Publication Type : Journal Article
Source : Journal of Universal Computer Science, 2023
Url : https://drive.google.com/file/d/1v5yrmeKLE4q29ADEmWWiXHMLQ4xgSrTQ/view
Campus : Amritapuri
Year : 2023
Abstract : Multimodal Learning Analytics (MMLA) solutions aim to provide a more holistic picture of a learning situation by processing multimodal educational data. Considering contextual information of a learning situation is known to help in providing more relevant outputs to educational stakeholders. However, most of the MMLA solutions are still in prototyping phase and dealing with different dimensions of an authentic MMLA situation that involve multiple cross-disciplinary stakeholders like teachers, researchers, and developers
Cite this Research Publication : Shankar, S.K., Ruiz-Calleja, A., Prieto, L.P., Rodríguez-Triana, M.J., Chejara, P., & Tripathi, S. (2023), CIMLA: A Modular and Modifiable Data Preparation, Organization, and Fusion Infrastructure to Partially Support the Development of Context-aware MMLA Solutions. JUCS - Journal of Universal Computer Science 29(3): 265-297. https://doi.org/10.3897/jucs.84558