TY - GEN
T1 - Improving the use of virtual worlds in education through learning analytics
T2 - Future Technologies Conference, FTC 2018
AU - Gavilanes-Sagnay, Fredy
AU - Loza-Aguirre, Edison
AU - Riofrío-Luzcando, Diego
AU - Segura-Morales, Marco
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - The use of Virtual Worlds in Education is becoming an innovative alternative to traditional education. However, these solutions are confronted to several issues such as: lack of indicators to follow up the students’ progress, lack of well-defined evaluation parameters, difficulties for evaluating collective and individual contributions, difficulties for keeping students engaged and motivated, a very time-consuming teachers’ supervision, and the absence of tutors for guiding the learning process, among others. In this review, we explore and describe academic contributions focused on the application of Learning Analytics to improve Virtual Worlds in Education from three perspectives: Personalized Learning, Adaptive Learning and Educational Intervention. Our results highlight that most of the research focus on support decisions whose nature concerns operational non-real-time issues. Additionally, almost all the contributions focus in solving only a few issues, but none of them offer a holistic framework that could be used by teachers or pedagogical personnel for decision making.
AB - The use of Virtual Worlds in Education is becoming an innovative alternative to traditional education. However, these solutions are confronted to several issues such as: lack of indicators to follow up the students’ progress, lack of well-defined evaluation parameters, difficulties for evaluating collective and individual contributions, difficulties for keeping students engaged and motivated, a very time-consuming teachers’ supervision, and the absence of tutors for guiding the learning process, among others. In this review, we explore and describe academic contributions focused on the application of Learning Analytics to improve Virtual Worlds in Education from three perspectives: Personalized Learning, Adaptive Learning and Educational Intervention. Our results highlight that most of the research focus on support decisions whose nature concerns operational non-real-time issues. Additionally, almost all the contributions focus in solving only a few issues, but none of them offer a holistic framework that could be used by teachers or pedagogical personnel for decision making.
KW - Data mining
KW - Educational platform
KW - Learning analytics
KW - Virtual environments
KW - Virtual worlds
UR - https://www.scopus.com/pages/publications/85055915166
U2 - 10.1007/978-3-030-02686-8_83
DO - 10.1007/978-3-030-02686-8_83
M3 - Contribución a la conferencia
AN - SCOPUS:85055915166
SN - 9783030026851
T3 - Advances in Intelligent Systems and Computing
SP - 1123
EP - 1132
BT - Proceedings of the Future Technologies Conference (FTC) 2018 - Volume 1
A2 - Bhatia, Rahul
A2 - Arai, Kohei
A2 - Kapoor, Supriya
PB - Springer Verlag
Y2 - 15 November 2018 through 16 November 2018
ER -