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
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10
Y1 - 2018/10
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.
T2 - 13th Latin American Conference on Learning Technologies, LACLO 2018
Y2 - 1 October 2018 through 5 October 2018
ER -