Engagement in learning is crucial in maintaining student eagerness as fuel for student learning to achieve learning success. In e-Learning settings, engagement also plays an im-portant role as a factor influencing stu...
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Engagement in learning is crucial in maintaining student eagerness as fuel for student learning to achieve learning success. In e-Learning settings, engagement also plays an im-portant role as a factor influencing stu...
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ISBN:
(数字)9798350379914
ISBN:
(纸本)9798350379921
Engagement in learning is crucial in maintaining student eagerness as fuel for student learning to achieve learning success. In e-Learning settings, engagement also plays an im-portant role as a factor influencing student learning success as students are regulating themselves during learning one-on-one using computer devices. Failing to detect student engagement may result in unsuccessful learning. In extreme cases, students may disengage and learning purpose cannot be achieved. This research aims to detect user engagement in e-Learning settings by using a deep learning approach with a feature extraction method. The dataset used in this study is the DAiSEE dataset. The dataset is in the form of videos and is extracted into frames and specific facial features. The facial area was extracted using the Mediapipe Library. The features include 1434 3D landmark features, 52 action unit features, one depth estimation feature, and three head pose estimation features. This research also conducts image selection, which selecting frames with an interval of 15 in the train, validation, and test folders. The multi-stacked Convolutional Neural Network model was used to classify four levels of engagements. The results were compared to the existing approach in engagement detection. The untuned proposed model achieved an accuracy of 48.95%, while the tuned proposed model achieved an accuracy of 52.94%. The proposed model is able to compete with state-of-the-art methods using a less complex model. Even so, the model still cannot learn labels perfectly due to a very imbalanced dataset and a possibility of model overfitting. This research is expected to be useful for educational institutions and the e-Learning field in the future.
Background:There is a growing interest to understand the neurobiological mechanisms that drive the positive associations of physical activity and fitness with measures of cognitive *** better understand those mechanis...
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Background:There is a growing interest to understand the neurobiological mechanisms that drive the positive associations of physical activity and fitness with measures of cognitive *** better understand those mechanisms,several studies have employed eye-based measures(e.g., eye movement measures such as saccades,pupillary measures such as pupil dilation,and vascular measures such as retinal vessel diameter)deemed to be proxies for specific neurobiological ***,there is currently no systematic review providing a comprehensive overview of these studies in the field of exercise-cognition ***,this review aimed to address that gap in the ***:To identify eligible studies,we searched 5 electronic databases on October 23,*** researchers independently extracted data and assessed the risk of bias using a modified version of the Tool for the assEssment of Study qualiTy and reporting in EXercise(TESTEX scale,for interventional studies) and the critical appraisal tool from the Joanna Briggs Institute(for cross-sectional studies).Results:Our systematic review(n=35 studies) offers the following main findings:(a) there is insufficient evidence available to draw solid conclusions concerning gaze-fixation-based measures;(b) the evidence that pupillometric measures,which are a proxy for the noradrenergic system,can explain the positive effect of acute exercise and cardiorespiratory fitness on cognitive performance is mixed;(c) physical training-or fitness-related changes of the cerebrovascular system(operationalized via changes in retinal vasculature) are,in general,positively associated with cognitive performance improvements;(d) acute and chronic physical exercises show a positive effect based on an oculomotor-based measure function(operationalized via antisaccade tasks);and(e) the positive association between cardiorespiratory fitness and cognitive performance is partly mediated by the dopaminergic system(operationalized via spo
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