TY - GEN
T1 - Enhanced affective factors management for HEI students dropout prevention
AU - Restrepo, Emmanuelle Gutiérrez Y
AU - Ferreira, Fernando
AU - Boticario, Jesús G.
AU - Marcelino-Jesus, Elsa
AU - Sarraipa, João
AU - Jardim-Goncalves, Ricardo
PY - 2016
Y1 - 2016
N2 - Among the problems affecting Higher Education Institutions (HEI) in Latin America and the Caribbean there is the dropout, which relates to a more general issue consisting in dealing with the diversity of students. Here provided solutions are to detect and deal with student’s particular capacities and needs. To cope with this situation the ACACIA project has defined a framework that develops both CADEP centers and technological infrastructure. The former consists of an organizational unit focus on Empowering, Innovating, Educating, Supporting, Monitoring and leveraging institutions in dealing with such diversity. The latter is based on building the required infrastructure to tackle those issues and covering both face-to-face and eLearning educational settings. This comprises non-intrusive affect detection methods along with ambient intelligent solutions, which provide context-aware affective feedback to each student. Preliminary experimentation results open interesting avenues to be further progressed thus taking advantage of current developments on affect computing technologies.
AB - Among the problems affecting Higher Education Institutions (HEI) in Latin America and the Caribbean there is the dropout, which relates to a more general issue consisting in dealing with the diversity of students. Here provided solutions are to detect and deal with student’s particular capacities and needs. To cope with this situation the ACACIA project has defined a framework that develops both CADEP centers and technological infrastructure. The former consists of an organizational unit focus on Empowering, Innovating, Educating, Supporting, Monitoring and leveraging institutions in dealing with such diversity. The latter is based on building the required infrastructure to tackle those issues and covering both face-to-face and eLearning educational settings. This comprises non-intrusive affect detection methods along with ambient intelligent solutions, which provide context-aware affective feedback to each student. Preliminary experimentation results open interesting avenues to be further progressed thus taking advantage of current developments on affect computing technologies.
KW - Emerging technologies for collaboration and learning
KW - Recommender systems for technology-enhanced learning
UR - http://www.scopus.com/inward/record.url?scp=84978790815&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-39483-1_61
DO - 10.1007/978-3-319-39483-1_61
M3 - Conference contribution
AN - SCOPUS:84978790815
SN - 978-3-319-39482-4
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 675
EP - 684
BT - Learning and Collaboration Technologies
A2 - Zaphiris, P.
A2 - Ioannou, A.
PB - Springer Verlag
T2 - 3rd International Conference on Learning and Collaboration Technologies, LCT 2016 and 18th International Conference on Human-Computer Interaction, HCI International 2016
Y2 - 17 July 2016 through 22 July 2016
ER -