计算机科学
过度拟合
人工智能
卷积神经网络
机器学习
深度学习
规范化(社会学)
模式识别(心理学)
人工神经网络
社会学
人类学
作者
Mayanda Mega Santoni,T. Basaruddin,Kasiyah Junus
出处
期刊:International Journal of Advanced Computer Science and Applications
[The Science and Information Organization]
日期:2023-01-01
卷期号:14 (3)
标识
DOI:10.14569/ijacsa.2023.0140371
摘要
The COVID-19 pandemic has significantly changed learning processes. Learning, which had generally been carried out face-to-face, has now turned online. This learning strategy has both advantages and challenges. On the bright side, online learning is unbound by space and time, allowing it to take place anywhere and anytime. On the other side, it faces a common challenge in the lack of direct interaction between educators and students, making it difficult to assess students’ engagement during an online learning process. Therefore, it is necessary to conduct research with the aim of automatically detecting students’ engagement during online learning. The data used in this research were derived from the DAiSEE dataset (Dataset for Affective States in E-Environments), which comprises ten-second video recordings of students. This dataset classifies engagement levels into four categories: low, very low, high, and very high. However, the issue of imbalanced data found in the DAiSEE dataset has yet to be addressed in previous research. This data imbalance can cause errors in the classification model, resulting in overfitting and underfitting of the model. In this study, Convolutional Neural Network, a deep learning model, was utilized for feature extraction on the DAiSEE dataset. The OpenFace library was used to perform facial landmark detection, head pose estimation, facial expression unit recognition, and eye gaze estimation. The pre-processing stages included data selection, dimensional reduction, and normalization. The PCA and SVD techniques were used for dimensional reduction. The data were later oversampled using the SMOTE algorithm. The training and testing data were distributed at an 80:20 ratio. The results obtained from this experiment exceeded the benchmark evaluation values on the DAiSEE dataset, achieving the best accuracy of 77.97% using the SVD dimensional reduction technique.
科研通智能强力驱动
Strongly Powered by AbleSci AI