Journal ArticleDOI
Automated detection of cognitive engagement to inform the art of staying engaged in problem-solving
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TLDR
It is found that students’ facial behaviors were powerful predictors of their cognitive engagement states and the SVM (Support Vector Machine) model demonstrated excellent capacity for distinguishing engaged and less engaged states when 17 informative facial features were added into the model.Abstract:
In the present paper, we used supervised machine learning algorithms to predict students' cognitive engagement states from their facial behaviors as 61 students solved a clinical reasoning problem in an intelligent tutoring system. We also examined how high and low performers differed in cognitive engagement levels when performing surface and deep learning behaviors. We found that students' facial behaviors were powerful predictors of their cognitive engagement states. In particular, we found that the SVM (Support Vector Machine) model demonstrated excellent capacity for distinguishing engaged and less engaged states when 17 informative facial features were added into the model. In addition, the results suggested that high performers did not differ significantly in the general level of cognitive engagement with low performers. There was also no difference in cognitive engagement levels between high and low performers when they performed shallow learning behaviors. However, high performers showed a significantly higher level of cognitive engagement than low performers when conducting deep learning behaviors. This study advances our understanding of how students regulate their engagement to succeed in problem-solving. This study also has significant methodological implications for the automated measurement of cognitive engagement.read more
Citations
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Journal ArticleDOI
Cognitive engagement in self-regulated learning: an integrative model
Shan Li,Susanne P. Lajoie +1 more
TL;DR: In this article, the authors identify the nature of cognitive engagement (i.e., changing consecutively, context-dependent, comprising quantitative and qualitative dimensions, occurring consciously or unconsciously), based on which they compared the conceptual differences and similarities between cognitive engagement and self-regulated learning.
Journal ArticleDOI
Examining the relationship between emotion variability, self-regulated learning, and task performance in an intelligent tutoring system
TL;DR: In this article, emotion variability was examined among 21 medical students in the context of solving two patient cases of different complexity with BioWorld, a computer-based intelligent tutoring system.
Journal ArticleDOI
Predicting individual learning performance using machine‐learning hybridized with the teaching‐learning‐based optimization
Mehrdad Arashpour,Emadaldin Mohammadi Golafshani,Rajendran Parthiban,Julia Lamborn,Alireza Hosseinzadeh Kashani,Heng Li,Parisa Farzanehfar +6 more
TL;DR: In this article , two well-known machine learning techniques, that is, support vector machine (SVM) and artificial neural network (ANN), are hybridized by teaching-based optimizer (TLBO) to reliably predict the student exam performance (fail-pass classes and final exam scores).
Journal ArticleDOI
Automatic engagement estimation in smart education/learning settings: a systematic review of engagement definitions, datasets, and methods
TL;DR: A literature review of recent developments in automatic engagement estimation, including engagement definitions, datasets, and machine learning-based methods for automation estimation is presented in this paper , which aims at providing new researchers with insight on automatic engagement estimations to enhance smart learning with automatic engagement recognition methods.
Journal ArticleDOI
A multimodal facial cues based engagement detection system in e-learning context using deep learning approach
TL;DR: In this paper , an engagement detection system is proposed to ensure that the students get immediate feedback during e-learning, where the results from different modalities (for which the algorithms are proposed) are combined to determine the EI (engagement index).
References
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