ArticleInternational journal of environmental research and public health2020
Academic Emotion Classification and Recognition Method for Large-scale Online Learning Environment-Based on A-CNN and LSTM-ATT Deep Learning Pipeline Method.
Article in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Mapping the landscape: A bibliometric analysis of AI and teacher collaboration in educational research.F1000Research · 2025Pooled it
- New Trends in Emotion Recognition Using Image Analysis by Neural Networks, A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Prediction of college student psychological state based on deep learning framework combining the improved Whale Optimization Algorithm and LSTM.Scientific reports · 2026Article
- Micro Biomimetic Eyeball for Humanoid Robots: A Visual System with High-Density Functional Integration Based on an Origami Mechanism.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Fuzzy clustering algorithm for university students' psychological fitness and performance detection.Heliyon · 2023Article
- Emotion Recognition Using Different Sensors, Emotion Models, Methods and Datasets: A Comprehensive Review.Sensors (Basel, Switzerland) · 2023Review
- Detection and analysis of graduate students' academic emotions in the online academic forum based on text mining with a deep learning approach.Frontiers in psychology · 2023Article
- The positive energy of netizens: development and application of fine-grained sentiment lexicon and emotional intensity model.Current psychology (New Brunswick, N.J.) · 2022Article
- School adaptation and adolescent immigrant mental health: Mediation of positive academic emotions and conduct problems.Frontiers in public health · 2022Article
- Unpacking the Emotional Experiences of Learners in a Blended Learning Context.Frontiers in psychology · 2022Article
- English Education Tutoring Teaching System Based on MOOC.Computational intelligence and neuroscience · 2022Article
- Changing Positive Academic Emotions of Art Students Utilizing Computer Information Technology Based on the Perspective of Teaching.Applied bionics and biomechanics · 2022Article
- MRI-Based Radiomic Signature Identifying Secondary Loss of Response to Infliximab in Crohn's Disease.Frontiers in nutrition · 2021Article
- Differences in Driving Intention Transitions Caused by Driver's Emotion Evolutions.International journal of environmental research and public health · 2020Article
Corrections and comments
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Authors and funding
5 authors.
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Abstract
Subjective well-being is a comprehensive psychological indicator for measuring quality of life. Studies have found that emotional measurement methods and measurement accuracy are important for well-being-related research. Academic emotion is an emotion description in the field of education. The subjective well-being of learners in an online learning environment can be studied by analyzing academic emotions. However, in a large-scale online learning environment, it is extremely challenging to classify learners' academic emotions quickly and accurately for specific comment aspects. This study used literature analysis and data pre-analysis to build a dimensional classification system of academic emotion aspects for students' comments in an online learning environment, as well as to develop an aspect-oriented academic emotion automatic recognition method, including an aspect-oriented convolutional neural network (A-CNN) and an academic emotion classification algorithm based on the long short-term memory with attention mechanism (LSTM-ATT) and the attention mechanism. The experiments showed that this model can provide quick and effective identification. The A-CNN model accuracy on the test set was 89%, and the LSTM-ATT model accuracy on the test set was 71%. This research provides a new method for the measurement of large-scale online academic emotions, as well as support for research related to students' well-being in online learning environments.
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