IoT and Wearables smart devices, eye-trackers and other camera systems, and self‐programmable microcomputers such as Raspberry Pi and Arduino are becoming accessible to the general public. These types of devices create new data sources, which can be used to investigate learning. To drive this investigation forward, Prof. Dr. Hendrik Drachsler and Dr. Jan Schneider from the Educational Technologies group at the DIPF compiled a Special Issue volume on Multimodal Learning Analytics (MMLA) for the Journal of Computer Assisted Learning. This is the first special issue on the topic of MMLA that has ever been published in a scientific journal. It consists of seven scientific publications, which cover topics such as:
- The definition of a conceptual model for mapping Multimodal Data and learning.
- The use of gestures for mathematical tutoring systems.
- Early identification of students at risk with the use of Multimodal Data.
- The use of Multimodal Data to predict team success in open-ended tasks.
- The analysis of students’ brain activity while performing a “wordlist” task.
- The use of Multimodal Tools for teacher training.
- Exploring the relationship between students’ arousal level during lectures and their corresponding grades.
This Special Issue shows the diversity of this emerging field of research and aims to bring those new to MMLA up to speed.