skip to main content
10.1145/3144826.3145396acmotherconferencesArticle/Chapter ViewAbstractPublication PagesteemConference Proceedingsconference-collections
research-article

edX-MAS: Model Analyzer System

Authors Info & Claims
Published:18 October 2017Publication 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.

References

  1. C. Delgado Kloos, C. Alario-Hoyos, C. Fernández-Panadero, I. Estévez-Ayres, P. Muñoz-Merino, R. Cobos, J. Moreno, E. Tovar, R. Cabedo, N. Piedra, J. Chicaiza, and J. López. Proyecto eMadrid: MOOCs y Analítica del Aprendizaje. SIIE16, CEDI2016. Salamanca, Castilla y León, Spain.Google ScholarGoogle Scholar
  2. R. Cobos, A. Wilde, and Zaluska. Predicting attrition from Massive Open Online Courses in FutureLearn and edX. Comparing attrition prediction in FutureLearn and edX MOOCs. Proceedings of the LAK FutureLearn Worshop in the Learning Analytics and Knowledge 2017 Conference (LAK17), Canada.Google ScholarGoogle Scholar
  3. R. Cobos, S. Gil, A. Lareo, and F.A. Vargas. Open-DLAs: An Open Dashboard for Learning Analytics. LS 2016 Third (2016) ACM Conference on Learning Scale Edinburgh, Scotland Uk, pages 265--268. (Apr. 2016) Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. M.Leon, R. Cobos, K. Dickens, Su. White, and H. Davis. Visualising the MOOC experience: a dynamic MOOC dashboard built through institutional collaboration. eMOOCs 2016. 4th European MOOCs Stakeholders Summit. Pages 461--470, (Feb. 2016).Google ScholarGoogle Scholar
  5. L.A. Ruipérez Valiente, R. Cobos, P.J. Muñoz-Merino, A.l Andújar, and C. Delgado Kloos. Early Prediction and Variable Importance of Certificate Accomplishment. European MOOC Stakeholder Summit 2017 (eMOOCs 2017). Leganés, Madrid, Spain. (May 2017)Google ScholarGoogle Scholar
  6. V. Macías. Herramienta para el modelado predictivo en entornos educativos en línea.Universidad Autónoma de Madrid. Spain. Bachelor dissertation. (June 2017)Google ScholarGoogle Scholar
  7. I.D. Claros, R. Cobos, G. Sandoval and M. Villanueva. Creating MOOCs by UAMx: experiences and expectations. European MOOC Stakeholder Summit 2015. Mons, BelgiumGoogle ScholarGoogle Scholar
  8. M Leon, R. Cobos, and K. Dickens. Internal Perspectives of MOOCs in Universities. European MOOC Stakeholder Summit 2017 (eMOOCs 2017). Leganés, Madrid, Spain.Google ScholarGoogle Scholar
  9. J. Friedman, T. Hastie and R. Tibshirani. Additive logistic regression: a statistical view of boosting. Annals of Statistics 28(2), 2000. 337--407Google ScholarGoogle Scholar
  10. Y. Freund and R. E. Schapire. 1997. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1):119--139. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. J. Friedman. 2001. Greedy Function Approximation: A Gradient Boosting Machine. The Annals of Statistics, Vol. 29, No. 5 (Oct., 2001), pp. 1189--1232.Google ScholarGoogle ScholarCross RefCross Ref
  12. T. Chen, T. He and M. Benesty. 2016. Package 'xgboost': Extreme Gradient Boosting. Documentation available in https://cran.r-project.org/web/ packages/xgboost/xgboost.pdfGoogle ScholarGoogle Scholar
  13. C. Cortes, V. Vapnik. (1995). "Support-vector networks". Machine Learning. 20 (3): 273--297 Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. J.M. Keller, M.R. Gray and J.A. Givens. 1985. A fuzzy k-Nearest Neighbour algorithm. IEEE Transactions on Systems, Man and Cybernetics 15(4) 580--585Google ScholarGoogle ScholarCross RefCross Ref

Index Terms

  1. edX-MAS: Model Analyzer System

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in
        • Published in

          cover image ACM Other conferences
          TEEM 2017: Proceedings of the 5th International Conference on Technological Ecosystems for Enhancing Multiculturality
          October 2017
          723 pages
          ISBN:9781450353861
          DOI:10.1145/3144826

          Copyright © 2017 ACM

          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]

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 18 October 2017

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • research-article
          • Research
          • Refereed limited

          Acceptance Rates

          TEEM 2017 Paper Acceptance Rate84of109submissions,77%Overall Acceptance Rate496of705submissions,70%

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader