TY - GEN
T1 - Using multimodal data to find patterns in student presentations
AU - Vieira Roque, Felipe
AU - Cechinel, Cristian
AU - Merino, Erick
AU - Villarroel, Rodolfo
AU - Lemos, Robson
AU - Munoz, Roberto
N1 - Publisher Copyright:
© 2018 IEEE.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2018/10
Y1 - 2018/10
N2 - Multimodal Learning Analytics is a subfield of Learning Analytics that uses data coming from complex learning environments and collected through alternative devices that are different from those normally observed in the Learning Analytics literature. The present work uses data captured by Microsoft Kinect and organized with Lelikëlen system to find patterns in students oral presentations during a given discipline. For that, a total of 16 different features related to the records of 43 students presentations (85 observations) were used to generate clusters of students with similar behavior. Initial results indicate three main different profiles of students according to their patterns in oral presentations: active, passive, and semi-active. Such findings can be further implemented in Lelikëlen system in order to allow instant feedback to students. Future work will also evaluate how students oral presentations patterns evolve during the semester, and compare patterns of students presentations across areas to evaluate whether there are similarities or not.
AB - Multimodal Learning Analytics is a subfield of Learning Analytics that uses data coming from complex learning environments and collected through alternative devices that are different from those normally observed in the Learning Analytics literature. The present work uses data captured by Microsoft Kinect and organized with Lelikëlen system to find patterns in students oral presentations during a given discipline. For that, a total of 16 different features related to the records of 43 students presentations (85 observations) were used to generate clusters of students with similar behavior. Initial results indicate three main different profiles of students according to their patterns in oral presentations: active, passive, and semi-active. Such findings can be further implemented in Lelikëlen system in order to allow instant feedback to students. Future work will also evaluate how students oral presentations patterns evolve during the semester, and compare patterns of students presentations across areas to evaluate whether there are similarities or not.
KW - Clustering
KW - Data mining
KW - Multimodal learning analytics
KW - Students postures
UR - http://www.scopus.com/inward/record.url?scp=85071006325&partnerID=8YFLogxK
U2 - 10.1109/LACLO.2018.00054
DO - 10.1109/LACLO.2018.00054
M3 - Conference contribution
AN - SCOPUS:85071006325
T3 - Proceedings - 13th Latin American Conference on Learning Technologies, LACLO 2018
SP - 256
EP - 263
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 -