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 R. 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…
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References
SHOWING 1-10 OF 30 REFERENCES
Replicating MOOC predictive models at scale
- Computer Science, PsychologyL@S
- 2018
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.
The Need for Open Source Software in Machine Learning
- Computer ScienceJ. Mach. Learn. Res.
- 2007
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.
Reproducibility in Machine Learning-Based Studies: An Example of Text Mining
- Computer Science
- 2017
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.
A Data Repository for the EDM Community: The PSLC DataShop
- Education
- 2010
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.
Deep Knowledge Tracing
- Computer ScienceNIPS
- 2015
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.
Computing Environments for Reproducibility: Capturing the "Whole Tale"
- Computer ScienceFuture Gener. Comput. Syst.
- 2019
Temporal Models for Predicting Student Dropout in Massive Open Online Courses
- Computer Science2015 IEEE International Conference on Data Mining Workshop (ICDMW)
- 2015
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.
Student success prediction in MOOCs
- PsychologyUser Modeling and User-Adapted Interaction
- 2018
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.
OpenML: A Collaborative Science Platform
- Computer ScienceECML/PKDD
- 2013
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.
An introduction to Docker for reproducible research
- Computer ScienceOPSR
- 2015
How the popular emerging technology Docker combines several areas from systems research - such as operating system virtualization, cross-platform portability, modular re-usable elements, versioning, and a 'DevOps' philosophy, to address these challenges is examined.