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An open approach for learning educational data mining

Published: 14 November 2013 Publication History

Abstract

The Open Monitoring Environment (OME) allows a teacher to monitor, model and, thus, understand, the learning process based on the real data rising from an educational robotics class. The OME uses a novel educational data mining approach where teachers are empowered to create rules to extract pedagogically and contextually meaningful patterns of actions from a raw data flow. The OME has been tested in various educational robotics settings and our results indicate that the data mining approach in the OME is easily accessible even for users who are not computer science experts. We propose that the OME could be utilized in computer science education as a platform for empirical, hands-on approach for teaching and learning educational data mining.

References

[1]
Amershi, S., and Conati, C. 2009. Combining Unsupervised and Supervised Classification to Build User Models for Exploratory Learning Environments. Journal of Educational Data Mining 1, 18--71.
[2]
Gobert, J., Sao Pedro, M., Baker, R., Toto, E., and Montalvo, O. 2012. Leveraging Educational Data Mining for Real-time Performance Assessment of Scientific Inquiry Skills within Microworlds. Journal of Educational Data Mining 4, 111--143.
[3]
Kristofic A., and Bieliková, M. 2005. Improving adaptation in web- based educational hypermedia by means of knowledge discovery. Proceedings of the sixteenth ACM conference on Hypertext and hypermedia, HYPERTEXT '05, 184--192.
[4]
Jormanainen, I., and Sutinen, E. 2013. Role blending in a learning environment supports facilitation in a robotics class. Journal of Educational Technology & Society, to appear.

Cited By

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  • (2024)Systematic Review and Analysis of EDM for Predicting the Academic Performance of StudentsJournal of The Institution of Engineers (India): Series B10.1007/s40031-024-00998-0105:4(1021-1071)Online publication date: 4-Feb-2024
  • (2019)Augmented intelligence in educational data miningSmart Learning Environments10.1186/s40561-019-0086-16:1Online publication date: 23-Sep-2019
  • (2019)Collaborative Learning in Data Science Education: A Data Expedition as a Formative Assessment ToolThe Challenges of the Digital Transformation in Education10.1007/978-3-030-11932-4_2(14-25)Online publication date: 16-Mar-2019
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Published In

cover image ACM Other conferences
Koli Calling '13: Proceedings of the 13th Koli Calling International Conference on Computing Education Research
November 2013
204 pages
ISBN:9781450324823
DOI:10.1145/2526968
Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Sponsors

  • Univ. Eastern Finland: University of Eastern Finland
  • The University of Newcastle, Australia
  • Turku University Foundation

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Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 November 2013

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Author Tags

  1. educational data mining
  2. learning analytics
  3. robotics

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  • Research-article

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Koli Calling '13
Sponsor:
  • Univ. Eastern Finland

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Koli Calling '13 Paper Acceptance Rate 20 of 40 submissions, 50%;
Overall Acceptance Rate 80 of 182 submissions, 44%

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Cited By

View all
  • (2024)Systematic Review and Analysis of EDM for Predicting the Academic Performance of StudentsJournal of The Institution of Engineers (India): Series B10.1007/s40031-024-00998-0105:4(1021-1071)Online publication date: 4-Feb-2024
  • (2019)Augmented intelligence in educational data miningSmart Learning Environments10.1186/s40561-019-0086-16:1Online publication date: 23-Sep-2019
  • (2019)Collaborative Learning in Data Science Education: A Data Expedition as a Formative Assessment ToolThe Challenges of the Digital Transformation in Education10.1007/978-3-030-11932-4_2(14-25)Online publication date: 16-Mar-2019
  • (2017)An Exploratory Study on Data Mining in Education: Practiced Algorithms and MethodsInternational Journal of Information and Education Technology10.18178/ijiet.2017.7.5.8887:5(319-323)Online publication date: 2017
  • (2017)Enhancing student learning behaviour using EDM and psychometric analysis2017 6th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)10.1109/ICRITO.2017.8342452(360-363)Online publication date: Sep-2017
  • (2017)Educational data mining and learning analysis2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence10.1109/CONFLUENCE.2017.7943201(491-494)Online publication date: Jan-2017
  • (2016)Principles of Citizen Science in Open Educational Projects Based on Open DataProceedings of the 12th Central and Eastern European Software Engineering Conference in Russia10.1145/3022211.3022216(1-5)Online publication date: 28-Oct-2016
  • (2015)On practice of using open data in construction of training and assessment tasks for programming courses2015 10th International Conference on Computer Science & Education (ICCSE)10.1109/ICCSE.2015.7250248(233-236)Online publication date: Jul-2015

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