Using data mining techniques to follow students trajectories in secondary schools of Uruguay

Luiz Antonio MacArini, Cristian Cechinel, Henrique Lemos Dos Santos, Xavier Ochoa, Virginia Rodes, Guillermo Ettlin Alonso, Alen Perez Casas

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

It is possible to observe an enormous increase on the number of researches focused on automatically find patterns and factors that affect students behavior and performance during their learning process. The fields of Learning Analytics and Educational Data Mining are in constant growing, developing new and innovative tools. Furthermore, new methodologies are being created to follow and help students and professors inside the many different types of educational settings. At the same time, it is also possible to see that the majority of the existing works are still restricted to small and controlled experiments, conducted on samples of students data. The present work describes the first step of an international collaboration focused on implementing Learning Analytics on a national scale. Precisely, this work describes the methodology applied to find rules that can be used to follow students' trajectories in secondary schools in Uruguay. The results points out for the possibility of delivering rules by analyzing patterns of students clusters based on their success (or failure) in the school year. Among other findings, this work shows a strong relationship between students grades and their number of absences in the classes.

Original languageEnglish (US)
Title of host publicationProceedings - 13th Latin American Conference on Learning Technologies, LACLO 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages307-314
Number of pages8
ISBN (Electronic)9781728103822
DOIs
StatePublished - Oct 1 2018
Event13th Latin American Conference on Learning Technologies, LACLO 2018 - Sao Paulo, Brazil
Duration: Oct 1 2018Oct 5 2018

Publication series

NameProceedings - 13th Latin American Conference on Learning Technologies, LACLO 2018

Conference

Conference13th Latin American Conference on Learning Technologies, LACLO 2018
CountryBrazil
CitySao Paulo
Period10/1/1810/5/18

Fingerprint

Uruguay
Data mining
secondary school
Trajectories
Students
student
methodology
educational setting
learning
learning process
university teacher
experiment
school
performance
Experiments

Keywords

  • At risk students
  • Clustering
  • Educational data mining
  • Learning analytics
  • Rules

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Computer Networks and Communications
  • Human-Computer Interaction
  • Education

Cite this

MacArini, L. A., Cechinel, C., Dos Santos, H. L., Ochoa, X., Rodes, V., Alonso, G. E., & Casas, A. P. (2018). Using data mining techniques to follow students trajectories in secondary schools of Uruguay. In Proceedings - 13th Latin American Conference on Learning Technologies, LACLO 2018 (pp. 307-314). [8783723] (Proceedings - 13th Latin American Conference on Learning Technologies, LACLO 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/LACLO.2018.00061

Using data mining techniques to follow students trajectories in secondary schools of Uruguay. / MacArini, Luiz Antonio; Cechinel, Cristian; Dos Santos, Henrique Lemos; Ochoa, Xavier; Rodes, Virginia; Alonso, Guillermo Ettlin; Casas, Alen Perez.

Proceedings - 13th Latin American Conference on Learning Technologies, LACLO 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 307-314 8783723 (Proceedings - 13th Latin American Conference on Learning Technologies, LACLO 2018).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

