The Syncretic Effect of Dual-Source Data on Affective Computing in Online Learning Contexts: A Perspective From Convolutional Neural Network With Attention Mechanism
被引:2
|
作者:
Zhai, Xuesong
论文数: 0引用数: 0
h-index: 0
机构:
Zhejiang Univ, Coll Educ, Hangzhou, Zhejiang, Peoples R China
Anhui Xinhua Univ, Hefei, Anhui, Peoples R China
Anhui Jianzhu Univ, Hefei, Anhui, Peoples R ChinaZhejiang Univ, Coll Educ, Hangzhou, Zhejiang, Peoples R China
Zhai, Xuesong
[1
,2
,3
]
Xu, Jiaqi
论文数: 0引用数: 0
h-index: 0
机构:
Zhejiang Univ, Coll Educ, Hangzhou, Zhejiang, Peoples R ChinaZhejiang Univ, Coll Educ, Hangzhou, Zhejiang, Peoples R China
Xu, Jiaqi
[1
]
Chen, Nian-Shing
论文数: 0引用数: 0
h-index: 0
机构:
Natl Taiwan Normal Univ, Program Learning Sci, Yunlin, TaiwanZhejiang Univ, Coll Educ, Hangzhou, Zhejiang, Peoples R China
Chen, Nian-Shing
[4
]
Shen, Jun
论文数: 0引用数: 0
h-index: 0
机构:
Univ Wollongong, Sch Comp & Informat Technol, Wollongong, NSW, AustraliaZhejiang Univ, Coll Educ, Hangzhou, Zhejiang, Peoples R China
Shen, Jun
[5
]
Li, Yan
论文数: 0引用数: 0
h-index: 0
机构:
Zhejiang Univ, Coll Educ, Hangzhou, Zhejiang, Peoples R ChinaZhejiang Univ, Coll Educ, Hangzhou, Zhejiang, Peoples R China
Li, Yan
[1
]
Wang, Yonggu
论文数: 0引用数: 0
h-index: 0
机构:
Zhejiang Univ Technol, Coll Educ Sci & Technol, Hangzhou, Zhejiang, Peoples R ChinaZhejiang Univ, Coll Educ, Hangzhou, Zhejiang, Peoples R China
Wang, Yonggu
[6
]
论文数: 引用数:
h-index:
机构:
Chu, Xiaoyan
[1
]
Zhu, Yumeng
论文数: 0引用数: 0
h-index: 0
机构:
Zhejiang Univ, Coll Educ, Hangzhou, Zhejiang, Peoples R ChinaZhejiang Univ, Coll Educ, Hangzhou, Zhejiang, Peoples R China
Zhu, Yumeng
[1
]
机构:
[1] Zhejiang Univ, Coll Educ, Hangzhou, Zhejiang, Peoples R China
[2] Anhui Xinhua Univ, Hefei, Anhui, Peoples R China
[3] Anhui Jianzhu Univ, Hefei, Anhui, Peoples R China
[4] Natl Taiwan Normal Univ, Program Learning Sci, Yunlin, Taiwan
Affective computing (AC) has been regarded as a relevant approach to identifying online learners' mental states and predicting their learning performance. Previous research mainly used one single-source data set, typically learners' facial expression, to compute learners' affection. However, a single facial expression may represent different affections in various head poses. This study proposed a dual-source data approach to solve the problem. Facial expression and head pose are two typical data sources that can be captured from online learning videos. The current study collected a dual-source data set of facial expressions and head poses from an online learning class in a middle school. A deep learning neural network using AlexNet with an attention mechanism was developed to verify the syncretic effect on affective computing of the proposed dual-source fusion strategy. The results show that the dual-source fusion approach significantly outperforms the single-source approach based on the AC recognition accuracy between the two approaches (dual-source approach using Attention-AlexNet model 80.96%; single-source approach, facial expression 76.65% and head pose 64.34%). This study contributes to the theoretical construction of the dual-source data fusion approach, and the empirical validation of the effect of the Attention-AlexNet neural network approach on affective computing in online learning contexts.