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Presented the 25th May 2019 at the conference Artificial Intelligence and Adaptive Education (AIAED'19) Beijing, China.
Abstract: We introduce the Multimodal Learning Analytics Pipeline, a generic approach for collecting and exploiting multimodal data to support learning activities across physical and digital spaces. The MMLA Pipeline facilitates researchers in setting up their multimodal experiments, reducing setup and configuration time required for collecting meaningful datasets. Using the MMLA Pipeline, researchers can decide to use a set of custom sensors to track different modalities, including behavioural cues or affective states. Hence, researchers can quickly obtain multimodal sessions consisting of synchronised sensor data and video recordings. They can analyse and annotate the sessions recorded and train machine learning algorithms to classify or predict the patterns investigated.