MacArini, LA, Cechinel, C, Dos Santos, HL, Ochoa, X, Rodes, V, Alonso, GE & Casas, AP 2018, Using data mining techniques to follow students trajectories in secondary schools of Uruguay. in Proceedings - 13th Latin American Conference on Learning Technologies, LACLO 2018., 8783723, Proceedings - 13th Latin American Conference on Learning Technologies, LACLO 2018, Institute of Electrical and Electronics Engineers Inc., pp. 307-314, 13th Latin American Conference on Learning Technologies, LACLO 2018, Sao Paulo, Brazil, 10/1/18. https://doi.org/10.1109/LACLO.2018.00061
MacArini LA, Cechinel C, Dos Santos HL, Ochoa X, Rodes V, Alonso GE et al. Using data mining techniques to follow students trajectories in secondary schools of Uruguay. In Proceedings - 13th Latin American Conference on Learning Technologies, LACLO 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 307-314. 8783723. (Proceedings - 13th Latin American Conference on Learning Technologies, LACLO 2018). https://doi.org/10.1109/LACLO.2018.00061
MacArini, Luiz Antonio ; Cechinel, Cristian ; Dos Santos, Henrique Lemos ; Ochoa, Xavier ; Rodes, Virginia ; Alonso, Guillermo Ettlin ; Casas, Alen Perez. / Using data mining techniques to follow students trajectories in secondary schools of Uruguay. Proceedings - 13th Latin American Conference on Learning Technologies, LACLO 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 307-314 (Proceedings - 13th Latin American Conference on Learning Technologies, LACLO 2018).
@inproceedings{1670530c7333449cbb430301badfe635,
title = "Using data mining techniques to follow students trajectories in secondary schools of Uruguay",
abstract = "It is possible to observe an enormous increase on the number of researches focused on automatically find patterns and factors that affect students behavior and performance during their learning process. The fields of Learning Analytics and Educational Data Mining are in constant growing, developing new and innovative tools. Furthermore, new methodologies are being created to follow and help students and professors inside the many different types of educational settings. At the same time, it is also possible to see that the majority of the existing works are still restricted to small and controlled experiments, conducted on samples of students data. The present work describes the first step of an international collaboration focused on implementing Learning Analytics on a national scale. Precisely, this work describes the methodology applied to find rules that can be used to follow students' trajectories in secondary schools in Uruguay. The results points out for the possibility of delivering rules by analyzing patterns of students clusters based on their success (or failure) in the school year. Among other findings, this work shows a strong relationship between students grades and their number of absences in the classes.",
keywords = "At risk students, Clustering, Educational data mining, Learning analytics, Rules",
author = "MacArini, {Luiz Antonio} and Cristian Cechinel and {Dos Santos}, {Henrique Lemos} and Xavier Ochoa and Virginia Rodes and Alonso, {Guillermo Ettlin} and Casas, {Alen Perez}",
year = "2018",
month = "10",
day = "1",
doi = "10.1109/LACLO.2018.00061",
language = "English (US)",
series = "Proceedings - 13th Latin American Conference on Learning Technologies, LACLO 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "307--314",
booktitle = "Proceedings - 13th Latin American Conference on Learning Technologies, LACLO 2018",

}

TY - GEN

T1 - Using data mining techniques to follow students trajectories in secondary schools of Uruguay

AU - MacArini, Luiz Antonio

AU - Cechinel, Cristian

AU - Dos Santos, Henrique Lemos

AU - Ochoa, Xavier

AU - Rodes, Virginia

AU - Alonso, Guillermo Ettlin

AU - Casas, Alen Perez

PY - 2018/10/1

Y1 - 2018/10/1

N2 - It is possible to observe an enormous increase on the number of researches focused on automatically find patterns and factors that affect students behavior and performance during their learning process. The fields of Learning Analytics and Educational Data Mining are in constant growing, developing new and innovative tools. Furthermore, new methodologies are being created to follow and help students and professors inside the many different types of educational settings. At the same time, it is also possible to see that the majority of the existing works are still restricted to small and controlled experiments, conducted on samples of students data. The present work describes the first step of an international collaboration focused on implementing Learning Analytics on a national scale. Precisely, this work describes the methodology applied to find rules that can be used to follow students' trajectories in secondary schools in Uruguay. The results points out for the possibility of delivering rules by analyzing patterns of students clusters based on their success (or failure) in the school year. Among other findings, this work shows a strong relationship between students grades and their number of absences in the classes.

AB - It is possible to observe an enormous increase on the number of researches focused on automatically find patterns and factors that affect students behavior and performance during their learning process. The fields of Learning Analytics and Educational Data Mining are in constant growing, developing new and innovative tools. Furthermore, new methodologies are being created to follow and help students and professors inside the many different types of educational settings. At the same time, it is also possible to see that the majority of the existing works are still restricted to small and controlled experiments, conducted on samples of students data. The present work describes the first step of an international collaboration focused on implementing Learning Analytics on a national scale. Precisely, this work describes the methodology applied to find rules that can be used to follow students' trajectories in secondary schools in Uruguay. The results points out for the possibility of delivering rules by analyzing patterns of students clusters based on their success (or failure) in the school year. Among other findings, this work shows a strong relationship between students grades and their number of absences in the classes.

KW - At risk students

KW - Clustering

KW - Educational data mining

KW - Learning analytics

KW - Rules

UR - http://www.scopus.com/inward/record.url?scp=85062790313&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85062790313&partnerID=8YFLogxK

U2 - 10.1109/LACLO.2018.00061

DO - 10.1109/LACLO.2018.00061

M3 - Conference contribution

AN - SCOPUS:85062790313

T3 - Proceedings - 13th Latin American Conference on Learning Technologies, LACLO 2018

SP - 307

EP - 314

BT - Proceedings - 13th Latin American Conference on Learning Technologies, LACLO 2018

PB - Institute of Electrical and Electronics Engineers Inc.

ER -