ArticleFrontiers in psychology2021
Affective Cognition of Students' Autonomous Learning in College English Teaching Based on Deep Learning.
Article in Frontiers in psychology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- An algorithm for optimizing psychological regulation strategies for college students based on image recognition and reinforcement learning.Scientific reports · 2026Article
- University english teaching evaluation using artificial intelligence and data mining technology.Scientific reports · 2025Article
- An empirical study on the teaching mode of cultural translation in college English based on the Production Oriented Approach (POA).PloS one · 2025Article
- Article
- The Effects of General Listening Anxiety and Listening Test Anxiety on Self-Perceived Listening Performance Among Chinese English Learners.Psychology research and behavior management · 2023Article
- Article
- COVID-19 online teaching intervention and learning performance of college foreign language students.Frontiers in psychology · 2022Article
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Abstract
Emotions can influence and regulate learners' attention, memory, thinking, and other cognitive activities. The similarities and differences between English and non-English majors in terms of English classroom learning engagement were compared, and the significant factors affecting the emotional, cognitive, and behavioral engagement of the two groups of students in the English classroom were different. English majors' affective engagement in the classroom was not significant, which was largely related to their time and frequency of English learning. Traditional methods of learner emotion recognition suffer from low recognition rate, complex algorithms, poor robustness, and easy to lose key information about facial expression features. The paper proposes a convolutional neural network-based learner emotion recognition method, which includes three convolutional layers, three pooling layers, and one fully connected layer. In the future, the method will can be applied to the construction of smart learning environments, providing technical support for improving learner models, realizing emotional interactions, and mining learning behaviors, etc.
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