%0 Conference Paper %1 10.1007/978-3-319-98572-5_4 %A Schneider, Jan %A Di Mitri, Daniele %A Limbu, Bibeg %A Drachsler, Hendrik %B Lifelong Technology-Enhanced Learning %C Cham %D 2018 %E Pammer-Schindler, Viktoria %E Pérez-Sanagustín, Mar %E Drachsler, Henrik %E Elferink, Raymond %E Scheffel, Maren %I Springer International Publishing %K learninganalytics multimodality sensor-basedlearning ubiquitous %P 45--58 %T Multimodal Learning Hub: A Tool for Capturing Customizable Multimodal Learning Experiences %X Studies in Learning Analytics provide concrete examples of how the analysis of direct interactions with learning management systems can be used to optimize and understand the learning process. Learning, however, does not necessarily only occur when the learner is directly interacting with such systems. With the use of sensors, it is possible to collect data from learners and their environment ubiquitously, therefore expanding the use cases of Learning Analytics. For this reason, we developed the Multimodal Learning Hub (MLH), a system designed to enhance learning in ubiquitous learning scenarios, by collecting and integrating multimodal data from customizable configurations of ubiquitous data providers. In this paper, we describe the MLH and report on the results of tests where we explored its reliability to integrate multimodal data. %@ 978-3-319-98572-5

@inproceedings{10.1007/978-3-319-98572-5_4, abstract = {Studies in Learning Analytics provide concrete examples of how the analysis of direct interactions with learning management systems can be used to optimize and understand the learning process. Learning, however, does not necessarily only occur when the learner is directly interacting with such systems. With the use of sensors, it is possible to collect data from learners and their environment ubiquitously, therefore expanding the use cases of Learning Analytics. For this reason, we developed the Multimodal Learning Hub (MLH), a system designed to enhance learning in ubiquitous learning scenarios, by collecting and integrating multimodal data from customizable configurations of ubiquitous data providers. In this paper, we describe the MLH and report on the results of tests where we explored its reliability to integrate multimodal data.}, added-at = {2018-09-02T22:54:57.000+0200}, address = {Cham}, author = {Schneider, Jan and Di Mitri, Daniele and Limbu, Bibeg and Drachsler, Hendrik}, biburl = {https://www.bibsonomy.org/bibtex/231e257fa5b1748e99a66b319cbb4e2b1/ereidt}, booktitle = {Lifelong Technology-Enhanced Learning}, editor = {Pammer-Schindler, Viktoria and P{\'e}rez-Sanagust{\'i}n, Mar and Drachsler, Henrik and Elferink, Raymond and Scheffel, Maren}, interhash = {36aeb456c5ed048208cb6256883073c5}, intrahash = {31e257fa5b1748e99a66b319cbb4e2b1}, isbn = {978-3-319-98572-5}, keywords = {learninganalytics multimodality sensor-basedlearning ubiquitous}, pages = {45--58}, publisher = {Springer International Publishing}, timestamp = {2018-09-02T22:54:57.000+0200}, title = {Multimodal Learning Hub: A Tool for Capturing Customizable Multimodal Learning Experiences}, year = 2018 }