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Discovering clues to avoid middle school failure at early stages

Published: 16 March 2015 Publication History

Abstract

The use of data mining techniques in educational domains helps to find new knowledge about how students learn and how to improve the resources management. Using these techniques for predicting school failure is very useful in order to carry out actions to avoid drop out. With this purpose, we try to determine the earliest stage when the quality of the results allows for clarifying the possibility of school failure. We process real information from a Spanish high school by structuring the whole data in incremental datasets, which represent how students' academic records grow. Our study reveals an early and robust detection of the risky cases of school failure at the end of the first out of four courses.

References

[1]
P. Cortez and A. Silva. Using data mining to predict secondary school student performance. In Proceeding of the 15th European Concurrent Engineering Conference/5th Future Business Technology Conference, pages 5--12, Porto, Portugal, 2008.
[2]
J. D. Finn and D. A. Rock. Academic Success Among Students at Risk for School Failure. Journal of Applied Psychology, 82(2): 221--234, 1997.
[3]
S. García, D. Molina, M. Lozano, and F. Herrera. A study on the use of non-parametric tests for analyzing the evolutionary algorithms' behaviour: A case study on the cec'2005 special session on real parameter optimization. Journal of Heuristics, 15(6): 617--644, Dec. 2009.
[4]
H. Gardner. Intelligence Reframed: Multiple Intelligences for the 21st Century. Basic Books, September 2000.
[5]
M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten. The weka data mining software: An update. SIGKDD Explor. Newsl., 11(1): 10--18, Nov. 2009.
[6]
D. Pyle. Data Preparation for Data Mining. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1999.
[7]
C. Romero and S. Ventura. Educational data mining: A review of the state of the art. Transactions on System Man Cybernetics Part C, 40(6): 601--618, 2010.
[8]
C. Romero and S. Ventura. Data mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3(1): 12--27, 2013.

Cited By

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  • (2023)Student performance prediction using datamining classification algorithms: Evaluating generalizability of models from geographical aspectEducation and Information Technologies10.1007/s10639-022-11560-028:11(14167-14185)Online publication date: 1-Nov-2023
  • (2022)Past, present, and future directions of learning analytics research for students with disabilitiesJournal of Research on Technology in Education10.1080/15391523.2022.206779655:6(931-946)Online publication date: 9-May-2022
  • (2021)Student Engagement Patterns in a Blended Learning Environment: an Educational Data Mining ApproachTechTrends10.1007/s11528-021-00638-0Online publication date: 28-Jul-2021
  • Show More Cited By

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Published In

cover image ACM Other conferences
LAK '15: Proceedings of the Fifth International Conference on Learning Analytics And Knowledge
March 2015
448 pages
ISBN:9781450334174
DOI:10.1145/2723576
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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 March 2015

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Author Tags

  1. drop-out
  2. early prediction
  3. educational data mining
  4. school failure

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  • Short-paper

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  • Spanish Ministry of Education

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LAK '15

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LAK '15 Paper Acceptance Rate 20 of 74 submissions, 27%;
Overall Acceptance Rate 236 of 782 submissions, 30%

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Cited By

View all
  • (2023)Student performance prediction using datamining classification algorithms: Evaluating generalizability of models from geographical aspectEducation and Information Technologies10.1007/s10639-022-11560-028:11(14167-14185)Online publication date: 1-Nov-2023
  • (2022)Past, present, and future directions of learning analytics research for students with disabilitiesJournal of Research on Technology in Education10.1080/15391523.2022.206779655:6(931-946)Online publication date: 9-May-2022
  • (2021)Student Engagement Patterns in a Blended Learning Environment: an Educational Data Mining ApproachTechTrends10.1007/s11528-021-00638-0Online publication date: 28-Jul-2021
  • (2018)EDARC: Collaborative Frequent Pattern and Analytical Mining Tool for Exploration of Educational InformationRecent Findings in Intelligent Computing Techniques10.1007/978-981-10-8633-5_26(251-259)Online publication date: 4-Nov-2018
  • (2015)The recent state of educational data mining: A survey and future visions2015 IEEE 3rd International Conference on MOOCs, Innovation and Technology in Education (MITE)10.1109/MITE.2015.7375344(354-359)Online publication date: Oct-2015

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