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edX-MAS: Model Analyzer System

Published: 18 October 2017 Publication History

Abstract

In this article an interactive tool for supporting the generation of Predictive Models for edX MOOCs is presented. This tool is a modular and scalable application that, based on the data collected from a MOOC course, allows the simple application of a complete Data Science process, where Machine Learning algorithms are applied to generate predictive models which could be used to predict which learners have passed the course and therefore obtained a certificate. The presented tool is called "edX-MAS" and it helps the user to analyze and compare the prediction quality of different Machine Learning algorithms, allowing the possibility of exporting the results in order to do a more exhaustive analysis.

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Cited By

View all
  • (2022)Towards a better understanding of the role of visualization in online learning: A reviewVisual Informatics10.1016/j.visinf.2022.09.0026:4(22-33)Online publication date: Dec-2022
  • (2020)An Algorithm and a Tool for the Automatic Grading of MOOC Learners from Their Contributions in the Discussion ForumApplied Sciences10.3390/app1101009511:1(95)Online publication date: 24-Dec-2020
  • (2019)Prediction in MOOCs: A Review and Future Research DirectionsIEEE Transactions on Learning Technologies10.1109/TLT.2018.285680812:3(384-401)Online publication date: 1-Jul-2019

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Published In

TEEM 2017: Proceedings of the 5th International Conference on Technological Ecosystems for Enhancing Multiculturality
October 2017
723 pages
ISBN:9781450353861
DOI:10.1145/3144826
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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  • University of Salamanca: University of Salamanca

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 October 2017

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Author Tags

  1. Data Science
  2. Learning Analytics
  3. MOOC
  4. data analysis
  5. machine learning
  6. predictive model

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  • Research-article
  • Research
  • Refereed limited

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TEEM 2017

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TEEM 2017 Paper Acceptance Rate 84 of 109 submissions, 77%;
Overall Acceptance Rate 496 of 705 submissions, 70%

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Cited By

View all
  • (2022)Towards a better understanding of the role of visualization in online learning: A reviewVisual Informatics10.1016/j.visinf.2022.09.0026:4(22-33)Online publication date: Dec-2022
  • (2020)An Algorithm and a Tool for the Automatic Grading of MOOC Learners from Their Contributions in the Discussion ForumApplied Sciences10.3390/app1101009511:1(95)Online publication date: 24-Dec-2020
  • (2019)Prediction in MOOCs: A Review and Future Research DirectionsIEEE Transactions on Learning Technologies10.1109/TLT.2018.285680812:3(384-401)Online publication date: 1-Jul-2019

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