  Automatic analysis of teacher and student interactions could be very
important to improve the quality of teaching and student engagement. However,
despite some recent progress in utilizing multimodal data for teaching and
learning analytics, a thorough analysis of a rich multimodal dataset coming for
a complex real learning environment has yet to be done. To bridge this gap, we
present a large-scale MUlti-modal Teaching and Learning Analytics (MUTLA)
dataset. This dataset includes time-synchronized multimodal data records of
students (learning logs, videos, EEG brainwaves) as they work in various
subjects from Squirrel AI Learning System (SAIL) to solve problems of varying
difficulty levels. The dataset resources include user records from the learner
records store of SAIL, brainwave data collected by EEG headset devices, and
video data captured by web cameras while students worked in the SAIL products.
Our hope is that by analyzing real-world student learning activities, facial
expressions, and brainwave patterns, researchers can better predict engagement,
which can then be used to improve adaptive learning selection and student
learning outcomes. An additional goal is to provide a dataset gathered from
real-world educational activities versus those from controlled lab environments
to benefit the educational learning community.
