108 research outputs found
MUTLA: A Large-Scale Dataset for Multimodal Teaching and Learning Analytics
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.Comment: 3 pages, 1 figure, 2 tables workshop pape
Retrieve-then-extract Based Knowledge Graph Querying Using Graph Neural Networks
The abstract of Retrieve-then-extract Based Knowledge Graph Querying Using
Graph Neural Networks will be updated here
Student Modeling and Analysis in Adaptive Instructional Systems
There is a growing interest in developing and implementing adaptive instructional systems to improve, automate, and personalize student education. A necessary part of any such adaptive instructional system is a student model used to predict or analyze learner behavior and inform adaptation. To help inform researchers in this area, this paper presents a state-of-the-art review of 11 years of research (2010-2021) in student modeling, focusing on learner characteristics, learning indicators, and foundational aspects of dissimilar models. We mainly emphasize increased prediction accuracy when using multidimensional learner data to create multimodal models in real-world adaptive instructional systems. In addition, we discuss challenges inherent in real-world multimodal modeling, such as uncontrolled data collection environments leading to noisy data and data sync issues. Finally, we reinforce our findings and conclusions through an industry case study of an adaptive instructional system. In our study, we verify that adding multiple data modalities increases our model prediction accuracy from 53.3% to 69%. At the same time, the challenges encountered with our real-world case study, including uncontrolled data collection environment with inevitably noisy data, calls for synchronization and noise control strategies for data quality and usability
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