A data value chain to support the processing of multimodal evidence in authentic learning scenarios

Shashi Kant Shankar, Adolfo Ruiz-Calleja, Sergio Serrano-Iglesias, Alejandro Ortega-Arranz, Paraskevi Topali, Alejandra Martínez-Monés
2019 Learning Analytics Summer Institute Spain  
Multimodal Learning Analytics (MMLA) uncovers the possibility to get a more holistic picture of a learning situation than traditional Learning Analytics, by triangulating learning evidence collected from multiple modalities. However, current MMLA solutions are complex and typically tailored to specific learning situations. In order to overcome this problem we are working towards an infrastructure that supports MMLA and can be adapted to different learning situations. As a first step in this
more » ... ction, this paper analyzes four MMLA scenarios, abstracts their data processing activities and extracts a Data Value Chain to model the processing of multimodal evidence of learning. This helps us to reflect on the requirements needed for an infrastructure to support MMLA.
dblp:conf/lasi-spain/ShankarRSOTM19 fatcat:iyj2llucjfd3jog2nplmrel56e