
The utility of using Recurrent Neural Networks to model student learning and the learned model can be used for intelligent curriculum design and allows straightforward interpretation and discovery of structure in student tasks are explored. Expand
Computing Environments for Reproducibility: Capturing the "Whole Tale"
TLDR
The Whole Tale project aims to address technical and institutional barriers by connecting computational, data-intensive research efforts with the larger research process--transforming the knowledge discovery and dissemination process into one where data products are united with research articles to create "living publications" or "tales". Expand
Best Practices for Computational Science: Software Infrastructure and Environments for Reproducible and Extensible Research
Introduction The goal of this article is to coalesce a discussion around best practices for scholarly research that utilizes computational methods, by providing a formalized set of best practiceExpand
Temporal Models for Predicting Student Dropout in Massive Open Online Courses
  • Mi Fei, D. Yeung
  • Computer Science
  • 2015 IEEE International Conference on Data Mining Workshop (ICDMW)
  • 2015
TLDR
Based on extensive experiments conducted on two MOOCs offered on Coursera and edX, a recurrent neural network (RNN) model with long short-term memory (LSTM) cells beats the baseline methods as well as other proposed methods by a large margin. Expand
Student success prediction in MOOCs
TLDR
This article presents a categorization of MOOC research according to the predictors, prediction, and underlying theoretical model, and critically survey work across each category, providing data on the raw data source, feature engineering, statistical model, evaluation method, prediction architecture, and other aspects of these experiments. Expand
OpenML: A Collaborative Science Platform
TLDR
OpenML is a novel open science platform that provides easy access to machine learning data, software and results to encourage further study and application and features a web API which is being integrated in popular machine learning tools such as Weka, KNIME, RapidMiner and R packages. Expand
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