TY - GEN
T1 - A systematic literature review of indicators for the understanding of interactions in virtual learning environments
AU - Gavilanes-Sagnay, Fredy
AU - Loza-Aguirre, Edison
AU - Riofrio-Luzcando, Diego
AU - Segura-Morales, Marco
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/12
Y1 - 2018/12
N2 - Virtual Learning Environments (VLEs) are becoming an innovative e-learning alternative. However, the implementation of these platforms suffers, in practice, of several issues among which the understanding of interactions inside the learning environment is an outstanding challenge for improving student experience. In this systematic literature review, we classify indicators found on academic contributions according to three categories of interactions to which they can contribute: Agent, Frequency and Participation Mode. Our results highlight that most of indicators focus on explicit actions. However, an alternative approach based on the understanding of perceptions and motivations could lead to propose indicators to solve problems for which there is no visible solution. The results presented in this study give a head start for implementing learning analytics solutions that would allow pedagogical managers to improve students' experience on VLEs.
AB - Virtual Learning Environments (VLEs) are becoming an innovative e-learning alternative. However, the implementation of these platforms suffers, in practice, of several issues among which the understanding of interactions inside the learning environment is an outstanding challenge for improving student experience. In this systematic literature review, we classify indicators found on academic contributions according to three categories of interactions to which they can contribute: Agent, Frequency and Participation Mode. Our results highlight that most of indicators focus on explicit actions. However, an alternative approach based on the understanding of perceptions and motivations could lead to propose indicators to solve problems for which there is no visible solution. The results presented in this study give a head start for implementing learning analytics solutions that would allow pedagogical managers to improve students' experience on VLEs.
KW - Data mining
KW - Educational platform
KW - Indicators
KW - Learning analytics
KW - Virtual environments
UR - http://www.scopus.com/inward/record.url?scp=85078545761&partnerID=8YFLogxK
U2 - 10.1109/CSCI46756.2018.00120
DO - 10.1109/CSCI46756.2018.00120
M3 - Contribución a la conferencia
AN - SCOPUS:85078545761
T3 - Proceedings - 2018 International Conference on Computational Science and Computational Intelligence, CSCI 2018
SP - 596
EP - 600
BT - Proceedings - 2018 International Conference on Computational Science and Computational Intelligence, CSCI 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 International Conference on Computational Science and Computational Intelligence, CSCI 2018
Y2 - 13 December 2018 through 15 December 2018
ER -