TY - JOUR
T1 - Where are You? Exploring Micro-Location in Indoor Learning Environments
AU - Riquelme, Fabian
AU - Noel, Rene
AU - Cornide-Reyes, Hector
AU - Geldes, Gustavo
AU - Cechinel, Cristian
AU - Miranda, Diego
AU - Villarroel, Rodolfo
AU - Munoz, Roberto
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2020
Y1 - 2020
N2 - Classroom teaching methodologies are gradually changing from masterclasses to active learning practices, and peer collaboration emerges as an essential skill to be developed. However, there are several challenges in evaluating collaborative activities more objectively, as well as to generate valuable information to teachers and appropriate feedback to students about their learning processes. In this context, multimodal learning analytics facilitate the evaluation of complex skills using data from multiple data sources. In this work, we propose the use of beacons to collect geolocation data from students who carry out collaborative tasks that involve movement and interactions through space. Furthermore, we suggest new ways to analyze, visualize, and interpret the data obtained. As a first practical approach, we carried out an exploratory, collaborative activity with sixteen undergraduate students working in a library, with bookshelves and work tables monitored by beacons. From the analysis of student movement dynamics, three types of well-differentiated student roles were identified: the collectors, those who go out to collect data from the bookshelves, ambassadors, those who communicate with other groups, and the secretaries, those who stay at their work desk to shape the requested essay. We believe these findings are valuable feedback for the enhancement of the learning activity and the first step towards MMLA-driven Teaching Process Improvement method.
AB - Classroom teaching methodologies are gradually changing from masterclasses to active learning practices, and peer collaboration emerges as an essential skill to be developed. However, there are several challenges in evaluating collaborative activities more objectively, as well as to generate valuable information to teachers and appropriate feedback to students about their learning processes. In this context, multimodal learning analytics facilitate the evaluation of complex skills using data from multiple data sources. In this work, we propose the use of beacons to collect geolocation data from students who carry out collaborative tasks that involve movement and interactions through space. Furthermore, we suggest new ways to analyze, visualize, and interpret the data obtained. As a first practical approach, we carried out an exploratory, collaborative activity with sixteen undergraduate students working in a library, with bookshelves and work tables monitored by beacons. From the analysis of student movement dynamics, three types of well-differentiated student roles were identified: the collectors, those who go out to collect data from the bookshelves, ambassadors, those who communicate with other groups, and the secretaries, those who stay at their work desk to shape the requested essay. We believe these findings are valuable feedback for the enhancement of the learning activity and the first step towards MMLA-driven Teaching Process Improvement method.
KW - Beacon
KW - collaborative learning
KW - geolocation device
KW - multimodal learning analytics
UR - http://www.scopus.com/inward/record.url?scp=85088708030&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2020.3008327
DO - 10.1109/ACCESS.2020.3008327
M3 - Article
AN - SCOPUS:85088708030
SN - 2169-3536
VL - 8
SP - 125776
EP - 125785
JO - IEEE Access
JF - IEEE Access
M1 - 9137270
ER -